diff --git a/latentsync/data/syncnet_dataset.py b/latentsync/data/syncnet_dataset.py index 4cc3f67..d32bb54 100644 --- a/latentsync/data/syncnet_dataset.py +++ b/latentsync/data/syncnet_dataset.py @@ -1,153 +1,142 @@ -# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import numpy as np -from torch.utils.data import Dataset -import torch -import random -from ..utils.util import gather_video_paths_recursively -from ..utils.image_processor import ImageProcessor -from ..utils.audio import melspectrogram -import math - -from decord import AudioReader, VideoReader, cpu - - -class SyncNetDataset(Dataset): - def __init__(self, data_dir: str, fileslist: str, config): - if fileslist != "": - with open(fileslist) as file: - self.video_paths = [line.rstrip() for line in file] - elif data_dir != "": - self.video_paths = gather_video_paths_recursively(data_dir) - else: - raise ValueError("data_dir and fileslist cannot be both empty") - - self.resolution = config.data.resolution - self.num_frames = config.data.num_frames - - self.mel_window_length = math.ceil(self.num_frames / 5 * 16) - - self.audio_sample_rate = config.data.audio_sample_rate - self.video_fps = config.data.video_fps - self.audio_samples_length = int( - config.data.audio_sample_rate // config.data.video_fps * config.data.num_frames - ) - self.image_processor = ImageProcessor(resolution=config.data.resolution, mask="half") - self.audio_cache_dir = config.data.audio_cache_dir - os.makedirs(self.audio_cache_dir, exist_ok=True) - - def __len__(self): - return len(self.video_paths) - - def read_audio(self, video_path: str): - ar = AudioReader(video_path, ctx=cpu(self.worker_id), sample_rate=self.audio_sample_rate) - original_mel = melspectrogram(ar[:].asnumpy().squeeze(0)) - return torch.from_numpy(original_mel) - - def crop_audio_window(self, original_mel, start_index): - start_idx = int(80.0 * (start_index / float(self.video_fps))) - end_idx = start_idx + self.mel_window_length - return original_mel[:, start_idx:end_idx].unsqueeze(0) - - def get_frames(self, video_reader: VideoReader): - total_num_frames = len(video_reader) - - start_idx = random.randint(0, total_num_frames - self.num_frames) - frames_index = np.arange(start_idx, start_idx + self.num_frames, dtype=int) - - while True: - wrong_start_idx = random.randint(0, total_num_frames - self.num_frames) - # wrong_start_idx = random.randint( - # max(0, start_idx - 25), min(total_num_frames - self.num_frames, start_idx + 25) - # ) - if wrong_start_idx == start_idx: - continue - # if wrong_start_idx >= start_idx - self.num_frames and wrong_start_idx <= start_idx + self.num_frames: - # continue - wrong_frames_index = np.arange(wrong_start_idx, wrong_start_idx + self.num_frames, dtype=int) - break - - frames = video_reader.get_batch(frames_index).asnumpy() - wrong_frames = video_reader.get_batch(wrong_frames_index).asnumpy() - - return frames, wrong_frames, start_idx - - def worker_init_fn(self, worker_id): - # Initialize the face mesh object in each worker process, - # because the face mesh object cannot be called in subprocesses - self.worker_id = worker_id - # setattr(self, f"image_processor_{worker_id}", ImageProcessor(self.resolution, self.mask)) - - def __getitem__(self, idx): - # image_processor = getattr(self, f"image_processor_{self.worker_id}") - while True: - try: - idx = random.randint(0, len(self) - 1) - - # Get video file path - video_path = self.video_paths[idx] - - vr = VideoReader(video_path, ctx=cpu(self.worker_id)) - - if len(vr) < 2 * self.num_frames: - continue - - frames, wrong_frames, start_idx = self.get_frames(vr) - - mel_cache_path = os.path.join( - self.audio_cache_dir, os.path.basename(video_path).replace(".mp4", "_mel.pt") - ) - - if os.path.isfile(mel_cache_path): - try: - original_mel = torch.load(mel_cache_path) - except Exception as e: - print(f"{type(e).__name__} - {e} - {mel_cache_path}") - os.remove(mel_cache_path) - original_mel = self.read_audio(video_path) - torch.save(original_mel, mel_cache_path) - else: - original_mel = self.read_audio(video_path) - torch.save(original_mel, mel_cache_path) - - mel = self.crop_audio_window(original_mel, start_idx) - - if mel.shape[-1] != self.mel_window_length: - continue - - if random.choice([True, False]): - y = torch.ones(1).float() - chosen_frames = frames - else: - y = torch.zeros(1).float() - chosen_frames = wrong_frames - - chosen_frames = self.image_processor.process_images(chosen_frames) - # chosen_frames, _, _ = image_processor.prepare_masks_and_masked_images( - # chosen_frames, affine_transform=True - # ) - - vr.seek(0) # avoid memory leak - break - - except Exception as e: # Handle the exception of face not detcted - print(f"{type(e).__name__} - {e} - {video_path}") - if "vr" in locals(): - vr.seek(0) # avoid memory leak - - sample = dict(frames=chosen_frames, audio_samples=mel, y=y) - - return sample +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +from torch.utils.data import Dataset +import torch +import random +from ..utils.util import gather_video_paths_recursively +from ..utils.image_processor import ImageProcessor +from ..utils.audio import melspectrogram +import math + +from decord import AudioReader, VideoReader, cpu + + +class SyncNetDataset(Dataset): + def __init__(self, data_dir: str, fileslist: str, config): + if fileslist != "": + with open(fileslist) as file: + self.video_paths = [line.rstrip() for line in file] + elif data_dir != "": + self.video_paths = gather_video_paths_recursively(data_dir) + else: + raise ValueError("data_dir and fileslist cannot be both empty") + + self.resolution = config.data.resolution + self.num_frames = config.data.num_frames + + self.mel_window_length = math.ceil(self.num_frames / 5 * 16) + + self.audio_sample_rate = config.data.audio_sample_rate + self.video_fps = config.data.video_fps + self.image_processor = ImageProcessor(resolution=config.data.resolution, mask="half") + self.audio_mel_cache_dir = config.data.audio_mel_cache_dir + os.makedirs(self.audio_mel_cache_dir, exist_ok=True) + + def __len__(self): + return len(self.video_paths) + + def read_audio(self, video_path: str): + ar = AudioReader(video_path, ctx=cpu(self.worker_id), sample_rate=self.audio_sample_rate) + original_mel = melspectrogram(ar[:].asnumpy().squeeze(0)) + return torch.from_numpy(original_mel) + + def crop_audio_window(self, original_mel, start_index): + start_idx = int(80.0 * (start_index / float(self.video_fps))) + end_idx = start_idx + self.mel_window_length + return original_mel[:, start_idx:end_idx].unsqueeze(0) + + def get_frames(self, video_reader: VideoReader): + total_num_frames = len(video_reader) + + start_idx = random.randint(0, total_num_frames - self.num_frames) + frames_index = np.arange(start_idx, start_idx + self.num_frames, dtype=int) + + while True: + wrong_start_idx = random.randint(0, total_num_frames - self.num_frames) + if wrong_start_idx == start_idx: + continue + wrong_frames_index = np.arange(wrong_start_idx, wrong_start_idx + self.num_frames, dtype=int) + break + + frames = video_reader.get_batch(frames_index).asnumpy() + wrong_frames = video_reader.get_batch(wrong_frames_index).asnumpy() + + return frames, wrong_frames, start_idx + + def worker_init_fn(self, worker_id): + # Initialize the face mesh object in each worker process, + # because the face mesh object cannot be called in subprocesses + self.worker_id = worker_id + # setattr(self, f"image_processor_{worker_id}", ImageProcessor(self.resolution, self.mask)) + + def __getitem__(self, idx): + # image_processor = getattr(self, f"image_processor_{self.worker_id}") + while True: + try: + idx = random.randint(0, len(self) - 1) + + # Get video file path + video_path = self.video_paths[idx] + + vr = VideoReader(video_path, ctx=cpu(self.worker_id)) + + if len(vr) < 2 * self.num_frames: + continue + + frames, wrong_frames, start_idx = self.get_frames(vr) + + mel_cache_path = os.path.join( + self.audio_mel_cache_dir, os.path.basename(video_path).replace(".mp4", "_mel.pt") + ) + + if os.path.isfile(mel_cache_path): + try: + original_mel = torch.load(mel_cache_path, weights_only=True) + except Exception as e: + print(f"{type(e).__name__} - {e} - {mel_cache_path}") + os.remove(mel_cache_path) + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + else: + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + + mel = self.crop_audio_window(original_mel, start_idx) + + if mel.shape[-1] != self.mel_window_length: + continue + + if random.choice([True, False]): + y = torch.ones(1).float() + chosen_frames = frames + else: + y = torch.zeros(1).float() + chosen_frames = wrong_frames + + chosen_frames = self.image_processor.process_images(chosen_frames) + + vr.seek(0) # avoid memory leak + break + + except Exception as e: # Handle the exception of face not detcted + print(f"{type(e).__name__} - {e} - {video_path}") + if "vr" in locals(): + vr.seek(0) # avoid memory leak + + sample = dict(frames=chosen_frames, audio_samples=mel, y=y) + + return sample diff --git a/latentsync/data/unet_dataset.py b/latentsync/data/unet_dataset.py index 156d1b5..ba2c309 100644 --- a/latentsync/data/unet_dataset.py +++ b/latentsync/data/unet_dataset.py @@ -1,186 +1,158 @@ -# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import numpy as np -from torch.utils.data import Dataset -import torch -import random -import cv2 -from ..utils.image_processor import ImageProcessor, load_fixed_mask -from ..utils.audio import melspectrogram -from decord import AudioReader, VideoReader, cpu - - -class UNetDataset(Dataset): - def __init__(self, train_data_dir: str, config): - if config.data.train_fileslist != "": - with open(config.data.train_fileslist) as file: - self.video_paths = [line.rstrip() for line in file] - elif train_data_dir != "": - self.video_paths = [] - for file in os.listdir(train_data_dir): - if file.endswith(".mp4"): - self.video_paths.append(os.path.join(train_data_dir, file)) - else: - raise ValueError("data_dir and fileslist cannot be both empty") - - self.resolution = config.data.resolution - self.num_frames = config.data.num_frames - - if self.num_frames == 16: - self.mel_window_length = 52 - elif self.num_frames == 5: - self.mel_window_length = 16 - else: - raise NotImplementedError("Only support 16 and 5 frames now") - - self.audio_sample_rate = config.data.audio_sample_rate - self.video_fps = config.data.video_fps - self.mask = config.data.mask - self.mask_image = load_fixed_mask(self.resolution) - self.load_audio_data = config.model.add_audio_layer and config.run.use_syncnet - self.audio_cache_dir = config.data.audio_cache_dir - os.makedirs(self.audio_cache_dir, exist_ok=True) - - def __len__(self): - return len(self.video_paths) - - def read_audio(self, video_path: str): - ar = AudioReader(video_path, ctx=cpu(self.worker_id), sample_rate=self.audio_sample_rate) - original_mel = melspectrogram(ar[:].asnumpy().squeeze(0)) - return torch.from_numpy(original_mel) - - def crop_audio_window(self, original_mel, start_index): - start_idx = int(80.0 * (start_index / float(self.video_fps))) - end_idx = start_idx + self.mel_window_length - return original_mel[:, start_idx:end_idx].unsqueeze(0) - - def crop_overlap_audio_window(self, original_mel, start_index): - half_num_frames = self.num_frames // 2 - if start_index - half_num_frames < 0: - return None - mels = [] - for i in range(start_index, start_index + self.num_frames): - mel = self.crop_audio_window(original_mel, i - half_num_frames) - if mel.shape[-1] != self.mel_window_length: - return None - mels.append(mel) - mel_overlap = torch.stack(mels) - return mel_overlap - - def get_frames(self, video_reader: VideoReader): - total_num_frames = len(video_reader) - - start_idx = random.randint(self.num_frames // 2, total_num_frames - self.num_frames - self.num_frames // 2) - frames_index = np.arange(start_idx, start_idx + self.num_frames, dtype=int) - - while True: - wrong_start_idx = random.randint(0, total_num_frames - self.num_frames) - if wrong_start_idx > start_idx - self.num_frames and wrong_start_idx < start_idx + self.num_frames: - continue - wrong_frames_index = np.arange(wrong_start_idx, wrong_start_idx + self.num_frames, dtype=int) - break - - frames = video_reader.get_batch(frames_index).asnumpy() - wrong_frames = video_reader.get_batch(wrong_frames_index).asnumpy() - - return frames, wrong_frames, start_idx - - def worker_init_fn(self, worker_id): - # Initialize the face mesh object in each worker process, - # because the face mesh object cannot be called in subprocesses - self.worker_id = worker_id - setattr( - self, - f"image_processor_{worker_id}", - ImageProcessor(self.resolution, self.mask, mask_image=self.mask_image), - ) - - def __getitem__(self, idx): - image_processor = getattr(self, f"image_processor_{self.worker_id}") - while True: - try: - idx = random.randint(0, len(self) - 1) - - # Get video file path - video_path = self.video_paths[idx] - - vr = VideoReader(video_path, ctx=cpu(self.worker_id)) - - if len(vr) < 3 * self.num_frames: - continue - - continuous_frames, ref_frames, start_idx = self.get_frames(vr) - - if self.load_audio_data: - mel_cache_path = os.path.join( - "/mnt/bn/maliva-gen-ai-v2/chunyu.li/audio_cache/mel_new", - os.path.basename(video_path).replace(".mp4", "_mel.pt"), - ) - - if os.path.isfile(mel_cache_path): - try: - original_mel = torch.load(mel_cache_path) - except Exception as e: - print(f"{type(e).__name__} - {e} - {mel_cache_path}") - os.remove(mel_cache_path) - original_mel = self.read_audio(video_path) - torch.save(original_mel, mel_cache_path) - else: - original_mel = self.read_audio(video_path) - torch.save(original_mel, mel_cache_path) - - mel = self.crop_audio_window(original_mel, start_idx) - - if mel.shape[-1] != self.mel_window_length: - continue - - # mel_overlap = self.crop_overlap_audio_window(original_mel, start_idx) - - # if mel_overlap is None: - # continue - mel_overlap = [] - else: - mel = [] - mel_overlap = [] - - gt, masked_gt, mask = image_processor.prepare_masks_and_masked_images( - continuous_frames, affine_transform=False - ) - - if self.mask == "fix_mask": - ref, _, _ = image_processor.prepare_masks_and_masked_images(ref_frames, affine_transform=False) - else: - ref = image_processor.process_images(ref_frames) - vr.seek(0) # avoid memory leak - break - - except Exception as e: # Handle the exception of face not detcted - print(f"{type(e).__name__} - {e} - {video_path}") - if "vr" in locals(): - vr.seek(0) # avoid memory leak - - sample = dict( - gt=gt, - masked_gt=masked_gt, - ref=ref, - mel_overlap=mel_overlap, - mel=mel, - mask=mask, - video_path=video_path, - start_idx=start_idx, - ) - - return sample +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import math +import numpy as np +from torch.utils.data import Dataset +import torch +import random +import cv2 +from ..utils.image_processor import ImageProcessor, load_fixed_mask +from ..utils.audio import melspectrogram +from decord import AudioReader, VideoReader, cpu +import torch.nn.functional as F + + +class UNetDataset(Dataset): + def __init__(self, train_data_dir: str, config): + if config.data.train_fileslist != "": + with open(config.data.train_fileslist) as file: + self.video_paths = [line.rstrip() for line in file] + elif train_data_dir != "": + self.video_paths = [] + for file in os.listdir(train_data_dir): + if file.endswith(".mp4"): + self.video_paths.append(os.path.join(train_data_dir, file)) + else: + raise ValueError("data_dir and fileslist cannot be both empty") + + self.resolution = config.data.resolution + self.num_frames = config.data.num_frames + + self.mel_window_length = math.ceil(self.num_frames / 5 * 16) + + self.audio_sample_rate = config.data.audio_sample_rate + self.video_fps = config.data.video_fps + self.mask = config.data.mask + self.mask_image = load_fixed_mask(self.resolution, config.data.mask_image_path) + self.load_audio_data = config.model.add_audio_layer and config.run.use_syncnet + self.audio_mel_cache_dir = config.data.audio_mel_cache_dir + os.makedirs(self.audio_mel_cache_dir, exist_ok=True) + + def __len__(self): + return len(self.video_paths) + + def read_audio(self, video_path: str): + ar = AudioReader(video_path, ctx=cpu(self.worker_id), sample_rate=self.audio_sample_rate) + original_mel = melspectrogram(ar[:].asnumpy().squeeze(0)) + return torch.from_numpy(original_mel) + + def crop_audio_window(self, original_mel, start_index): + start_idx = int(80.0 * (start_index / float(self.video_fps))) + end_idx = start_idx + self.mel_window_length + return original_mel[:, start_idx:end_idx].unsqueeze(0) + + def get_frames(self, video_reader: VideoReader): + total_num_frames = len(video_reader) + + start_idx = random.randint(0, total_num_frames - self.num_frames) + gt_frames_index = np.arange(start_idx, start_idx + self.num_frames, dtype=int) + + while True: + ref_start_idx = random.randint(0, total_num_frames - self.num_frames) + if ref_start_idx > start_idx - self.num_frames and ref_start_idx < start_idx + self.num_frames: + continue + ref_frames_index = np.arange(ref_start_idx, ref_start_idx + self.num_frames, dtype=int) + break + + gt_frames = video_reader.get_batch(gt_frames_index).asnumpy() + ref_frames = video_reader.get_batch(ref_frames_index).asnumpy() + + return gt_frames, ref_frames, start_idx + + def worker_init_fn(self, worker_id): + # Initialize the face mesh object in each worker process, + # because the face mesh object cannot be called in subprocesses + self.worker_id = worker_id + setattr( + self, + f"image_processor_{worker_id}", + ImageProcessor(self.resolution, self.mask, mask_image=self.mask_image), + ) + + def __getitem__(self, idx): + image_processor: ImageProcessor = getattr(self, f"image_processor_{self.worker_id}") + while True: + try: + idx = random.randint(0, len(self) - 1) + + # Get video file path + video_path = self.video_paths[idx] + + vr = VideoReader(video_path, ctx=cpu(self.worker_id)) + + if len(vr) < 3 * self.num_frames: + continue + + gt_frames, ref_frames, start_idx = self.get_frames(vr) + + if self.load_audio_data: + mel_cache_path = os.path.join( + self.audio_mel_cache_dir, os.path.basename(video_path).replace(".mp4", "_mel.pt") + ) + + if os.path.isfile(mel_cache_path): + try: + original_mel = torch.load(mel_cache_path, weights_only=True) + except Exception as e: + print(f"{type(e).__name__} - {e} - {mel_cache_path}") + os.remove(mel_cache_path) + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + else: + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + + mel = self.crop_audio_window(original_mel, start_idx) + + if mel.shape[-1] != self.mel_window_length: + continue + else: + mel = [] + + gt_pixel_values, masked_pixel_values, masks = image_processor.prepare_masks_and_masked_images( + gt_frames, affine_transform=False + ) # (f, c, h, w) + ref_pixel_values = image_processor.process_images(ref_frames) + + vr.seek(0) # avoid memory leak + break + + except Exception as e: # Handle the exception of face not detcted + print(f"{type(e).__name__} - {e} - {video_path}") + if "vr" in locals(): + vr.seek(0) # avoid memory leak + + sample = dict( + gt_pixel_values=gt_pixel_values, + masked_pixel_values=masked_pixel_values, + ref_pixel_values=ref_pixel_values, + mel=mel, + masks=masks, + video_path=video_path, + start_idx=start_idx, + ) + + return sample diff --git a/latentsync/models/attention.py b/latentsync/models/attention.py index 7682806..2f71313 100644 --- a/latentsync/models/attention.py +++ b/latentsync/models/attention.py @@ -1,488 +1,280 @@ -# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py -from dataclasses import dataclass -# Removed turtle import as forward methods are defined in classes -from typing import Optional -import torch -import torch.nn.functional as F -from torch import nn -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers import ModelMixin -from diffusers.utils import BaseOutput -from diffusers.utils.import_utils import is_xformers_available -from diffusers.models.attention import Attention as CrossAttention, FeedForward, AdaLayerNorm -from einops import rearrange, repeat -from .utils import zero_module - - -@dataclass -class Transformer3DModelOutput(BaseOutput): - sample: torch.FloatTensor - - -if is_xformers_available(): - import xformers - import xformers.ops -else: - xformers = None - - -class Transformer3DModel(ModelMixin, ConfigMixin): - @register_to_config - def __init__( - self, - num_attention_heads: int = 16, - attention_head_dim: int = 88, - in_channels: Optional[int] = None, - num_layers: int = 1, - dropout: float = 0.0, - norm_num_groups: int = 32, - cross_attention_dim: Optional[int] = None, - attention_bias: bool = False, - activation_fn: str = "geglu", - num_embeds_ada_norm: Optional[int] = None, - use_linear_projection: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - use_motion_module: bool = False, - unet_use_cross_frame_attention=None, - unet_use_temporal_attention=None, - add_audio_layer=False, - audio_condition_method="cross_attn", - custom_audio_layer: bool = False, - ): - super().__init__() - self.use_linear_projection = use_linear_projection - self.num_attention_heads = num_attention_heads - self.attention_head_dim = attention_head_dim - inner_dim = num_attention_heads * attention_head_dim - - # Define input layers - self.in_channels = in_channels - - self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) - if use_linear_projection: - self.proj_in = nn.Linear(in_channels, inner_dim) - else: - self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) - - if not custom_audio_layer: - # Define transformers blocks - self.transformer_blocks = nn.ModuleList( - [ - BasicTransformerBlock( - inner_dim, - num_attention_heads, - attention_head_dim, - dropout=dropout, - cross_attention_dim=cross_attention_dim, - activation_fn=activation_fn, - num_embeds_ada_norm=num_embeds_ada_norm, - attention_bias=attention_bias, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - custom_audio_layer=custom_audio_layer, - audio_condition_method=audio_condition_method, - ) - for d in range(num_layers) - ] - ) - else: - self.transformer_blocks = nn.ModuleList( - [ - AudioTransformerBlock( - inner_dim, - num_attention_heads, - attention_head_dim, - dropout=dropout, - cross_attention_dim=cross_attention_dim, - activation_fn=activation_fn, - num_embeds_ada_norm=num_embeds_ada_norm, - attention_bias=attention_bias, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - ) - for d in range(num_layers) - ] - ) - - # 4. Define output layers - if use_linear_projection: - self.proj_out = nn.Linear(in_channels, inner_dim) - else: - self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) - - if custom_audio_layer: - self.proj_out = zero_module(self.proj_out) - - def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True): - # Input - assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." - video_length = hidden_states.shape[2] - hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") - - # No need to do this for audio input, because different audio samples are independent - # encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length) - - batch, channel, height, weight = hidden_states.shape - residual = hidden_states - - hidden_states = self.norm(hidden_states) - if not self.use_linear_projection: - hidden_states = self.proj_in(hidden_states) - inner_dim = hidden_states.shape[1] - hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) - else: - inner_dim = hidden_states.shape[1] - hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) - hidden_states = self.proj_in(hidden_states) - - # Blocks - for block in self.transformer_blocks: - hidden_states = block( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - timestep=timestep, - video_length=video_length, - ) - - # Output - if not self.use_linear_projection: - hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() - hidden_states = self.proj_out(hidden_states) - else: - hidden_states = self.proj_out(hidden_states) - hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() - - output = hidden_states + residual - - output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) - if not return_dict: - return (output,) - - return Transformer3DModelOutput(sample=output) - - -class BasicTransformerBlock(nn.Module): - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - dropout=0.0, - cross_attention_dim: Optional[int] = None, - activation_fn: str = "geglu", - num_embeds_ada_norm: Optional[int] = None, - attention_bias: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - use_motion_module: bool = False, - unet_use_cross_frame_attention=None, - unet_use_temporal_attention=None, - add_audio_layer=False, - custom_audio_layer=False, - audio_condition_method="cross_attn", - ): - super().__init__() - self.only_cross_attention = only_cross_attention - self.use_ada_layer_norm = num_embeds_ada_norm is not None - self.unet_use_cross_frame_attention = unet_use_cross_frame_attention - self.unet_use_temporal_attention = unet_use_temporal_attention - self.use_motion_module = use_motion_module - self.add_audio_layer = add_audio_layer - - # SC-Attn - assert unet_use_cross_frame_attention is not None - if unet_use_cross_frame_attention: - raise NotImplementedError("SparseCausalAttention2D not implemented yet.") - else: - self.attn1 = CrossAttention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - ) - self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) - - # Cross-Attn - if add_audio_layer and audio_condition_method == "cross_attn" and not custom_audio_layer: - self.audio_cross_attn = AudioCrossAttn( - dim=dim, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - attention_head_dim=attention_head_dim, - dropout=dropout, - attention_bias=attention_bias, - upcast_attention=upcast_attention, - num_embeds_ada_norm=num_embeds_ada_norm, - use_ada_layer_norm=self.use_ada_layer_norm, - zero_proj_out=False, - ) - else: - self.audio_cross_attn = None - - # Feed-forward - self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) - self.norm3 = nn.LayerNorm(dim) - - # Temp-Attn - assert unet_use_temporal_attention is not None - if unet_use_temporal_attention: - self.attn_temp = CrossAttention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - ) - nn.init.zeros_(self.attn_temp.to_out[0].weight.data) - self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) - - def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool): - if not is_xformers_available(): - print("Here is how to install it") - raise ModuleNotFoundError( - "Refer to https://github.com/facebookresearch/xformers for more information on how to install" - " xformers", - name="xformers", - ) - elif not torch.cuda.is_available(): - raise ValueError( - "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only" - " available for GPU " - ) - else: - try: - # Make sure we can run the memory efficient attention - _ = xformers.ops.memory_efficient_attention( - torch.randn((1, 2, 40), device="cuda"), - torch.randn((1, 2, 40), device="cuda"), - torch.randn((1, 2, 40), device="cuda"), - ) - except Exception as e: - raise e - self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers - if self.audio_cross_attn is not None: - self.audio_cross_attn.attn._use_memory_efficient_attention_xformers = ( - use_memory_efficient_attention_xformers - ) - # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers - - def forward( - self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None - ): - # SparseCausal-Attention - norm_hidden_states = ( - self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) - ) - - # if self.only_cross_attention: - # hidden_states = ( - # self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states - # ) - # else: - # hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states - - # pdb.set_trace() - if self.unet_use_cross_frame_attention: - hidden_states = ( - self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) - + hidden_states - ) - else: - hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states - - if self.audio_cross_attn is not None and encoder_hidden_states is not None: - hidden_states = self.audio_cross_attn( - hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask - ) - - # Feed-forward - hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states - - # Temporal-Attention - if self.unet_use_temporal_attention: - d = hidden_states.shape[1] - hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) - norm_hidden_states = ( - self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states) - ) - hidden_states = self.attn_temp(norm_hidden_states) + hidden_states - hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) - - return hidden_states - - -class AudioTransformerBlock(nn.Module): - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - dropout=0.0, - cross_attention_dim: Optional[int] = None, - activation_fn: str = "geglu", - num_embeds_ada_norm: Optional[int] = None, - attention_bias: bool = False, - only_cross_attention: bool = False, - upcast_attention: bool = False, - use_motion_module: bool = False, - unet_use_cross_frame_attention=None, - unet_use_temporal_attention=None, - add_audio_layer=False, - ): - super().__init__() - self.only_cross_attention = only_cross_attention - self.use_ada_layer_norm = num_embeds_ada_norm is not None - self.unet_use_cross_frame_attention = unet_use_cross_frame_attention - self.unet_use_temporal_attention = unet_use_temporal_attention - self.use_motion_module = use_motion_module - self.add_audio_layer = add_audio_layer - - # SC-Attn - assert unet_use_cross_frame_attention is not None - if unet_use_cross_frame_attention: - raise NotImplementedError("SparseCausalAttention2D not implemented yet.") - else: - self.attn1 = CrossAttention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - ) - self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) - - self.audio_cross_attn = AudioCrossAttn( - dim=dim, - cross_attention_dim=cross_attention_dim, - num_attention_heads=num_attention_heads, - attention_head_dim=attention_head_dim, - dropout=dropout, - attention_bias=attention_bias, - upcast_attention=upcast_attention, - num_embeds_ada_norm=num_embeds_ada_norm, - use_ada_layer_norm=self.use_ada_layer_norm, - zero_proj_out=False, - ) - - # Feed-forward - self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) - self.norm3 = nn.LayerNorm(dim) - - def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool): - if not is_xformers_available(): - print("Here is how to install it") - raise ModuleNotFoundError( - "Refer to https://github.com/facebookresearch/xformers for more information on how to install" - " xformers", - name="xformers", - ) - elif not torch.cuda.is_available(): - raise ValueError( - "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only" - " available for GPU " - ) - else: - try: - # Make sure we can run the memory efficient attention - _ = xformers.ops.memory_efficient_attention( - torch.randn((1, 2, 40), device="cuda"), - torch.randn((1, 2, 40), device="cuda"), - torch.randn((1, 2, 40), device="cuda"), - ) - except Exception as e: - raise e - self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers - if self.audio_cross_attn is not None: - self.audio_cross_attn.attn._use_memory_efficient_attention_xformers = ( - use_memory_efficient_attention_xformers - ) - # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers - - def forward( - self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None - ): - # SparseCausal-Attention - norm_hidden_states = ( - self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) - ) - - # pdb.set_trace() - if self.unet_use_cross_frame_attention: - hidden_states = ( - self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) - + hidden_states - ) - else: - hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states - - if self.audio_cross_attn is not None and encoder_hidden_states is not None: - hidden_states = self.audio_cross_attn( - hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask - ) - - # Feed-forward - hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states - - return hidden_states - - -class AudioCrossAttn(nn.Module): - def __init__( - self, - dim, - cross_attention_dim, - num_attention_heads, - attention_head_dim, - dropout, - attention_bias, - upcast_attention, - num_embeds_ada_norm, - use_ada_layer_norm, - zero_proj_out=False, - ): - super().__init__() - - self.norm = AdaLayerNorm(dim, num_embeds_ada_norm) if use_ada_layer_norm else nn.LayerNorm(dim) - self.attn = CrossAttention( - query_dim=dim, - cross_attention_dim=cross_attention_dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - ) - - if zero_proj_out: - self.proj_out = zero_module(nn.Linear(dim, dim)) - - self.zero_proj_out = zero_proj_out - self.use_ada_layer_norm = use_ada_layer_norm - - def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None): - previous_hidden_states = hidden_states - hidden_states = self.norm(hidden_states, timestep) if self.use_ada_layer_norm else self.norm(hidden_states) - - if encoder_hidden_states.dim() == 4: - encoder_hidden_states = rearrange(encoder_hidden_states, "b f n d -> (b f) n d") - - hidden_states = self.attn( - hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask - ) - - if self.zero_proj_out: - hidden_states = self.proj_out(hidden_states) - return hidden_states + previous_hidden_states +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py + +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.models.attention import FeedForward, AdaLayerNorm + +from einops import rearrange, repeat + + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +class Transformer3DModel(ModelMixin, ConfigMixin): + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + add_audio_layer=False, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + + # Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + add_audio_layer=add_audio_layer, + ) + for d in range(num_layers) + ] + ) + + # Define output layers + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True): + # Input + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + video_length=video_length, + ) + + # Output + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + upcast_attention: bool = False, + add_audio_layer=False, + ): + super().__init__() + self.use_ada_layer_norm = num_embeds_ada_norm is not None + self.add_audio_layer = add_audio_layer + + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + + # Cross-attn + if add_audio_layer: + self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + else: + self.attn2 = None + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + + def forward( + self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None + ): + norm_hidden_states = ( + self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) + ) + + hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states + + if self.attn2 is not None and encoder_hidden_states is not None: + if encoder_hidden_states.dim() == 4: + encoder_hidden_states = rearrange(encoder_hidden_states, "b f s d -> (b f) s d") + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + hidden_states = ( + self.attn2( + norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + hidden_states + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + return hidden_states + + +class Attention(nn.Module): + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + bias=False, + upcast_attention: bool = False, + upcast_softmax: bool = False, + norm_num_groups: Optional[int] = None, + ): + super().__init__() + inner_dim = dim_head * heads + cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.upcast_attention = upcast_attention + self.upcast_softmax = upcast_softmax + + self.scale = dim_head**-0.5 + + self.heads = heads + + if norm_num_groups is not None: + self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True) + else: + self.group_norm = None + + self.to_q = nn.Linear(query_dim, inner_dim, bias=bias) + self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias) + self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(inner_dim, query_dim)) + self.to_out.append(nn.Dropout(dropout)) + + def split_heads(self, tensor): + batch_size, seq_len, dim = tensor.shape + tensor = tensor.reshape(batch_size, seq_len, self.heads, dim // self.heads) + tensor = tensor.permute(0, 2, 1, 3) + return tensor + + def concat_heads(self, tensor): + batch_size, heads, seq_len, head_dim = tensor.shape + tensor = tensor.permute(0, 2, 1, 3) + tensor = tensor.reshape(batch_size, seq_len, heads * head_dim) + return tensor + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) + query = self.split_heads(query) + + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + key = self.split_heads(key) + value = self.split_heads(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # Use PyTorch native implementation of FlashAttention-2 + hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask) + + hidden_states = self.concat_heads(hidden_states) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + return hidden_states diff --git a/latentsync/models/motion_module.py b/latentsync/models/motion_module.py index 24a0d3b..a9f3df0 100644 --- a/latentsync/models/motion_module.py +++ b/latentsync/models/motion_module.py @@ -1,332 +1,313 @@ -# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py - -# Actually we don't use the motion module in the final version of LatentSync -# When we started the project, we used the codebase of AnimateDiff and tried motion module -# But the results are poor, and we decied to leave the code here for possible future usage - -from dataclasses import dataclass - -import torch -import torch.nn.functional as F -from torch import nn - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers import ModelMixin -from diffusers.utils import BaseOutput -from diffusers.utils.import_utils import is_xformers_available -from diffusers.models.attention import Attention as CrossAttention, FeedForward - -from einops import rearrange, repeat -import math -from .utils import zero_module - - -@dataclass -class TemporalTransformer3DModelOutput(BaseOutput): - sample: torch.FloatTensor - - -if is_xformers_available(): - import xformers - import xformers.ops -else: - xformers = None - - -def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict): - if motion_module_type == "Vanilla": - return VanillaTemporalModule( - in_channels=in_channels, - **motion_module_kwargs, - ) - else: - raise ValueError - - -class VanillaTemporalModule(nn.Module): - def __init__( - self, - in_channels, - num_attention_heads=8, - num_transformer_block=2, - attention_block_types=("Temporal_Self", "Temporal_Self"), - cross_frame_attention_mode=None, - temporal_position_encoding=False, - temporal_position_encoding_max_len=24, - temporal_attention_dim_div=1, - zero_initialize=True, - ): - super().__init__() - - self.temporal_transformer = TemporalTransformer3DModel( - in_channels=in_channels, - num_attention_heads=num_attention_heads, - attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div, - num_layers=num_transformer_block, - attention_block_types=attention_block_types, - cross_frame_attention_mode=cross_frame_attention_mode, - temporal_position_encoding=temporal_position_encoding, - temporal_position_encoding_max_len=temporal_position_encoding_max_len, - ) - - if zero_initialize: - self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out) - - def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None): - hidden_states = input_tensor - hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask) - - output = hidden_states - return output - - -class TemporalTransformer3DModel(nn.Module): - def __init__( - self, - in_channels, - num_attention_heads, - attention_head_dim, - num_layers, - attention_block_types=( - "Temporal_Self", - "Temporal_Self", - ), - dropout=0.0, - norm_num_groups=32, - cross_attention_dim=768, - activation_fn="geglu", - attention_bias=False, - upcast_attention=False, - cross_frame_attention_mode=None, - temporal_position_encoding=False, - temporal_position_encoding_max_len=24, - ): - super().__init__() - - inner_dim = num_attention_heads * attention_head_dim - - self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) - self.proj_in = nn.Linear(in_channels, inner_dim) - - self.transformer_blocks = nn.ModuleList( - [ - TemporalTransformerBlock( - dim=inner_dim, - num_attention_heads=num_attention_heads, - attention_head_dim=attention_head_dim, - attention_block_types=attention_block_types, - dropout=dropout, - norm_num_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - activation_fn=activation_fn, - attention_bias=attention_bias, - upcast_attention=upcast_attention, - cross_frame_attention_mode=cross_frame_attention_mode, - temporal_position_encoding=temporal_position_encoding, - temporal_position_encoding_max_len=temporal_position_encoding_max_len, - ) - for d in range(num_layers) - ] - ) - self.proj_out = nn.Linear(inner_dim, in_channels) - - def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): - assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." - video_length = hidden_states.shape[2] - hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") - - batch, channel, height, weight = hidden_states.shape - residual = hidden_states - - hidden_states = self.norm(hidden_states) - hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, channel) - hidden_states = self.proj_in(hidden_states) - - # Transformer Blocks - for block in self.transformer_blocks: - hidden_states = block( - hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length - ) - - # output - hidden_states = self.proj_out(hidden_states) - hidden_states = hidden_states.reshape(batch, height, weight, channel).permute(0, 3, 1, 2).contiguous() - - output = hidden_states + residual - output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) - - return output - - -class TemporalTransformerBlock(nn.Module): - def __init__( - self, - dim, - num_attention_heads, - attention_head_dim, - attention_block_types=( - "Temporal_Self", - "Temporal_Self", - ), - dropout=0.0, - norm_num_groups=32, - cross_attention_dim=768, - activation_fn="geglu", - attention_bias=False, - upcast_attention=False, - cross_frame_attention_mode=None, - temporal_position_encoding=False, - temporal_position_encoding_max_len=24, - ): - super().__init__() - - attention_blocks = [] - norms = [] - - for block_name in attention_block_types: - attention_blocks.append( - VersatileAttention( - attention_mode=block_name.split("_")[0], - cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None, - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - dropout=dropout, - bias=attention_bias, - upcast_attention=upcast_attention, - cross_frame_attention_mode=cross_frame_attention_mode, - temporal_position_encoding=temporal_position_encoding, - temporal_position_encoding_max_len=temporal_position_encoding_max_len, - ) - ) - norms.append(nn.LayerNorm(dim)) - - self.attention_blocks = nn.ModuleList(attention_blocks) - self.norms = nn.ModuleList(norms) - - self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) - self.ff_norm = nn.LayerNorm(dim) - - def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): - for attention_block, norm in zip(self.attention_blocks, self.norms): - norm_hidden_states = norm(hidden_states) - hidden_states = ( - attention_block( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None, - video_length=video_length, - ) - + hidden_states - ) - - hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states - - output = hidden_states - return output - - -class PositionalEncoding(nn.Module): - def __init__(self, d_model, dropout=0.0, max_len=24): - super().__init__() - self.dropout = nn.Dropout(p=dropout) - position = torch.arange(max_len).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) - pe = torch.zeros(1, max_len, d_model) - pe[0, :, 0::2] = torch.sin(position * div_term) - pe[0, :, 1::2] = torch.cos(position * div_term) - self.register_buffer("pe", pe) - - def forward(self, x): - x = x + self.pe[:, : x.size(1)] - return self.dropout(x) - - -class VersatileAttention(CrossAttention): - def __init__( - self, - attention_mode=None, - cross_frame_attention_mode=None, - temporal_position_encoding=False, - temporal_position_encoding_max_len=24, - *args, - **kwargs, - ): - super().__init__(*args, **kwargs) - assert attention_mode == "Temporal" - - self.attention_mode = attention_mode - self.is_cross_attention = kwargs["cross_attention_dim"] is not None - - self.pos_encoder = ( - PositionalEncoding(kwargs["query_dim"], dropout=0.0, max_len=temporal_position_encoding_max_len) - if (temporal_position_encoding and attention_mode == "Temporal") - else None - ) - - def extra_repr(self): - return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}" - - def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): - batch_size, sequence_length, _ = hidden_states.shape - - if self.attention_mode == "Temporal": - d = hidden_states.shape[1] - hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) - - if self.pos_encoder is not None: - hidden_states = self.pos_encoder(hidden_states) - - encoder_hidden_states = ( - repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) - if encoder_hidden_states is not None - else encoder_hidden_states - ) - else: - raise NotImplementedError - - # encoder_hidden_states = encoder_hidden_states - - if self.group_norm is not None: - hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = self.to_q(hidden_states) - dim = query.shape[-1] - query = self.reshape_heads_to_batch_dim(query) - - if self.added_kv_proj_dim is not None: - raise NotImplementedError - - encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states - key = self.to_k(encoder_hidden_states) - value = self.to_v(encoder_hidden_states) - - key = self.reshape_heads_to_batch_dim(key) - value = self.reshape_heads_to_batch_dim(value) - - if attention_mask is not None: - if attention_mask.shape[-1] != query.shape[1]: - target_length = query.shape[1] - attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) - attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) - - # attention, what we cannot get enough of - if self._use_memory_efficient_attention_xformers: - hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) - # Some versions of xformers return output in fp32, cast it back to the dtype of the input - hidden_states = hidden_states.to(query.dtype) - else: - if self._slice_size is None or query.shape[0] // self._slice_size == 1: - hidden_states = self._attention(query, key, value, attention_mask) - else: - hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) - - # linear proj - hidden_states = self.to_out[0](hidden_states) - - # dropout - hidden_states = self.to_out[1](hidden_states) - - if self.attention_mode == "Temporal": - hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) - - return hidden_states +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py + +# Actually we don't use the motion module in the final version of LatentSync +# When we started the project, we used the codebase of AnimateDiff and tried motion module +# But the results are poor, and we decied to leave the code here for possible future usage + +from dataclasses import dataclass + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.models.attention import FeedForward +from .attention import Attention + +from einops import rearrange, repeat +import math +from .utils import zero_module + + +@dataclass +class TemporalTransformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict): + if motion_module_type == "Vanilla": + return VanillaTemporalModule( + in_channels=in_channels, + **motion_module_kwargs, + ) + else: + raise ValueError + + +class VanillaTemporalModule(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads=8, + num_transformer_block=2, + attention_block_types=("Temporal_Self", "Temporal_Self"), + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + temporal_attention_dim_div=1, + zero_initialize=True, + ): + super().__init__() + + self.temporal_transformer = TemporalTransformer3DModel( + in_channels=in_channels, + num_attention_heads=num_attention_heads, + attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div, + num_layers=num_transformer_block, + attention_block_types=attention_block_types, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + + if zero_initialize: + self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out) + + def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None): + hidden_states = input_tensor + hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask) + + output = hidden_states + return output + + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads, + attention_head_dim, + num_layers, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + for d in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, channel) + hidden_states = self.proj_in(hidden_states) + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length + ) + + # output + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, channel).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + + return output + + +class TemporalTransformerBlock(nn.Module): + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + attention_blocks = [] + norms = [] + + for block_name in attention_block_types: + attention_blocks.append( + VersatileAttention( + attention_mode=block_name.split("_")[0], + cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None, + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + ) + norms.append(nn.LayerNorm(dim)) + + self.attention_blocks = nn.ModuleList(attention_blocks) + self.norms = nn.ModuleList(norms) + + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.ff_norm = nn.LayerNorm(dim) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + for attention_block, norm in zip(self.attention_blocks, self.norms): + norm_hidden_states = norm(hidden_states) + hidden_states = ( + attention_block( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None, + video_length=video_length, + ) + + hidden_states + ) + + hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states + + output = hidden_states + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model, dropout=0.0, max_len=24): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x): + x = x + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class VersatileAttention(Attention): + def __init__( + self, + attention_mode=None, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + assert attention_mode == "Temporal" + + self.attention_mode = attention_mode + self.is_cross_attention = kwargs["cross_attention_dim"] is not None + + self.pos_encoder = ( + PositionalEncoding(kwargs["query_dim"], dropout=0.0, max_len=temporal_position_encoding_max_len) + if (temporal_position_encoding and attention_mode == "Temporal") + else None + ) + + def extra_repr(self): + return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}" + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + if self.attention_mode == "Temporal": + s = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) s c -> (b s) f c", f=video_length) + + if self.pos_encoder is not None: + hidden_states = self.pos_encoder(hidden_states) + + ##### This section will not be executed ##### + encoder_hidden_states = ( + repeat(encoder_hidden_states, "b n c -> (b s) n c", s=s) + if encoder_hidden_states is not None + else encoder_hidden_states + ) + ############################################# + else: + raise NotImplementedError + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) + query = self.split_heads(query) + + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + key = self.split_heads(key) + value = self.split_heads(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # Use PyTorch native implementation of FlashAttention-2 + hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask) + + hidden_states = self.concat_heads(hidden_states) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + + if self.attention_mode == "Temporal": + hidden_states = rearrange(hidden_states, "(b s) f c -> (b f) s c", s=s) + + return hidden_states diff --git a/latentsync/models/resnet.py b/latentsync/models/resnet.py index 3654ae3..dc1517e 100644 --- a/latentsync/models/resnet.py +++ b/latentsync/models/resnet.py @@ -1,234 +1,228 @@ -# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from einops import rearrange - - -class InflatedConv3d(nn.Conv2d): - def forward(self, x): - video_length = x.shape[2] - - x = rearrange(x, "b c f h w -> (b f) c h w") - x = super().forward(x) - x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) - - return x - - -class InflatedGroupNorm(nn.GroupNorm): - def forward(self, x): - video_length = x.shape[2] - - x = rearrange(x, "b c f h w -> (b f) c h w") - x = super().forward(x) - x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) - - return x - - -class Upsample3D(nn.Module): - def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_conv_transpose = use_conv_transpose - self.name = name - - conv = None - if use_conv_transpose: - raise NotImplementedError - elif use_conv: - self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) - - def forward(self, hidden_states, output_size=None): - assert hidden_states.shape[1] == self.channels - - if self.use_conv_transpose: - raise NotImplementedError - - # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 - dtype = hidden_states.dtype - if dtype == torch.bfloat16: - hidden_states = hidden_states.to(torch.float32) - - # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 - if hidden_states.shape[0] >= 64: - hidden_states = hidden_states.contiguous() - - # if `output_size` is passed we force the interpolation output - # size and do not make use of `scale_factor=2` - if output_size is None: - hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") - else: - hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") - - # If the input is bfloat16, we cast back to bfloat16 - if dtype == torch.bfloat16: - hidden_states = hidden_states.to(dtype) - - # if self.use_conv: - # if self.name == "conv": - # hidden_states = self.conv(hidden_states) - # else: - # hidden_states = self.Conv2d_0(hidden_states) - hidden_states = self.conv(hidden_states) - - return hidden_states - - -class Downsample3D(nn.Module): - def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.padding = padding - stride = 2 - self.name = name - - if use_conv: - self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) - else: - raise NotImplementedError - - def forward(self, hidden_states): - assert hidden_states.shape[1] == self.channels - if self.use_conv and self.padding == 0: - raise NotImplementedError - - assert hidden_states.shape[1] == self.channels - hidden_states = self.conv(hidden_states) - - return hidden_states - - -class ResnetBlock3D(nn.Module): - def __init__( - self, - *, - in_channels, - out_channels=None, - conv_shortcut=False, - dropout=0.0, - temb_channels=512, - groups=32, - groups_out=None, - pre_norm=True, - eps=1e-6, - non_linearity="swish", - time_embedding_norm="default", - output_scale_factor=1.0, - use_in_shortcut=None, - use_inflated_groupnorm=False, - ): - super().__init__() - self.pre_norm = pre_norm - self.pre_norm = True - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.use_conv_shortcut = conv_shortcut - self.time_embedding_norm = time_embedding_norm - self.output_scale_factor = output_scale_factor - - if groups_out is None: - groups_out = groups - - assert use_inflated_groupnorm != None - if use_inflated_groupnorm: - self.norm1 = InflatedGroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) - else: - self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) - - self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) - - if temb_channels is not None: - time_emb_proj_out_channels = out_channels - # if self.time_embedding_norm == "default": - # time_emb_proj_out_channels = out_channels - # elif self.time_embedding_norm == "scale_shift": - # time_emb_proj_out_channels = out_channels * 2 - # else: - # raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") - - self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) - else: - self.time_emb_proj = None - - if self.time_embedding_norm == "scale_shift": - self.double_len_linear = torch.nn.Linear(time_emb_proj_out_channels, 2 * time_emb_proj_out_channels) - else: - self.double_len_linear = None - - if use_inflated_groupnorm: - self.norm2 = InflatedGroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) - else: - self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) - - self.dropout = torch.nn.Dropout(dropout) - self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) - - if non_linearity == "swish": - self.nonlinearity = lambda x: F.silu(x) - elif non_linearity == "mish": - self.nonlinearity = Mish() - elif non_linearity == "silu": - self.nonlinearity = nn.SiLU() - - self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut - - self.conv_shortcut = None - if self.use_in_shortcut: - self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - - def forward(self, input_tensor, temb): - hidden_states = input_tensor - - hidden_states = self.norm1(hidden_states) - hidden_states = self.nonlinearity(hidden_states) - - hidden_states = self.conv1(hidden_states) - - if temb is not None: - if temb.dim() == 2: - # input (1, 1280) - temb = self.time_emb_proj(self.nonlinearity(temb)) - temb = temb[:, :, None, None, None] # unsqueeze - else: - # input (1, 1280, 16) - temb = temb.permute(0, 2, 1) - temb = self.time_emb_proj(self.nonlinearity(temb)) - if self.double_len_linear is not None: - temb = self.double_len_linear(self.nonlinearity(temb)) - temb = temb.permute(0, 2, 1) - temb = temb[:, :, :, None, None] - - if temb is not None and self.time_embedding_norm == "default": - hidden_states = hidden_states + temb - - hidden_states = self.norm2(hidden_states) - - if temb is not None and self.time_embedding_norm == "scale_shift": - scale, shift = torch.chunk(temb, 2, dim=1) - hidden_states = hidden_states * (1 + scale) + shift - - hidden_states = self.nonlinearity(hidden_states) - - hidden_states = self.dropout(hidden_states) - hidden_states = self.conv2(hidden_states) - - if self.conv_shortcut is not None: - input_tensor = self.conv_shortcut(input_tensor) - - output_tensor = (input_tensor + hidden_states) / self.output_scale_factor - - return output_tensor - - -class Mish(torch.nn.Module): - def forward(self, hidden_states): - return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange + + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class InflatedGroupNorm(nn.GroupNorm): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + raise NotImplementedError + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + raise NotImplementedError + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + raise NotImplementedError + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlock3D(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + time_embedding_norm="default", + output_scale_factor=1.0, + use_in_shortcut=None, + use_inflated_groupnorm=False, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.time_embedding_norm = time_embedding_norm + self.output_scale_factor = output_scale_factor + + if groups_out is None: + groups_out = groups + + assert use_inflated_groupnorm != None + if use_inflated_groupnorm: + self.norm1 = InflatedGroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + else: + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + + self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + time_emb_proj_out_channels = out_channels + elif self.time_embedding_norm == "scale_shift": + time_emb_proj_out_channels = out_channels * 2 + else: + raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") + + self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) + else: + self.time_emb_proj = None + + if self.time_embedding_norm == "scale_shift": + self.double_len_linear = torch.nn.Linear(time_emb_proj_out_channels, 2 * time_emb_proj_out_channels) + else: + self.double_len_linear = None + + if use_inflated_groupnorm: + self.norm2 = InflatedGroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + else: + self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + elif non_linearity == "mish": + self.nonlinearity = Mish() + elif non_linearity == "silu": + self.nonlinearity = nn.SiLU() + + self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + if temb is not None: + if temb.dim() == 2: + # input (1, 1280) + temb = self.time_emb_proj(self.nonlinearity(temb)) + temb = temb[:, :, None, None, None] # unsqueeze + else: + # input (1, 1280, 16) + temb = temb.permute(0, 2, 1) + temb = self.time_emb_proj(self.nonlinearity(temb)) + if self.double_len_linear is not None: + temb = self.double_len_linear(self.nonlinearity(temb)) + temb = temb.permute(0, 2, 1) + temb = temb[:, :, :, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + + +class Mish(torch.nn.Module): + def forward(self, hidden_states): + return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) diff --git a/latentsync/models/stable_syncnet.py b/latentsync/models/stable_syncnet.py new file mode 100644 index 0000000..8c4d163 --- /dev/null +++ b/latentsync/models/stable_syncnet.py @@ -0,0 +1,234 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn +from einops import rearrange +from torch.nn import functional as F +from .attention import Attention + +import torch.nn as nn +import torch.nn.functional as F + +from diffusers.models.attention import FeedForward +from einops import rearrange + + +class StableSyncNet(nn.Module): + def __init__(self, config, gradient_checkpointing=False): + super().__init__() + self.audio_encoder = DownEncoder2D( + in_channels=config["audio_encoder"]["in_channels"], + block_out_channels=config["audio_encoder"]["block_out_channels"], + downsample_factors=config["audio_encoder"]["downsample_factors"], + dropout=config["audio_encoder"]["dropout"], + attn_blocks=config["audio_encoder"]["attn_blocks"], + gradient_checkpointing=gradient_checkpointing, + ) + + self.visual_encoder = DownEncoder2D( + in_channels=config["visual_encoder"]["in_channels"], + block_out_channels=config["visual_encoder"]["block_out_channels"], + downsample_factors=config["visual_encoder"]["downsample_factors"], + dropout=config["visual_encoder"]["dropout"], + attn_blocks=config["visual_encoder"]["attn_blocks"], + gradient_checkpointing=gradient_checkpointing, + ) + + self.eval() + + def forward(self, image_sequences, audio_sequences): + vision_embeds = self.visual_encoder(image_sequences) # (b, c, 1, 1) + audio_embeds = self.audio_encoder(audio_sequences) # (b, c, 1, 1) + + vision_embeds = vision_embeds.reshape(vision_embeds.shape[0], -1) # (b, c) + audio_embeds = audio_embeds.reshape(audio_embeds.shape[0], -1) # (b, c) + + # Make them unit vectors + vision_embeds = F.normalize(vision_embeds, p=2, dim=1) + audio_embeds = F.normalize(audio_embeds, p=2, dim=1) + + return vision_embeds, audio_embeds + + +class ResnetBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + dropout: float = 0.0, + norm_num_groups: int = 32, + eps: float = 1e-6, + act_fn: str = "silu", + downsample_factor=2, + ): + super().__init__() + + self.norm1 = nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.norm2 = nn.GroupNorm(num_groups=norm_num_groups, num_channels=out_channels, eps=eps, affine=True) + self.dropout = nn.Dropout(dropout) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if act_fn == "relu": + self.act_fn = nn.ReLU() + elif act_fn == "silu": + self.act_fn = nn.SiLU() + + if in_channels != out_channels: + self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + else: + self.conv_shortcut = None + + if isinstance(downsample_factor, list): + downsample_factor = tuple(downsample_factor) + + if downsample_factor == 1: + self.downsample_conv = None + else: + self.downsample_conv = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=downsample_factor, padding=0 + ) + self.pad = (0, 1, 0, 1) + if isinstance(downsample_factor, tuple): + if downsample_factor[0] == 1: + self.pad = (0, 1, 1, 1) # The padding order is from back to front + elif downsample_factor[1] == 1: + self.pad = (1, 1, 0, 1) + + def forward(self, input_tensor): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.act_fn(hidden_states) + + hidden_states = self.conv1(hidden_states) + hidden_states = self.norm2(hidden_states) + hidden_states = self.act_fn(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + hidden_states += input_tensor + + if self.downsample_conv is not None: + hidden_states = F.pad(hidden_states, self.pad, mode="constant", value=0) + hidden_states = self.downsample_conv(hidden_states) + + return hidden_states + + +class AttentionBlock2D(nn.Module): + def __init__(self, query_dim, norm_num_groups=32, dropout=0.0): + super().__init__() + self.norm1 = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=query_dim, eps=1e-6, affine=True) + self.norm2 = nn.LayerNorm(query_dim) + self.norm3 = nn.LayerNorm(query_dim) + + self.ff = FeedForward(query_dim, dropout=dropout, activation_fn="geglu") + + self.conv_in = nn.Conv2d(query_dim, query_dim, kernel_size=1, stride=1, padding=0) + self.conv_out = nn.Conv2d(query_dim, query_dim, kernel_size=1, stride=1, padding=0) + + self.attn = Attention(query_dim=query_dim, heads=8, dim_head=query_dim // 8, dropout=dropout, bias=True) + + def forward(self, hidden_states): + assert hidden_states.dim() == 4, f"Expected hidden_states to have ndim=4, but got ndim={hidden_states.dim()}." + + batch, channel, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm1(hidden_states) + hidden_states = self.conv_in(hidden_states) + hidden_states = rearrange(hidden_states, "b c h w -> b (h w) c") + + norm_hidden_states = self.norm2(hidden_states) + + hidden_states = self.attn(norm_hidden_states, attention_mask=None) + hidden_states + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + hidden_states = rearrange(hidden_states, "b (h w) c -> b c h w", h=height, w=width) + hidden_states = self.conv_out(hidden_states) + + hidden_states = hidden_states + residual + return hidden_states + + +class DownEncoder2D(nn.Module): + def __init__( + self, + in_channels=4 * 16, + block_out_channels=[64, 128, 256, 256], + downsample_factors=[2, 2, 2, 2], + layers_per_block=2, + norm_num_groups=32, + attn_blocks=[1, 1, 1, 1], + dropout: float = 0.0, + act_fn="silu", + gradient_checkpointing=False, + ): + super().__init__() + self.layers_per_block = layers_per_block + self.gradient_checkpointing = gradient_checkpointing + + # in + self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1) + + # down + self.down_blocks = nn.ModuleList([]) + + output_channels = block_out_channels[0] + for i, block_out_channel in enumerate(block_out_channels): + input_channels = output_channels + output_channels = block_out_channel + # is_final_block = i == len(block_out_channels) - 1 + + down_block = ResnetBlock2D( + in_channels=input_channels, + out_channels=output_channels, + downsample_factor=downsample_factors[i], + norm_num_groups=norm_num_groups, + dropout=dropout, + act_fn=act_fn, + ) + + self.down_blocks.append(down_block) + + if attn_blocks[i] == 1: + attention_block = AttentionBlock2D(query_dim=output_channels, dropout=dropout) + self.down_blocks.append(attention_block) + + # out + self.norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) + self.act_fn_out = nn.ReLU() + + def forward(self, hidden_states): + hidden_states = self.conv_in(hidden_states) + + # down + for down_block in self.down_blocks: + if self.gradient_checkpointing: + hidden_states = torch.utils.checkpoint.checkpoint(down_block, hidden_states, use_reentrant=False) + else: + hidden_states = down_block(hidden_states) + + # post-process + hidden_states = self.norm_out(hidden_states) + hidden_states = self.act_fn_out(hidden_states) + + return hidden_states diff --git a/latentsync/models/unet.py b/latentsync/models/unet.py index 171c7db..cbe6113 100644 --- a/latentsync/models/unet.py +++ b/latentsync/models/unet.py @@ -1,528 +1,512 @@ -# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet.py - -from dataclasses import dataclass -from typing import List, Optional, Tuple, Union -import copy - -import torch -import torch.nn as nn -import torch.utils.checkpoint - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers import ModelMixin -from diffusers import UNet2DConditionModel -from diffusers.utils import BaseOutput, logging -from diffusers.models.embeddings import TimestepEmbedding, Timesteps -from .unet_blocks import ( - CrossAttnDownBlock3D, - CrossAttnUpBlock3D, - DownBlock3D, - UNetMidBlock3DCrossAttn, - UpBlock3D, - get_down_block, - get_up_block, -) -from .resnet import InflatedConv3d, InflatedGroupNorm - -from ..utils.util import zero_rank_log -from einops import rearrange -from .utils import zero_module - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class UNet3DConditionOutput(BaseOutput): - sample: torch.FloatTensor - - -class UNet3DConditionModel(ModelMixin, ConfigMixin): - _supports_gradient_checkpointing = True - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 4, - out_channels: int = 4, - center_input_sample: bool = False, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str] = ( - "CrossAttnDownBlock3D", - "CrossAttnDownBlock3D", - "CrossAttnDownBlock3D", - "DownBlock3D", - ), - mid_block_type: str = "UNetMidBlock3DCrossAttn", - up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), - layers_per_block: int = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - act_fn: str = "silu", - norm_num_groups: int = 32, - norm_eps: float = 1e-5, - cross_attention_dim: int = 1280, - attention_head_dim: Union[int, Tuple[int]] = 8, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - use_inflated_groupnorm=False, - # Additional - use_motion_module=False, - motion_module_resolutions=(1, 2, 4, 8), - motion_module_mid_block=False, - motion_module_decoder_only=False, - motion_module_type=None, - motion_module_kwargs={}, - unet_use_cross_frame_attention=False, - unet_use_temporal_attention=False, - add_audio_layer=False, - audio_condition_method: str = "cross_attn", - custom_audio_layer=False, - ): - super().__init__() - - self.sample_size = sample_size - time_embed_dim = block_out_channels[0] * 4 - self.use_motion_module = use_motion_module - self.add_audio_layer = add_audio_layer - - self.conv_in = zero_module(InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))) - - # time - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - - self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) - - # class embedding - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - else: - self.class_embedding = None - - self.down_blocks = nn.ModuleList([]) - self.mid_block = None - self.up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - res = 2**i - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block, - in_channels=input_channel, - out_channels=output_channel, - temb_channels=time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attention_head_dim[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module - and (res in motion_module_resolutions) - and (not motion_module_decoder_only), - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=custom_audio_layer, - ) - self.down_blocks.append(down_block) - - # mid - if mid_block_type == "UNetMidBlock3DCrossAttn": - self.mid_block = UNetMidBlock3DCrossAttn( - in_channels=block_out_channels[-1], - temb_channels=time_embed_dim, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - resnet_time_scale_shift=resnet_time_scale_shift, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attention_head_dim[-1], - resnet_groups=norm_num_groups, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module and motion_module_mid_block, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=custom_audio_layer, - ) - else: - raise ValueError(f"unknown mid_block_type : {mid_block_type}") - - # count how many layers upsample the videos - self.num_upsamplers = 0 - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_attention_head_dim = list(reversed(attention_head_dim)) - only_cross_attention = list(reversed(only_cross_attention)) - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - res = 2 ** (3 - i) - is_final_block = i == len(block_out_channels) - 1 - - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=layers_per_block + 1, - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=reversed_attention_head_dim[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module and (res in motion_module_resolutions), - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=custom_audio_layer, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - if use_inflated_groupnorm: - self.conv_norm_out = InflatedGroupNorm( - num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps - ) - else: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps - ) - self.conv_act = nn.SiLU() - - self.conv_out = zero_module(InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)) - - def set_attention_slice(self, slice_size): - r""" - Enable sliced attention computation. - - When this option is enabled, the attention module will split the input tensor in slices, to compute attention - in several steps. This is useful to save some memory in exchange for a small speed decrease. - - Args: - slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): - When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If - `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is - provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` - must be a multiple of `slice_size`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_slicable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_slicable_dims(module) - - num_slicable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_slicable_layers * [1] - - slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): - module.gradient_checkpointing = value - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - # support controlnet - down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - mid_block_additional_residual: Optional[torch.Tensor] = None, - return_dict: bool = True, - ) -> Union[UNet3DConditionOutput, Tuple]: - r""" - Args: - sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor - timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps - encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. - - Returns: - [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When - returning a tuple, the first element is the sample tensor. - """ - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - - if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): - logger.info("Forward upsample size to force interpolation output size.") - forward_upsample_size = True - - # prepare attention_mask - if attention_mask is not None: - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # time - timesteps = timestep - if not torch.is_tensor(timesteps): - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - - # timesteps does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=self.dtype) - emb = self.time_embedding(t_emb) - - if self.class_embedding is not None: - if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) - emb = emb + class_emb - - # pre-process - sample = self.conv_in(sample) - - # down - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - ) - else: - sample, res_samples = downsample_block( - hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states - ) - - down_block_res_samples += res_samples - - # support controlnet - down_block_res_samples = list(down_block_res_samples) - if down_block_additional_residuals is not None: - for i, down_block_additional_residual in enumerate(down_block_additional_residuals): - if down_block_additional_residual.dim() == 4: # boardcast - down_block_additional_residual = down_block_additional_residual.unsqueeze(2) - down_block_res_samples[i] = down_block_res_samples[i] + down_block_additional_residual - - # mid - sample = self.mid_block( - sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask - ) - - # support controlnet - if mid_block_additional_residual is not None: - if mid_block_additional_residual.dim() == 4: # boardcast - mid_block_additional_residual = mid_block_additional_residual.unsqueeze(2) - sample = sample + mid_block_additional_residual - - # up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - upsample_size=upsample_size, - attention_mask=attention_mask, - ) - else: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - upsample_size=upsample_size, - encoder_hidden_states=encoder_hidden_states, - ) - - # post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if not return_dict: - return (sample,) - - return UNet3DConditionOutput(sample=sample) - - def load_state_dict(self, state_dict, strict=True): - # If the loaded checkpoint's in_channels or out_channels are different from config - temp_state_dict = copy.deepcopy(state_dict) - if temp_state_dict["conv_in.weight"].shape[1] != self.config.in_channels: - del temp_state_dict["conv_in.weight"] - del temp_state_dict["conv_in.bias"] - if temp_state_dict["conv_out.weight"].shape[0] != self.config.out_channels: - del temp_state_dict["conv_out.weight"] - del temp_state_dict["conv_out.bias"] - - # If the loaded checkpoint's cross_attention_dim is different from config - keys_to_remove = [] - for key in temp_state_dict: - if "audio_cross_attn.attn.to_k." in key or "audio_cross_attn.attn.to_v." in key: - if temp_state_dict[key].shape[1] != self.config.cross_attention_dim: - keys_to_remove.append(key) - - for key in keys_to_remove: - del temp_state_dict[key] - - return super().load_state_dict(state_dict=temp_state_dict, strict=strict) - - @classmethod - def from_pretrained(cls, model_config: dict, ckpt_path: str, device="cpu"): - unet = cls.from_config(model_config).to(device) - if ckpt_path != "": - zero_rank_log(logger, f"Load from checkpoint: {ckpt_path}") - ckpt = torch.load(ckpt_path, map_location=device) - if "global_step" in ckpt: - zero_rank_log(logger, f"resume from global_step: {ckpt['global_step']}") - resume_global_step = ckpt["global_step"] - else: - resume_global_step = 0 - state_dict = ckpt["state_dict"] if "state_dict" in ckpt else ckpt - unet.load_state_dict(state_dict, strict=False) - else: - resume_global_step = 0 - - return unet, resume_global_step +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet.py + +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union +import copy + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models import ModelMixin + +from diffusers.utils import BaseOutput, logging +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from .unet_blocks import ( + CrossAttnDownBlock3D, + CrossAttnUpBlock3D, + DownBlock3D, + UNetMidBlock3DCrossAttn, + UpBlock3D, + get_down_block, + get_up_block, +) +from .resnet import InflatedConv3d, InflatedGroupNorm + +from ..utils.util import zero_rank_log +from .utils import zero_module + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + use_inflated_groupnorm=False, + # Additional + use_motion_module=False, + motion_module_resolutions=(1, 2, 4, 8), + motion_module_mid_block=False, + motion_module_decoder_only=False, + motion_module_type=None, + motion_module_kwargs={}, + add_audio_layer=False, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + self.use_motion_module = use_motion_module + self.add_audio_layer = add_audio_layer + + self.conv_in = zero_module(InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2**i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and motion_module_mid_block, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + self.conv_act = nn.SiLU() + + self.conv_out = zero_module(InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor = None, + class_labels: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + # support controlnet + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + + # down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states + ) + + down_block_res_samples += res_samples + + # support controlnet + down_block_res_samples = list(down_block_res_samples) + if down_block_additional_residuals is not None: + for i, down_block_additional_residual in enumerate(down_block_additional_residuals): + if down_block_additional_residual.dim() == 4: # boardcast + down_block_additional_residual = down_block_additional_residual.unsqueeze(2) + down_block_res_samples[i] = down_block_res_samples[i] + down_block_additional_residual + + # mid + sample = self.mid_block( + sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + # support controlnet + if mid_block_additional_residual is not None: + if mid_block_additional_residual.dim() == 4: # boardcast + mid_block_additional_residual = mid_block_additional_residual.unsqueeze(2) + sample = sample + mid_block_additional_residual + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + encoder_hidden_states=encoder_hidden_states, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + def load_state_dict(self, state_dict, strict=True): + # If the loaded checkpoint's in_channels or out_channels are different from config + if state_dict["conv_in.weight"].shape[1] != self.config.in_channels: + del state_dict["conv_in.weight"] + del state_dict["conv_in.bias"] + if state_dict["conv_out.weight"].shape[0] != self.config.out_channels: + del state_dict["conv_out.weight"] + del state_dict["conv_out.bias"] + + # If the loaded checkpoint's cross_attention_dim is different from config + keys_to_remove = [] + for key in state_dict: + if "attn2.to_k." in key or "attn2.to_v." in key: + if state_dict[key].shape[1] != self.config.cross_attention_dim: + keys_to_remove.append(key) + + for key in keys_to_remove: + del state_dict[key] + + return super().load_state_dict(state_dict=state_dict, strict=strict) + + @classmethod + def from_pretrained(cls, model_config: dict, ckpt_path: str, device="cpu"): + unet = cls.from_config(model_config).to(device) + if ckpt_path != "": + zero_rank_log(logger, f"Load from checkpoint: {ckpt_path}") + ckpt = torch.load(ckpt_path, map_location=device, weights_only=True) + if "global_step" in ckpt: + zero_rank_log(logger, f"resume from global_step: {ckpt['global_step']}") + resume_global_step = ckpt["global_step"] + else: + resume_global_step = 0 + unet.load_state_dict(ckpt["state_dict"], strict=False) + + del ckpt + torch.cuda.empty_cache() + else: + resume_global_step = 0 + + return unet, resume_global_step diff --git a/latentsync/models/unet_blocks.py b/latentsync/models/unet_blocks.py index cc913a0..26557a7 100644 --- a/latentsync/models/unet_blocks.py +++ b/latentsync/models/unet_blocks.py @@ -1,903 +1,777 @@ -# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py - -import torch -from torch import nn - -from .attention import Transformer3DModel -from .resnet import Downsample3D, ResnetBlock3D, Upsample3D -from .motion_module import get_motion_module - - -def get_down_block( - down_block_type, - num_layers, - in_channels, - out_channels, - temb_channels, - add_downsample, - resnet_eps, - resnet_act_fn, - attn_num_head_channels, - resnet_groups=None, - cross_attention_dim=None, - downsample_padding=None, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - resnet_time_scale_shift="default", - unet_use_cross_frame_attention=False, - unet_use_temporal_attention=False, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - add_audio_layer=False, - audio_condition_method="cross_attn", - custom_audio_layer=False, -): - down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type - if down_block_type == "DownBlock3D": - return DownBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - elif down_block_type == "CrossAttnDownBlock3D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") - return CrossAttnDownBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attn_num_head_channels, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=custom_audio_layer, - ) - raise ValueError(f"{down_block_type} does not exist.") - - -def get_up_block( - up_block_type, - num_layers, - in_channels, - out_channels, - prev_output_channel, - temb_channels, - add_upsample, - resnet_eps, - resnet_act_fn, - attn_num_head_channels, - resnet_groups=None, - cross_attention_dim=None, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - resnet_time_scale_shift="default", - unet_use_cross_frame_attention=False, - unet_use_temporal_attention=False, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - add_audio_layer=False, - audio_condition_method="cross_attn", - custom_audio_layer=False, -): - up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type - if up_block_type == "UpBlock3D": - return UpBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - elif up_block_type == "CrossAttnUpBlock3D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") - return CrossAttnUpBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attn_num_head_channels, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - use_inflated_groupnorm=use_inflated_groupnorm, - use_motion_module=use_motion_module, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=custom_audio_layer, - ) - raise ValueError(f"{up_block_type} does not exist.") - - -class UNetMidBlock3DCrossAttn(nn.Module): - def __init__( - self, - in_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - output_scale_factor=1.0, - cross_attention_dim=1280, - dual_cross_attention=False, - use_linear_projection=False, - upcast_attention=False, - unet_use_cross_frame_attention=False, - unet_use_temporal_attention=False, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - add_audio_layer=False, - audio_condition_method="cross_attn", - custom_audio_layer: bool = False, - ): - super().__init__() - - self.has_cross_attention = True - self.attn_num_head_channels = attn_num_head_channels - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - - # there is always at least one resnet - resnets = [ - ResnetBlock3D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_inflated_groupnorm=use_inflated_groupnorm, - ) - ] - attentions = [] - audio_attentions = [] - motion_modules = [] - - for _ in range(num_layers): - if dual_cross_attention: - raise NotImplementedError - attentions.append( - Transformer3DModel( - attn_num_head_channels, - in_channels // attn_num_head_channels, - in_channels=in_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - ) - ) - audio_attentions.append( - Transformer3DModel( - attn_num_head_channels, - in_channels // attn_num_head_channels, - in_channels=in_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=True, - ) - if custom_audio_layer - else None - ) - motion_modules.append( - get_motion_module( - in_channels=in_channels, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - if use_motion_module - else None - ) - resnets.append( - ResnetBlock3D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_inflated_groupnorm=use_inflated_groupnorm, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.audio_attentions = nn.ModuleList(audio_attentions) - self.resnets = nn.ModuleList(resnets) - self.motion_modules = nn.ModuleList(motion_modules) - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): - hidden_states = self.resnets[0](hidden_states, temb) - for attn, audio_attn, resnet, motion_module in zip( - self.attentions, self.audio_attentions, self.resnets[1:], self.motion_modules - ): - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - hidden_states = ( - audio_attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - if audio_attn is not None - else hidden_states - ) - hidden_states = ( - motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) - if motion_module is not None - else hidden_states - ) - hidden_states = resnet(hidden_states, temb) - - return hidden_states - - -class CrossAttnDownBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - cross_attention_dim=1280, - output_scale_factor=1.0, - downsample_padding=1, - add_downsample=True, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - unet_use_cross_frame_attention=False, - unet_use_temporal_attention=False, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - add_audio_layer=False, - audio_condition_method="cross_attn", - custom_audio_layer: bool = False, - ): - super().__init__() - resnets = [] - attentions = [] - audio_attentions = [] - motion_modules = [] - - self.has_cross_attention = True - self.attn_num_head_channels = attn_num_head_channels - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock3D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_inflated_groupnorm=use_inflated_groupnorm, - ) - ) - if dual_cross_attention: - raise NotImplementedError - attentions.append( - Transformer3DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - ) - ) - audio_attentions.append( - Transformer3DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=True, - ) - if custom_audio_layer - else None - ) - motion_modules.append( - get_motion_module( - in_channels=out_channels, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - if use_motion_module - else None - ) - - self.attentions = nn.ModuleList(attentions) - self.audio_attentions = nn.ModuleList(audio_attentions) - self.resnets = nn.ModuleList(resnets) - self.motion_modules = nn.ModuleList(motion_modules) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample3D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): - output_states = () - - for resnet, attn, audio_attn, motion_module in zip( - self.resnets, self.attentions, self.audio_attentions, self.motion_modules - ): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), - hidden_states, - encoder_hidden_states, - )[0] - if motion_module is not None: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(motion_module), - hidden_states.requires_grad_(), - temb, - encoder_hidden_states, - ) - - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - - hidden_states = ( - audio_attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - if audio_attn is not None - else hidden_states - ) - - # add motion module - hidden_states = ( - motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) - if motion_module is not None - else hidden_states - ) - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -class DownBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor=1.0, - add_downsample=True, - downsample_padding=1, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - ): - super().__init__() - resnets = [] - motion_modules = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock3D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_inflated_groupnorm=use_inflated_groupnorm, - ) - ) - motion_modules.append( - get_motion_module( - in_channels=out_channels, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - if use_motion_module - else None - ) - - self.resnets = nn.ModuleList(resnets) - self.motion_modules = nn.ModuleList(motion_modules) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample3D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None): - output_states = () - - for resnet, motion_module in zip(self.resnets, self.motion_modules): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - if motion_module is not None: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(motion_module), - hidden_states.requires_grad_(), - temb, - encoder_hidden_states, - ) - else: - hidden_states = resnet(hidden_states, temb) - - # add motion module - hidden_states = ( - motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) - if motion_module is not None - else hidden_states - ) - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -class CrossAttnUpBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - cross_attention_dim=1280, - output_scale_factor=1.0, - add_upsample=True, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - unet_use_cross_frame_attention=False, - unet_use_temporal_attention=False, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - add_audio_layer=False, - audio_condition_method="cross_attn", - custom_audio_layer=False, - ): - super().__init__() - resnets = [] - attentions = [] - audio_attentions = [] - motion_modules = [] - - self.has_cross_attention = True - self.attn_num_head_channels = attn_num_head_channels - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock3D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_inflated_groupnorm=use_inflated_groupnorm, - ) - ) - if dual_cross_attention: - raise NotImplementedError - attentions.append( - Transformer3DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - ) - ) - audio_attentions.append( - Transformer3DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - use_motion_module=use_motion_module, - unet_use_cross_frame_attention=unet_use_cross_frame_attention, - unet_use_temporal_attention=unet_use_temporal_attention, - add_audio_layer=add_audio_layer, - audio_condition_method=audio_condition_method, - custom_audio_layer=True, - ) - if custom_audio_layer - else None - ) - motion_modules.append( - get_motion_module( - in_channels=out_channels, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - if use_motion_module - else None - ) - - self.attentions = nn.ModuleList(attentions) - self.audio_attentions = nn.ModuleList(audio_attentions) - self.resnets = nn.ModuleList(resnets) - self.motion_modules = nn.ModuleList(motion_modules) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - res_hidden_states_tuple, - temb=None, - encoder_hidden_states=None, - upsample_size=None, - attention_mask=None, - ): - for resnet, attn, audio_attn, motion_module in zip( - self.resnets, self.attentions, self.audio_attentions, self.motion_modules - ): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), - hidden_states, - encoder_hidden_states, - )[0] - if motion_module is not None: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(motion_module), - hidden_states.requires_grad_(), - temb, - encoder_hidden_states, - ) - - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - hidden_states = ( - audio_attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - if audio_attn is not None - else hidden_states - ) - - # add motion module - hidden_states = ( - motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) - if motion_module is not None - else hidden_states - ) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -class UpBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor=1.0, - add_upsample=True, - use_inflated_groupnorm=False, - use_motion_module=None, - motion_module_type=None, - motion_module_kwargs=None, - ): - super().__init__() - resnets = [] - motion_modules = [] - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock3D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - use_inflated_groupnorm=use_inflated_groupnorm, - ) - ) - motion_modules.append( - get_motion_module( - in_channels=out_channels, - motion_module_type=motion_module_type, - motion_module_kwargs=motion_module_kwargs, - ) - if use_motion_module - else None - ) - - self.resnets = nn.ModuleList(resnets) - self.motion_modules = nn.ModuleList(motion_modules) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - res_hidden_states_tuple, - temb=None, - upsample_size=None, - encoder_hidden_states=None, - ): - for resnet, motion_module in zip(self.resnets, self.motion_modules): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - if motion_module is not None: - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(motion_module), - hidden_states.requires_grad_(), - temb, - encoder_hidden_states, - ) - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = ( - motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) - if motion_module is not None - else hidden_states - ) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py + +import torch +from torch import nn + +from .attention import Transformer3DModel +from .resnet import Downsample3D, ResnetBlock3D, Upsample3D +from .motion_module import get_motion_module + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, +): + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, +): + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ] + attentions = [] + motion_modules = [] + + for _ in range(num_layers): + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + add_audio_layer=add_audio_layer, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=in_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet, motion_module in zip(self.attentions, self.resnets[1:], self.motion_modules): + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + return_dict=False, + )[0] + + if motion_module is not None: + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + add_audio_layer=add_audio_layer, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + output_states = () + + for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules): + if torch.is_grad_enabled() and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + use_reentrant=False, + )[0] + + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + encoder_hidden_states, + use_reentrant=False, + ) + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + if motion_module is not None: + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None): + output_states = () + + for resnet, motion_module in zip(self.resnets, self.motion_modules): + if torch.is_grad_enabled() and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + encoder_hidden_states, + use_reentrant=False, + ) + else: + hidden_states = resnet(hidden_states, temb) + + if motion_module is not None: + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + add_audio_layer=add_audio_layer, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + attention_mask=None, + ): + for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if torch.is_grad_enabled() and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + use_reentrant=False, + )[0] + + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + encoder_hidden_states, + use_reentrant=False, + ) + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + if motion_module is not None: + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + upsample_size=None, + encoder_hidden_states=None, + ): + for resnet, motion_module in zip(self.resnets, self.motion_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if torch.is_grad_enabled() and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, use_reentrant=False + ) + + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + encoder_hidden_states, + use_reentrant=False, + ) + else: + hidden_states = resnet(hidden_states, temb) + + if motion_module is not None: + hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/latentsync/models/utils.py b/latentsync/models/utils.py index b96595c..0ef6518 100644 --- a/latentsync/models/utils.py +++ b/latentsync/models/utils.py @@ -1,19 +1,19 @@ -# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -def zero_module(module): - # Zero out the parameters of a module and return it. - for p in module.parameters(): - p.detach().zero_() - return module +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +def zero_module(module): + # Zero out the parameters of a module and return it. + for p in module.parameters(): + p.detach().zero_() + return module diff --git a/latentsync/models/wav2lip_syncnet.py b/latentsync/models/wav2lip_syncnet.py new file mode 100644 index 0000000..7fcf0bc --- /dev/null +++ b/latentsync/models/wav2lip_syncnet.py @@ -0,0 +1,90 @@ +# Adapted from https://github.com/primepake/wav2lip_288x288/blob/master/models/syncnetv2.py +# The code here is for ablation study. + +from torch import nn +from torch.nn import functional as F + + +class Wav2LipSyncNet(nn.Module): + def __init__(self, act_fn="leaky"): + super().__init__() + + # input image sequences: (15, 128, 256) + self.visual_encoder = nn.Sequential( + Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3, act_fn=act_fn), # (128, 256) + Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1, act_fn=act_fn), # (126, 127) + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 128, kernel_size=3, stride=2, padding=1, act_fn=act_fn), # (63, 64) + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 256, kernel_size=3, stride=3, padding=1, act_fn=act_fn), # (21, 22) + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 512, kernel_size=3, stride=2, padding=1, act_fn=act_fn), # (11, 11) + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, act_fn=act_fn), # (6, 6) + Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1, act_fn="relu"), # (3, 3) + Conv2d(1024, 1024, kernel_size=3, stride=1, padding=0, act_fn="relu"), # (1, 1) + Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0, act_fn="relu"), + ) + + # input audio sequences: (1, 80, 16) + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1, act_fn=act_fn), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1, act_fn=act_fn), # (27, 16) + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 128, kernel_size=3, stride=3, padding=1, act_fn=act_fn), # (9, 6) + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1, act_fn=act_fn), # (3, 3) + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 512, kernel_size=3, stride=1, padding=1, act_fn=act_fn), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 1024, kernel_size=3, stride=1, padding=0, act_fn="relu"), # (1, 1) + Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0, act_fn="relu"), + ) + + def forward(self, image_sequences, audio_sequences): + vision_embeds = self.visual_encoder(image_sequences) # (b, c, 1, 1) + audio_embeds = self.audio_encoder(audio_sequences) # (b, c, 1, 1) + + vision_embeds = vision_embeds.reshape(vision_embeds.shape[0], -1) # (b, c) + audio_embeds = audio_embeds.reshape(audio_embeds.shape[0], -1) # (b, c) + + # Make them unit vectors + vision_embeds = F.normalize(vision_embeds, p=2, dim=1) + audio_embeds = F.normalize(audio_embeds, p=2, dim=1) + + return vision_embeds, audio_embeds + + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, act_fn="relu", *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential(nn.Conv2d(cin, cout, kernel_size, stride, padding), nn.BatchNorm2d(cout)) + if act_fn == "relu": + self.act_fn = nn.ReLU() + elif act_fn == "tanh": + self.act_fn = nn.Tanh() + elif act_fn == "silu": + self.act_fn = nn.SiLU() + elif act_fn == "leaky": + self.act_fn = nn.LeakyReLU(0.2, inplace=True) + + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out += x + return self.act_fn(out) diff --git a/latentsync/pipelines/lipsync_pipeline.py b/latentsync/pipelines/lipsync_pipeline.py index 5ad4b0b..446f2ae 100644 --- a/latentsync/pipelines/lipsync_pipeline.py +++ b/latentsync/pipelines/lipsync_pipeline.py @@ -1,522 +1,465 @@ -# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/pipelines/pipeline_animation.py - -import inspect -import os -import shutil -from typing import Callable, List, Optional, Union -from dataclasses import dataclass -import subprocess - -import numpy as np -import torch -import torchvision - -from diffusers.utils import is_accelerate_available -from packaging import version -from transformers import AutoProcessor, Wav2Vec2Model - -from diffusers.configuration_utils import FrozenDict -from diffusers.models import AutoencoderKL -from diffusers.pipelines.pipeline_utils import DiffusionPipeline -from diffusers.schedulers import ( - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - LMSDiscreteScheduler, - PNDMScheduler, -) -from diffusers.utils import deprecate, logging - -from einops import rearrange - -from ..models.unet import UNet3DConditionModel -from ..utils.image_processor import ImageProcessor -from ..utils.util import read_video, read_audio, write_video -import tqdm -import soundfile as sf - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -class LipsyncPipeline(DiffusionPipeline): - _optional_components = [] - - def __init__( - self, - vae: AutoencoderKL, - audio_processor: AutoProcessor, - audio_encoder: Wav2Vec2Model, - unet: UNet3DConditionModel, - scheduler: Union[ - DDIMScheduler, - PNDMScheduler, - LMSDiscreteScheduler, - EulerDiscreteScheduler, - EulerAncestralDiscreteScheduler, - DPMSolverMultistepScheduler, - ], - ): - super().__init__() - - if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" - f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " - "to update the config accordingly as leaving `steps_offset` might led to incorrect results" - " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," - " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" - " file" - ) - deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["steps_offset"] = 1 - scheduler._internal_dict = FrozenDict(new_config) - - if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." - " `clip_sample` should be set to False in the configuration file. Please make sure to update the" - " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" - " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" - " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" - ) - deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["clip_sample"] = False - scheduler._internal_dict = FrozenDict(new_config) - - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - - self.register_modules( - vae=vae, - audio_processor=audio_processor, - audio_encoder=audio_encoder, - unet=unet, - scheduler=scheduler, - ) - - self.latent_space = vae is not None - if self.latent_space: - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - - self.set_progress_bar_config(desc="Steps") - - def enable_vae_slicing(self): - self.vae.enable_slicing() - - def disable_vae_slicing(self): - self.vae.disable_slicing() - - def enable_sequential_cpu_offload(self, gpu_id=0): - if is_accelerate_available(): - from accelerate import cpu_offload - else: - raise ImportError("Please install accelerate via `pip install accelerate`") - - device = torch.device(f"cuda:{gpu_id}") - - for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: - if cpu_offloaded_model is not None: - cpu_offload(cpu_offloaded_model, device) - - @property - def _execution_device(self): - if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): - return self.device - for module in self.unet.modules(): - if ( - hasattr(module, "_hf_hook") - and hasattr(module._hf_hook, "execution_device") - and module._hf_hook.execution_device is not None - ): - return torch.device(module._hf_hook.execution_device) - return self.device - - def decode_latents(self, latents): - if self.latent_space: - latents = latents / self.vae.config.scaling_factor + self.vae.config.shift_factor - latents = rearrange(latents, "b c f h w -> (b f) c h w") - decoded_latents = self.vae.decode(latents).sample - else: - decoded_latents = rearrange(latents, "b c f h w -> (b f) c h w") - return decoded_latents - - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs(self, height, width, callback_steps): - assert height == width, "Height and width must be equal" - - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - def prepare_latents(self, batch_size, num_frames, num_channels_latents, height, width, dtype, device, generator): - if self.latent_space: - shape = ( - batch_size, - num_channels_latents, - 1, - height // self.vae_scale_factor, - width // self.vae_scale_factor, - ) - else: - shape = ( - batch_size, - num_channels_latents, - 1, - height, - width, - ) - rand_device = "cpu" if device.type == "mps" else device - latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) - latents = latents.repeat(1, 1, num_frames, 1, 1) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - def prepare_mask_latents( - self, mask, masked_image, height, width, dtype, device, generator, do_classifier_free_guidance - ): - if self.latent_space: - # resize the mask to latents shape as we concatenate the mask to the latents - # we do that before converting to dtype to avoid breaking in case we're using cpu_offload - # and half precision - mask = torch.nn.functional.interpolate( - mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) - ) - masked_image = masked_image.to(device=device, dtype=dtype) - - # encode the mask image into latents space so we can concatenate it to the latents - masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) - masked_image_latents = ( - masked_image_latents - self.vae.config.shift_factor - ) * self.vae.config.scaling_factor - else: - masked_image_latents = masked_image.to(device=device, dtype=dtype) - - # aligning device to prevent device errors when concating it with the latent model input - masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) - mask = mask.to(device=device, dtype=dtype) - - # assume batch size = 1 - mask = rearrange(mask, "f c h w -> 1 c f h w") - masked_image_latents = rearrange(masked_image_latents, "f c h w -> 1 c f h w") - - mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask - masked_image_latents = ( - torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents - ) - return mask, masked_image_latents - - def prepare_image_latents(self, images, device, dtype, generator, do_classifier_free_guidance): - images = images.to(device=device, dtype=dtype) - if self.latent_space: - image_latents = self.vae.encode(images).latent_dist.sample(generator=generator) - image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor - else: - image_latents = images - image_latents = rearrange(image_latents, "f c h w -> 1 c f h w") - image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents - - return image_latents - - def set_progress_bar_config(self, **kwargs): - if not hasattr(self, "_progress_bar_config"): - self._progress_bar_config = {} - self._progress_bar_config.update(kwargs) - - @staticmethod - def recover_original_pixel_values(decoded_latents, pixel_values, masks, device, weight_dtype): - # Combine the pixel values - pixel_values = pixel_values.to(device=device, dtype=weight_dtype) - masks = masks.to(device=device, dtype=weight_dtype) - combined_pixel_values = decoded_latents * masks + pixel_values * (1 - masks) - return combined_pixel_values - - @staticmethod - def pixel_values_to_images(pixel_values: torch.Tensor): - pixel_values = rearrange(pixel_values, "f c h w -> f h w c") - pixel_values = (pixel_values / 2 + 0.5).clamp(0, 1) - images = (pixel_values * 255).to(torch.uint8) - images = images.cpu().numpy() - return images - - def crop_audio_window(self, original_mel, start_index): - start_idx = int(80.0 * (start_index / float(self.video_fps))) - end_idx = start_idx + self.mel_window_length - return original_mel[:, start_idx:end_idx].unsqueeze(0) - - def affine_transform_video(self, video_path): - video_frames = read_video(video_path, use_decord=False) - faces = [] - boxes = [] - affine_matrices = [] - print(f"Affine transforming {len(video_frames)} faces...") - for frame in tqdm.tqdm(video_frames): - face, box, affine_matrix = self.image_processor.affine_transform(frame) - faces.append(face) - boxes.append(box) - affine_matrices.append(affine_matrix) - - faces = torch.stack(faces) - return faces, video_frames, boxes, affine_matrices - - def restore_video(self, faces, video_frames, boxes, affine_matrices): - video_frames = video_frames[: faces.shape[0]] - out_frames = [] - for index, face in enumerate(faces): - x1, y1, x2, y2 = boxes[index] - height = int(y2 - y1) - width = int(x2 - x1) - face = torchvision.transforms.functional.resize(face, size=(height, width), antialias=True) - face = rearrange(face, "c h w -> h w c") - face = (face / 2 + 0.5).clamp(0, 1) - face = (face * 255).to(torch.uint8).cpu().numpy() - out_frame = self.image_processor.restorer.restore_img(video_frames[index], face, affine_matrices[index]) - out_frames.append(out_frame) - return np.stack(out_frames, axis=0) - - @torch.no_grad() - def __call__( - self, - video_path: str, - audio_path: str, - video_out_path: str, - video_mask_path: str = None, - num_frames: int = 16, - video_fps: int = 25, - audio_sample_rate: int = 16000, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - weight_dtype: Optional[torch.dtype] = torch.float16, - eta: float = 0.0, - mask: str = "fix_mask", - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: Optional[int] = 1, - **kwargs, - ): - is_train = self.unet.training - self.unet.eval() - - # 0. Define call parameters - batch_size = 1 - device = self._execution_device - self.image_processor = ImageProcessor(height, mask=mask, device="cuda") - self.set_progress_bar_config(desc=f"Sample frames: {num_frames}") - - video_frames, original_video_frames, boxes, affine_matrices = self.affine_transform_video(video_path) - audio_samples = read_audio(audio_path) - - # 1. Default height and width to unet - if self.latent_space: - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - - # 2. Check inputs - self.check_inputs(height, width, callback_steps) - - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - do_classifier_free_guidance = guidance_scale > 1.0 - - # 3. set timesteps - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps = self.scheduler.timesteps - - # 4. Prepare extra step kwargs. - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - self.video_fps = video_fps - - if self.unet.add_audio_layer: - whisper_feature = self.audio_encoder.audio2feat(audio_path) - whisper_chunks = self.audio_encoder.feature2chunks(feature_array=whisper_feature, fps=video_fps) - - num_inferences = min(len(video_frames), len(whisper_chunks)) // num_frames - else: - num_inferences = len(video_frames) // num_frames - - synced_video_frames = [] - masked_video_frames = [] - - save_affine_faces = False - if save_affine_faces: - pixel_values_faces = [] - masked_pixel_values_faces = [] - - # Prepare latent variables - if self.latent_space: - num_channels_latents = self.vae.config.latent_channels - else: - num_channels_latents = 3 - - all_latents = self.prepare_latents( - batch_size, - num_frames * num_inferences, - num_channels_latents, - height, - width, - weight_dtype, - device, - generator, - ) - - for i in tqdm.tqdm(range(num_inferences), desc="Doing inference..."): - if self.unet.add_audio_layer: - # mel_overlap = torch.stack(mel_overlap_list[i * num_frames : (i + 1) * num_frames]) - # mel_overlap = mel_overlap.unsqueeze(0).to(device, dtype=weight_dtype) - mel_overlap = torch.stack(whisper_chunks[i * num_frames : (i + 1) * num_frames]) - mel_overlap = mel_overlap.to(device, dtype=weight_dtype) - if do_classifier_free_guidance: - empty_mel_overlap = torch.zeros_like(mel_overlap) - mel_overlap = torch.cat([empty_mel_overlap, mel_overlap]) - else: - mel_overlap = None - inference_video_frames = video_frames[i * num_frames : (i + 1) * num_frames] - latents = all_latents[:, :, i * num_frames : (i + 1) * num_frames] - pixel_values, masked_pixel_values, masks = self.image_processor.prepare_masks_and_masked_images( - inference_video_frames, affine_transform=False - ) - - if save_affine_faces: - pixel_values_faces.append(pixel_values) - masked_pixel_values_faces.append(masked_pixel_values) - - # 7. Prepare mask latent variables - mask_latents, masked_image_latents = self.prepare_mask_latents( - masks, - masked_pixel_values, - height, - width, - weight_dtype, - device, - generator, - do_classifier_free_guidance, - ) - - # 8. Prepare image latents - image_latents = self.prepare_image_latents( - pixel_values, - device, - weight_dtype, - generator, - do_classifier_free_guidance, - ) - - # 9. Denoising loop - num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order - with self.progress_bar(total=num_inference_steps) as progress_bar: - for j, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - - # concat latents, mask, masked_image_latents in the channel dimension - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - latent_model_input = torch.cat( - [latent_model_input, mask_latents, masked_image_latents, image_latents], dim=1 - ) - - # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=mel_overlap).sample - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - # call the callback, if provided - if j == len(timesteps) - 1 or ( - (j + 1) > num_warmup_steps and (j + 1) % self.scheduler.order == 0 - ): - progress_bar.update() - if callback is not None and j % callback_steps == 0: - callback(j, t, latents) - - # Recover the pixel values - decoded_latents = self.decode_latents(latents) - decoded_latents = self.recover_original_pixel_values( - decoded_latents, pixel_values, 1 - masks, device, weight_dtype - ) - synced_video_frames.append(decoded_latents) - masked_video_frames.append(masked_pixel_values) - - synced_video_frames = self.restore_video( - torch.cat(synced_video_frames), original_video_frames, boxes, affine_matrices - ) - masked_video_frames = self.restore_video( - torch.cat(masked_video_frames), original_video_frames, boxes, affine_matrices - ) - - audio_samples_remain_length = int(synced_video_frames.shape[0] / video_fps * audio_sample_rate) - audio_samples = audio_samples[:audio_samples_remain_length].cpu().numpy() - - if is_train: - self.unet.train() - - temp_dir = "temp" - if os.path.exists(temp_dir): - shutil.rmtree(temp_dir) - os.makedirs(temp_dir, exist_ok=True) - - if save_affine_faces: - pixel_values_faces = torch.cat(pixel_values_faces) - masked_pixel_values_faces = torch.cat(masked_pixel_values_faces) - - pixel_values_faces = self.pixel_values_to_images(pixel_values_faces) - masked_pixel_values_faces = self.pixel_values_to_images(masked_pixel_values_faces) - - write_video("affine_faces.mp4", pixel_values_faces, fps=25) - write_video("masked_affine_faces.mp4", masked_pixel_values_faces, fps=25) - - write_video(os.path.join(temp_dir, "video.mp4"), synced_video_frames, fps=25) - # write_video(video_mask_path, masked_video_frames, fps=25) - - sf.write(os.path.join(temp_dir, "audio.wav"), audio_samples, audio_sample_rate) - - command = f"ffmpeg -y -loglevel error -nostdin -i {os.path.join(temp_dir, 'video.mp4')} -i {os.path.join(temp_dir, 'audio.wav')} -c:v libx264 -c:a aac -q:v 0 -q:a 0 {video_out_path}" - subprocess.run(command, shell=True) +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/pipelines/pipeline_animation.py + +import inspect +import os +import shutil +from typing import Callable, List, Optional, Union +import subprocess + +import numpy as np +import torch +import torchvision + +from packaging import version + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL +from diffusers.pipelines import DiffusionPipeline +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import deprecate, logging + +from einops import rearrange +import cv2 + +from ..models.unet import UNet3DConditionModel +from ..utils.util import read_video, read_audio, write_video, check_ffmpeg_installed +from ..utils.image_processor import ImageProcessor, load_fixed_mask +from ..whisper.audio2feature import Audio2Feature +import tqdm +import soundfile as sf + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class LipsyncPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae: AutoencoderKL, + audio_encoder: Audio2Feature, + denoising_unet: UNet3DConditionModel, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + is_unet_version_less_0_9_0 = hasattr(denoising_unet.config, "_diffusers_version") and version.parse( + version.parse(denoising_unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = ( + hasattr(denoising_unet.config, "sample_size") and denoising_unet.config.sample_size < 64 + ) + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(denoising_unet.config) + new_config["sample_size"] = 64 + denoising_unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + audio_encoder=audio_encoder, + denoising_unet=denoising_unet, + scheduler=scheduler, + ) + + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + + self.set_progress_bar_config(desc="Steps") + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.denoising_unet, "_hf_hook"): + return self.device + for module in self.denoising_unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def decode_latents(self, latents): + latents = latents / self.vae.config.scaling_factor + self.vae.config.shift_factor + latents = rearrange(latents, "b c f h w -> (b f) c h w") + decoded_latents = self.vae.decode(latents).sample + return decoded_latents + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs(self, height, width, callback_steps): + assert height == width, "Height and width must be equal" + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + def prepare_latents(self, batch_size, num_frames, num_channels_latents, height, width, dtype, device, generator): + shape = ( + batch_size, + num_channels_latents, + 1, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + rand_device = "cpu" if device.type == "mps" else device + latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) + latents = latents.repeat(1, 1, num_frames, 1, 1) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_mask_latents( + self, mask, masked_image, height, width, dtype, device, generator, do_classifier_free_guidance + ): + # resize the mask to latents shape as we concatenate the mask to the latents + # we do that before converting to dtype to avoid breaking in case we're using cpu_offload + # and half precision + mask = torch.nn.functional.interpolate( + mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) + ) + masked_image = masked_image.to(device=device, dtype=dtype) + + # encode the mask image into latents space so we can concatenate it to the latents + masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) + masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor + + # aligning device to prevent device errors when concating it with the latent model input + masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) + mask = mask.to(device=device, dtype=dtype) + + # assume batch size = 1 + mask = rearrange(mask, "f c h w -> 1 c f h w") + masked_image_latents = rearrange(masked_image_latents, "f c h w -> 1 c f h w") + + mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask + masked_image_latents = ( + torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents + ) + return mask, masked_image_latents + + def prepare_image_latents(self, images, device, dtype, generator, do_classifier_free_guidance): + images = images.to(device=device, dtype=dtype) + image_latents = self.vae.encode(images).latent_dist.sample(generator=generator) + image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor + image_latents = rearrange(image_latents, "f c h w -> 1 c f h w") + image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents + + return image_latents + + def set_progress_bar_config(self, **kwargs): + if not hasattr(self, "_progress_bar_config"): + self._progress_bar_config = {} + self._progress_bar_config.update(kwargs) + + @staticmethod + def paste_surrounding_pixels_back(decoded_latents, pixel_values, masks, device, weight_dtype): + # Paste the surrounding pixels back, because we only want to change the mouth region + pixel_values = pixel_values.to(device=device, dtype=weight_dtype) + masks = masks.to(device=device, dtype=weight_dtype) + combined_pixel_values = decoded_latents * masks + pixel_values * (1 - masks) + return combined_pixel_values + + @staticmethod + def pixel_values_to_images(pixel_values: torch.Tensor): + pixel_values = rearrange(pixel_values, "f c h w -> f h w c") + pixel_values = (pixel_values / 2 + 0.5).clamp(0, 1) + images = (pixel_values * 255).to(torch.uint8) + images = images.cpu().numpy() + return images + + def affine_transform_video(self, video_frames: np.ndarray): + faces = [] + boxes = [] + affine_matrices = [] + print(f"Affine transforming {len(video_frames)} faces...") + for frame in tqdm.tqdm(video_frames): + face, box, affine_matrix = self.image_processor.affine_transform(frame) + faces.append(face) + boxes.append(box) + affine_matrices.append(affine_matrix) + + faces = torch.stack(faces) + return faces, boxes, affine_matrices + + def restore_video(self, faces, video_frames, boxes, affine_matrices): + video_frames = video_frames[: faces.shape[0]] + out_frames = [] + print(f"Restoring {len(faces)} faces...") + for index, face in enumerate(tqdm.tqdm(faces)): + x1, y1, x2, y2 = boxes[index] + height = int(y2 - y1) + width = int(x2 - x1) + face = torchvision.transforms.functional.resize(face, size=(height, width), antialias=True) + face = rearrange(face, "c h w -> h w c") + face = (face / 2 + 0.5).clamp(0, 1) + face = (face * 255).to(torch.uint8).cpu().numpy() + # face = cv2.resize(face, (width, height), interpolation=cv2.INTER_LANCZOS4) + out_frame = self.image_processor.restorer.restore_img(video_frames[index], face, affine_matrices[index]) + out_frames.append(out_frame) + return np.stack(out_frames, axis=0) + + @torch.no_grad() + def __call__( + self, + video_path: str, + audio_path: str, + video_out_path: str, + video_mask_path: str = None, + num_frames: int = 16, + video_fps: int = 25, + audio_sample_rate: int = 16000, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 20, + guidance_scale: float = 1.5, + weight_dtype: Optional[torch.dtype] = torch.float16, + eta: float = 0.0, + mask: str = "fix_mask", + mask_image_path: str = "latentsync/utils/mask.png", + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + **kwargs, + ): + is_train = self.denoising_unet.training + self.denoising_unet.eval() + + check_ffmpeg_installed() + + # 0. Define call parameters + batch_size = 1 + device = self._execution_device + mask_image = load_fixed_mask(height, mask_image_path) + self.image_processor = ImageProcessor(height, mask=mask, device="cuda", mask_image=mask_image) + self.set_progress_bar_config(desc=f"Sample frames: {num_frames}") + + # 1. Default height and width to unet + height = height or self.denoising_unet.config.sample_size * self.vae_scale_factor + width = width or self.denoising_unet.config.sample_size * self.vae_scale_factor + + # 2. Check inputs + self.check_inputs(height, width, callback_steps) + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 4. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + whisper_feature = self.audio_encoder.audio2feat(audio_path) + whisper_chunks = self.audio_encoder.feature2chunks(feature_array=whisper_feature, fps=video_fps) + + audio_samples = read_audio(audio_path) + video_frames = read_video(video_path, use_decord=False) + + num_inferences = min(len(video_frames), len(whisper_chunks)) // num_frames + video_frames = video_frames[: num_inferences * num_frames] + faces, boxes, affine_matrices = self.affine_transform_video(video_frames) + + synced_video_frames = [] + masked_video_frames = [] + + num_channels_latents = self.vae.config.latent_channels + + # Prepare latent variables + all_latents = self.prepare_latents( + batch_size, + num_frames * num_inferences, + num_channels_latents, + height, + width, + weight_dtype, + device, + generator, + ) + + for i in tqdm.tqdm(range(num_inferences), desc="Doing inference..."): + if self.denoising_unet.add_audio_layer: + audio_embeds = torch.stack(whisper_chunks[i * num_frames : (i + 1) * num_frames]) + audio_embeds = audio_embeds.to(device, dtype=weight_dtype) + if do_classifier_free_guidance: + null_audio_embeds = torch.zeros_like(audio_embeds) + audio_embeds = torch.cat([null_audio_embeds, audio_embeds]) + else: + audio_embeds = None + inference_faces = faces[i * num_frames : (i + 1) * num_frames] + latents = all_latents[:, :, i * num_frames : (i + 1) * num_frames] + ref_pixel_values, masked_pixel_values, masks = self.image_processor.prepare_masks_and_masked_images( + inference_faces, affine_transform=False + ) + + # 7. Prepare mask latent variables + mask_latents, masked_image_latents = self.prepare_mask_latents( + masks, + masked_pixel_values, + height, + width, + weight_dtype, + device, + generator, + do_classifier_free_guidance, + ) + + # 8. Prepare image latents + ref_latents = self.prepare_image_latents( + ref_pixel_values, + device, + weight_dtype, + generator, + do_classifier_free_guidance, + ) + + # 9. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for j, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + denoising_unet_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + + denoising_unet_input = self.scheduler.scale_model_input(denoising_unet_input, t) + + # concat latents, mask, masked_image_latents in the channel dimension + denoising_unet_input = torch.cat( + [denoising_unet_input, mask_latents, masked_image_latents, ref_latents], dim=1 + ) + + # predict the noise residual + noise_pred = self.denoising_unet( + denoising_unet_input, t, encoder_hidden_states=audio_embeds + ).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_audio = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_audio - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if j == len(timesteps) - 1 or ((j + 1) > num_warmup_steps and (j + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and j % callback_steps == 0: + callback(j, t, latents) + + # Recover the pixel values + decoded_latents = self.decode_latents(latents) + decoded_latents = self.paste_surrounding_pixels_back( + decoded_latents, ref_pixel_values, 1 - masks, device, weight_dtype + ) + synced_video_frames.append(decoded_latents) + # masked_video_frames.append(masked_pixel_values) + + synced_video_frames = self.restore_video( + torch.cat(synced_video_frames), video_frames, boxes, affine_matrices + ) + # masked_video_frames = self.restore_video( + # torch.cat(masked_video_frames), video_frames, boxes, affine_matrices + # ) + + audio_samples_remain_length = int(synced_video_frames.shape[0] / video_fps * audio_sample_rate) + audio_samples = audio_samples[:audio_samples_remain_length].cpu().numpy() + + if is_train: + self.denoising_unet.train() + + temp_dir = "temp" + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + os.makedirs(temp_dir, exist_ok=True) + + write_video(os.path.join(temp_dir, "video.mp4"), synced_video_frames, fps=25) + # write_video(video_mask_path, masked_video_frames, fps=25) + + sf.write(os.path.join(temp_dir, "audio.wav"), audio_samples, audio_sample_rate) + + command = f"ffmpeg -y -loglevel error -nostdin -i {os.path.join(temp_dir, 'video.mp4')} -i {os.path.join(temp_dir, 'audio.wav')} -c:v libx264 -c:a aac -q:v 0 -q:a 0 {video_out_path}" + subprocess.run(command, shell=True) diff --git a/latentsync/trepa/loss.py b/latentsync/trepa/loss.py new file mode 100644 index 0000000..41bd029 --- /dev/null +++ b/latentsync/trepa/loss.py @@ -0,0 +1,65 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +from einops import rearrange +from .third_party.VideoMAEv2.utils import load_videomae_model + + +class TREPALoss: + def __init__( + self, + device="cuda", + ckpt_path="checkpoints/auxiliary/vit_g_hybrid_pt_1200e_ssv2_ft.pth", + with_cp=False, + ): + self.model = load_videomae_model(device, ckpt_path, with_cp).eval().to(dtype=torch.float16) + self.model.requires_grad_(False) + + def __call__(self, videos_fake, videos_real): + batch_size = videos_fake.shape[0] + num_frames = videos_fake.shape[2] + videos_fake = rearrange(videos_fake.clone(), "b c f h w -> (b f) c h w") + videos_real = rearrange(videos_real.clone(), "b c f h w -> (b f) c h w") + + videos_fake = F.interpolate(videos_fake, size=(224, 224), mode="bilinear") + videos_real = F.interpolate(videos_real, size=(224, 224), mode="bilinear") + + videos_fake = rearrange(videos_fake, "(b f) c h w -> b c f h w", f=num_frames) + videos_real = rearrange(videos_real, "(b f) c h w -> b c f h w", f=num_frames) + + # Because input pixel range is [-1, 1], and model expects pixel range to be [0, 1] + videos_fake = (videos_fake / 2 + 0.5).clamp(0, 1) + videos_real = (videos_real / 2 + 0.5).clamp(0, 1) + + feats_fake = self.model.forward_features(videos_fake) + feats_real = self.model.forward_features(videos_real) + + feats_fake = F.normalize(feats_fake, p=2, dim=1) + feats_real = F.normalize(feats_real, p=2, dim=1) + + return F.mse_loss(feats_fake, feats_real) + + +if __name__ == "__main__": + torch.manual_seed(42) + + # input shape: (b, c, f, h, w) + videos_fake = torch.randn(2, 3, 16, 256, 256, requires_grad=True).to(device="cuda", dtype=torch.float16) + videos_real = torch.randn(2, 3, 16, 256, 256, requires_grad=True).to(device="cuda", dtype=torch.float16) + + trepa_loss = TREPALoss(device="cuda", with_cp=True) + loss = trepa_loss(videos_fake, videos_real) + print(loss) diff --git a/latentsync/trepa/third_party/VideoMAEv2/utils.py b/latentsync/trepa/third_party/VideoMAEv2/utils.py index 0506435..42c3167 100644 --- a/latentsync/trepa/third_party/VideoMAEv2/utils.py +++ b/latentsync/trepa/third_party/VideoMAEv2/utils.py @@ -1,81 +1,82 @@ -import os -import torch -import requests -from tqdm import tqdm -from torchvision import transforms -from .videomaev2_finetune import vit_giant_patch14_224 - -def to_normalized_float_tensor(vid): - return vid.permute(3, 0, 1, 2).to(torch.float32) / 255 - - -# NOTE: for those functions, which generally expect mini-batches, we keep them -# as non-minibatch so that they are applied as if they were 4d (thus image). -# this way, we only apply the transformation in the spatial domain -def resize(vid, size, interpolation='bilinear'): - # NOTE: using bilinear interpolation because we don't work on minibatches - # at this level - scale = None - if isinstance(size, int): - scale = float(size) / min(vid.shape[-2:]) - size = None - return torch.nn.functional.interpolate( - vid, - size=size, - scale_factor=scale, - mode=interpolation, - align_corners=False) - - -class ToFloatTensorInZeroOne(object): - def __call__(self, vid): - return to_normalized_float_tensor(vid) - - -class Resize(object): - def __init__(self, size): - self.size = size - def __call__(self, vid): - return resize(vid, self.size) - -def preprocess_videomae(videos): - transform = transforms.Compose( - [ToFloatTensorInZeroOne(), - Resize((224, 224))]) - return torch.stack([transform(f) for f in torch.from_numpy(videos)]) - - -def load_videomae_model(device, ckpt_path=None): - if ckpt_path is None: - current_dir = os.path.dirname(os.path.abspath(__file__)) - ckpt_path = os.path.join(current_dir, 'vit_g_hybrid_pt_1200e_ssv2_ft.pth') - - if not os.path.exists(ckpt_path): - # download the ckpt to the path - ckpt_url = 'https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/internvideo/videomaev2/vit_g_hybrid_pt_1200e_ssv2_ft.pth' - response = requests.get(ckpt_url, stream=True, allow_redirects=True) - total_size = int(response.headers.get("content-length", 0)) - block_size = 1024 - - with tqdm(total=total_size, unit="B", unit_scale=True) as progress_bar: - with open(ckpt_path, "wb") as fw: - for data in response.iter_content(block_size): - progress_bar.update(len(data)) - fw.write(data) - - model = vit_giant_patch14_224( - img_size=224, - pretrained=False, - num_classes=174, - all_frames=16, - tubelet_size=2, - drop_path_rate=0.3, - use_mean_pooling=True) - - ckpt = torch.load(ckpt_path, map_location='cpu') - for model_key in ['model', 'module']: - if model_key in ckpt: - ckpt = ckpt[model_key] - break - model.load_state_dict(ckpt) - return model.to(device) \ No newline at end of file +import os +import torch +import requests +from tqdm import tqdm +from torchvision import transforms +from .videomaev2_finetune import vit_giant_patch14_224 + + +def to_normalized_float_tensor(vid): + return vid.permute(3, 0, 1, 2).to(torch.float32) / 255 + + +# NOTE: for those functions, which generally expect mini-batches, we keep them +# as non-minibatch so that they are applied as if they were 4d (thus image). +# this way, we only apply the transformation in the spatial domain +def resize(vid, size, interpolation="bilinear"): + # NOTE: using bilinear interpolation because we don't work on minibatches + # at this level + scale = None + if isinstance(size, int): + scale = float(size) / min(vid.shape[-2:]) + size = None + return torch.nn.functional.interpolate(vid, size=size, scale_factor=scale, mode=interpolation, align_corners=False) + + +class ToFloatTensorInZeroOne(object): + def __call__(self, vid): + return to_normalized_float_tensor(vid) + + +class Resize(object): + def __init__(self, size): + self.size = size + + def __call__(self, vid): + return resize(vid, self.size) + + +def preprocess_videomae(videos): + transform = transforms.Compose([ToFloatTensorInZeroOne(), Resize((224, 224))]) + return torch.stack([transform(f) for f in torch.from_numpy(videos)]) + + +def load_videomae_model(device, ckpt_path=None, with_cp=False): + if ckpt_path is None: + current_dir = os.path.dirname(os.path.abspath(__file__)) + ckpt_path = os.path.join(current_dir, "vit_g_hybrid_pt_1200e_ssv2_ft.pth") + + if not os.path.exists(ckpt_path): + # download the ckpt to the path + ckpt_url = "https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/internvideo/videomaev2/vit_g_hybrid_pt_1200e_ssv2_ft.pth" + response = requests.get(ckpt_url, stream=True, allow_redirects=True) + total_size = int(response.headers.get("content-length", 0)) + block_size = 1024 + + with tqdm(total=total_size, unit="B", unit_scale=True) as progress_bar: + with open(ckpt_path, "wb") as fw: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + fw.write(data) + + model = vit_giant_patch14_224( + img_size=224, + pretrained=False, + num_classes=174, + all_frames=16, + tubelet_size=2, + drop_path_rate=0.3, + use_mean_pooling=True, + with_cp=with_cp, + ) + + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True) + for model_key in ["model", "module"]: + if model_key in ckpt: + ckpt = ckpt[model_key] + break + model.load_state_dict(ckpt) + + del ckpt + torch.cuda.empty_cache() + return model.to(device) diff --git a/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py index 0eb2c40..42bae38 100644 --- a/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py +++ b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py @@ -1,539 +1,543 @@ -# -------------------------------------------------------- -# Based on BEiT, timm, DINO and DeiT code bases -# https://github.com/microsoft/unilm/tree/master/beit -# https://github.com/rwightman/pytorch-image-models/tree/master/timm -# https://github.com/facebookresearch/deit -# https://github.com/facebookresearch/dino -# --------------------------------------------------------' -from functools import partial - -import math -import warnings -import numpy as np -import collections.abc -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as cp -from itertools import repeat - - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - # Cut & paste from PyTorch official master until it's in a few official releases - RW - # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn( - "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2, - ) - - with torch.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [l, u], then translate to - # [2l-1, 2u-1]. - tensor.uniform_(2 * l - 1, 2 * u - 1) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - tensor.erfinv_() - - # Transform to proper mean, std - tensor.mul_(std * math.sqrt(2.0)) - tensor.add_(mean) - - # Clamp to ensure it's in the proper range - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - Args: - tensor: an n-dimensional `torch.Tensor` - mean: the mean of the normal distribution - std: the standard deviation of the normal distribution - a: the minimum cutoff value - b: the maximum cutoff value - Examples: - >>> w = torch.empty(3, 5) - >>> nn.init.trunc_normal_(w) - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - - return parse - - -to_2tuple = _ntuple(2) - - -def drop_path(x, drop_prob: float = 0.0, training: bool = False): - """ - Adapted from timm codebase - """ - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -def _cfg(url="", **kwargs): - return { - "url": url, - "num_classes": 400, - "input_size": (3, 224, 224), - "pool_size": None, - "crop_pct": 0.9, - "interpolation": "bicubic", - "mean": (0.5, 0.5, 0.5), - "std": (0.5, 0.5, 0.5), - **kwargs, - } - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" - - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - def extra_repr(self) -> str: - return "p={}".format(self.drop_prob) - - -class Mlp(nn.Module): - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - # x = self.drop(x) - # commit this for the orignal BERT implement - x = self.fc2(x) - x = self.drop(x) - return x - - -class CosAttention(nn.Module): - - def __init__( - self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, attn_head_dim=None - ): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - if attn_head_dim is not None: - head_dim = attn_head_dim - all_head_dim = head_dim * self.num_heads - # self.scale = qk_scale or head_dim**-0.5 - # DO NOT RENAME [self.scale] (for no weight decay) - if qk_scale is None: - self.scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) - else: - self.scale = qk_scale - - self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) - if qkv_bias: - self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) - self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) - else: - self.q_bias = None - self.v_bias = None - - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(all_head_dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x): - B, N, C = x.shape - qkv_bias = None - if self.q_bias is not None: - qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) - qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) - qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) - - # torch.log(torch.tensor(1. / 0.01)) = 4.6052 - logit_scale = torch.clamp(self.scale, max=4.6052).exp() - - attn = attn * logit_scale - - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, -1) - - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Attention(nn.Module): - - def __init__( - self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, attn_head_dim=None - ): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - if attn_head_dim is not None: - head_dim = attn_head_dim - all_head_dim = head_dim * self.num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) - if qkv_bias: - self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) - self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) - else: - self.q_bias = None - self.v_bias = None - - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(all_head_dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x): - B, N, C = x.shape - qkv_bias = None - if self.q_bias is not None: - qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) - qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) - qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = q @ k.transpose(-2, -1) - - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, -1) - - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Block(nn.Module): - - def __init__( - self, - dim, - num_heads, - mlp_ratio=4.0, - qkv_bias=False, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - init_values=None, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - attn_head_dim=None, - cos_attn=False, - ): - super().__init__() - self.norm1 = norm_layer(dim) - if cos_attn: - self.attn = CosAttention( - dim, - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - attn_head_dim=attn_head_dim, - ) - else: - self.attn = Attention( - dim, - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - attn_head_dim=attn_head_dim, - ) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if init_values > 0: - self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) - self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) - else: - self.gamma_1, self.gamma_2 = None, None - - def forward(self, x): - if self.gamma_1 is None: - x = x + self.drop_path(self.attn(self.norm1(x))) - x = x + self.drop_path(self.mlp(self.norm2(x))) - else: - x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) - x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) - return x - - -class PatchEmbed(nn.Module): - """Image to Patch Embedding""" - - def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, num_frames=16, tubelet_size=2): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - num_spatial_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1]) - num_patches = num_spatial_patches * (num_frames // tubelet_size) - - self.img_size = img_size - self.tubelet_size = tubelet_size - self.patch_size = patch_size - self.num_patches = num_patches - self.proj = nn.Conv3d( - in_channels=in_chans, - out_channels=embed_dim, - kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), - stride=(self.tubelet_size, patch_size[0], patch_size[1]), - ) - - def forward(self, x, **kwargs): - B, C, T, H, W = x.shape - assert ( - H == self.img_size[0] and W == self.img_size[1] - ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." - # b, c, l -> b, l, c - # [1, 1408, 8, 16, 16] -> [1, 1408, 2048] -> [1, 2048, 1408] - x = self.proj(x).flatten(2).transpose(1, 2) - return x - - -# sin-cos position encoding -# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 -def get_sinusoid_encoding_table(n_position, d_hid): - """Sinusoid position encoding table""" - - # TODO: make it with torch instead of numpy - def get_position_angle_vec(position): - return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] - - sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) - sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i - sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 - - return torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0) - - -class VisionTransformer(nn.Module): - """Vision Transformer with support for patch or hybrid CNN input stage""" - - def __init__( - self, - img_size=224, - patch_size=16, - in_chans=3, - num_classes=1000, - embed_dim=768, - depth=12, - num_heads=12, - mlp_ratio=4.0, - qkv_bias=False, - qk_scale=None, - drop_rate=0.0, - attn_drop_rate=0.0, - drop_path_rate=0.0, - head_drop_rate=0.0, - norm_layer=nn.LayerNorm, - init_values=0.0, - use_learnable_pos_emb=False, - init_scale=0.0, - all_frames=16, - tubelet_size=2, - use_mean_pooling=True, - with_cp=False, - cos_attn=False, - ): - super().__init__() - self.num_classes = num_classes - # num_features for consistency with other models - self.num_features = self.embed_dim = embed_dim - self.tubelet_size = tubelet_size - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=in_chans, - embed_dim=embed_dim, - num_frames=all_frames, - tubelet_size=tubelet_size, - ) - num_patches = self.patch_embed.num_patches - self.with_cp = with_cp - - if use_learnable_pos_emb: - self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - else: - # sine-cosine positional embeddings is on the way - self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim) - - self.pos_drop = nn.Dropout(p=drop_rate) - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule - self.blocks = nn.ModuleList( - [ - Block( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[i], - norm_layer=norm_layer, - init_values=init_values, - cos_attn=cos_attn, - ) - for i in range(depth) - ] - ) - self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) - self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None - self.head_dropout = nn.Dropout(head_drop_rate) - self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - if use_learnable_pos_emb: - trunc_normal_(self.pos_embed, std=0.02) - - self.apply(self._init_weights) - - self.head.weight.data.mul_(init_scale) - self.head.bias.data.mul_(init_scale) - self.num_frames = all_frames - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def get_num_layers(self): - return len(self.blocks) - - @torch.jit.ignore - def no_weight_decay(self): - return {"pos_embed", "cls_token"} - - def get_classifier(self): - return self.head - - def reset_classifier(self, num_classes, global_pool=""): - self.num_classes = num_classes - self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - def interpolate_pos_encoding(self, t): - T = 8 - t0 = t // self.tubelet_size - if T == t0: - return self.pos_embed - dim = self.pos_embed.shape[-1] - patch_pos_embed = self.pos_embed.permute(0, 2, 1).reshape(1, dim, 8, 16, 16) - # we add a small number to avoid floating point error in the interpolation - # see discussion at https://github.com/facebookresearch/dino/issues/8 - t0 = t0 + 0.1 - patch_pos_embed = nn.functional.interpolate( - patch_pos_embed, - scale_factor=(t0 / T, 1, 1), - mode="trilinear", - ) - assert int(t0) == patch_pos_embed.shape[-3] - patch_pos_embed = patch_pos_embed.reshape(1, dim, -1).permute(0, 2, 1) - return patch_pos_embed - - def forward_features(self, x): - # [1, 3, 16, 224, 224] - B = x.size(0) - T = x.size(2) - - # [1, 2048, 1408] - x = self.patch_embed(x) - - if self.pos_embed is not None: - x = x + self.interpolate_pos_encoding(T).expand(B, -1, -1).type_as(x).to(x.device).clone().detach() - x = self.pos_drop(x) - - for blk in self.blocks: - if self.with_cp: - x = cp.checkpoint(blk, x) - else: - x = blk(x) - - # return self.fc_norm(x) - - if self.fc_norm is not None: - return self.fc_norm(x.mean(1)) - else: - return self.norm(x[:, 0]) - - def forward(self, x): - x = self.forward_features(x) - x = self.head_dropout(x) - x = self.head(x) - return x - - -def vit_giant_patch14_224(pretrained=False, **kwargs): - model = VisionTransformer( - patch_size=14, - embed_dim=1408, - depth=40, - num_heads=16, - mlp_ratio=48 / 11, - qkv_bias=True, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - **kwargs, - ) - model.default_cfg = _cfg() - return model +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import math +import warnings +import numpy as np +import collections.abc +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from itertools import repeat + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + """ + Adapted from timm codebase + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +def _cfg(url="", **kwargs): + return { + "url": url, + "num_classes": 400, + "input_size": (3, 224, 224), + "pool_size": None, + "crop_pct": 0.9, + "interpolation": "bicubic", + "mean": (0.5, 0.5, 0.5), + "std": (0.5, 0.5, 0.5), + **kwargs, + } + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Mlp(nn.Module): + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the original BERT implement + x = self.fc2(x) + x = self.drop(x) + return x + + +class CosAttention(nn.Module): + + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, attn_head_dim=None + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + # self.scale = qk_scale or head_dim**-0.5 + # DO NOT RENAME [self.scale] (for no weight decay) + if qk_scale is None: + self.scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + else: + self.scale = qk_scale + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + + # torch.log(torch.tensor(1. / 0.01)) = 4.6052 + logit_scale = torch.clamp(self.scale, max=4.6052).exp() + + attn = attn * logit_scale + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Attention(nn.Module): + + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, attn_head_dim=None + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + # Use PyTorch native implementation of FlashAttention-2 + attn = F.scaled_dot_product_attention(q, k, v) + + x = attn.transpose(1, 2).reshape(B, N, -1) + + # Deprecated attn implementation, which consumes much more VRAM + # q = q * self.scale + # attn = q @ k.transpose(-2, -1) + # attn = attn.softmax(dim=-1) + # attn = self.attn_drop(attn) + # x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + init_values=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + attn_head_dim=None, + cos_attn=False, + ): + super().__init__() + self.norm1 = norm_layer(dim) + if cos_attn: + self.attn = CosAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim, + ) + else: + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim, + ) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x): + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, num_frames=16, tubelet_size=2): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_spatial_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1]) + num_patches = num_spatial_patches * (num_frames // tubelet_size) + + self.img_size = img_size + self.tubelet_size = tubelet_size + self.patch_size = patch_size + self.num_patches = num_patches + self.proj = nn.Conv3d( + in_channels=in_chans, + out_channels=embed_dim, + kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), + stride=(self.tubelet_size, patch_size[0], patch_size[1]), + ) + + def forward(self, x, **kwargs): + B, C, T, H, W = x.shape + assert ( + H == self.img_size[0] and W == self.img_size[1] + ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + # b, c, l -> b, l, c + # [1, 1408, 8, 16, 16] -> [1, 1408, 2048] -> [1, 2048, 1408] + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +# sin-cos position encoding +# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 +def get_sinusoid_encoding_table(n_position, d_hid): + """Sinusoid position encoding table""" + + # TODO: make it with torch instead of numpy + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0) + + +class VisionTransformer(nn.Module): + """Vision Transformer with support for patch or hybrid CNN input stage""" + + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=1000, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + head_drop_rate=0.0, + norm_layer=nn.LayerNorm, + init_values=0.0, + use_learnable_pos_emb=False, + init_scale=0.0, + all_frames=16, + tubelet_size=2, + use_mean_pooling=True, + with_cp=False, + cos_attn=False, + ): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.tubelet_size = tubelet_size + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size, + ) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + else: + # sine-cosine positional embeddings is on the way + self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim) + + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList( + [ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn, + ) + for i in range(depth) + ] + ) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head_dropout = nn.Dropout(head_drop_rate) + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=0.02) + + self.apply(self._init_weights) + + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + self.num_frames = all_frames + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {"pos_embed", "cls_token"} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=""): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def interpolate_pos_encoding(self, t): + T = 8 + t0 = t // self.tubelet_size + if T == t0: + return self.pos_embed + dim = self.pos_embed.shape[-1] + patch_pos_embed = self.pos_embed.permute(0, 2, 1).reshape(1, dim, 8, 16, 16) + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + t0 = t0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=(t0 / T, 1, 1), + mode="trilinear", + ) + assert int(t0) == patch_pos_embed.shape[-3] + patch_pos_embed = patch_pos_embed.reshape(1, dim, -1).permute(0, 2, 1) + return patch_pos_embed + + def forward_features(self, x): + # [1, 3, 16, 224, 224] + B = x.size(0) + T = x.size(2) + + # [1, 2048, 1408] + x = self.patch_embed(x) + + if self.pos_embed is not None: + x = x + self.interpolate_pos_encoding(T).expand(B, -1, -1).type_as(x).to(x.device).clone().detach() + x = self.pos_drop(x) + + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x, use_reentrant=False) + else: + x = blk(x) + + # return self.fc_norm(x) + + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)) + else: + return self.norm(x[:, 0]) + + def forward(self, x): + x = self.forward_features(x) + x = self.head_dropout(x) + x = self.head(x) + return x + + +def vit_giant_patch14_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=14, + embed_dim=1408, + depth=40, + num_heads=16, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs, + ) + model.default_cfg = _cfg() + return model diff --git a/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py index 5fbc31a..61d7743 100644 --- a/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py +++ b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py @@ -1,469 +1,469 @@ -# -------------------------------------------------------- -# Based on BEiT, timm, DINO and DeiT code bases -# https://github.com/microsoft/unilm/tree/master/beit -# https://github.com/rwightman/pytorch-image-models/tree/master/timm -# https://github.com/facebookresearch/deit -# https://github.com/facebookresearch/dino -# --------------------------------------------------------' -from functools import partial - -import torch -import torch.nn as nn -import torch.utils.checkpoint as cp - -from .videomaev2_finetune import ( - Block, - PatchEmbed, - _cfg, - get_sinusoid_encoding_table, -) - -from .videomaev2_finetune import trunc_normal_ as __call_trunc_normal_ - -def trunc_normal_(tensor, mean=0., std=1.): - __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) - - -class PretrainVisionTransformerEncoder(nn.Module): - """ Vision Transformer with support for patch or hybrid CNN input stage - """ - - def __init__(self, - img_size=224, - patch_size=16, - in_chans=3, - num_classes=0, - embed_dim=768, - depth=12, - num_heads=12, - mlp_ratio=4., - qkv_bias=False, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0., - norm_layer=nn.LayerNorm, - init_values=None, - tubelet_size=2, - use_learnable_pos_emb=False, - with_cp=False, - all_frames=16, - cos_attn=False): - super().__init__() - self.num_classes = num_classes - # num_features for consistency with other models - self.num_features = self.embed_dim = embed_dim - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=in_chans, - embed_dim=embed_dim, - num_frames=all_frames, - tubelet_size=tubelet_size) - num_patches = self.patch_embed.num_patches - self.with_cp = with_cp - - if use_learnable_pos_emb: - self.pos_embed = nn.Parameter( - torch.zeros(1, num_patches + 1, embed_dim)) - else: - # sine-cosine positional embeddings - self.pos_embed = get_sinusoid_encoding_table( - num_patches, embed_dim) - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) - ] # stochastic depth decay rule - self.blocks = nn.ModuleList([ - Block( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[i], - norm_layer=norm_layer, - init_values=init_values, - cos_attn=cos_attn) for i in range(depth) - ]) - self.norm = norm_layer(embed_dim) - self.head = nn.Linear( - embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - if use_learnable_pos_emb: - trunc_normal_(self.pos_embed, std=.02) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def get_num_layers(self): - return len(self.blocks) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token'} - - def get_classifier(self): - return self.head - - def reset_classifier(self, num_classes, global_pool=''): - self.num_classes = num_classes - self.head = nn.Linear( - self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - def forward_features(self, x, mask): - x = self.patch_embed(x) - - x = x + self.pos_embed.type_as(x).to(x.device).clone().detach() - - B, _, C = x.shape - x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible - - for blk in self.blocks: - if self.with_cp: - x_vis = cp.checkpoint(blk, x_vis) - else: - x_vis = blk(x_vis) - - x_vis = self.norm(x_vis) - return x_vis - - def forward(self, x, mask): - x = self.forward_features(x, mask) - x = self.head(x) - return x - - -class PretrainVisionTransformerDecoder(nn.Module): - """ Vision Transformer with support for patch or hybrid CNN input stage - """ - - def __init__(self, - patch_size=16, - num_classes=768, - embed_dim=768, - depth=12, - num_heads=12, - mlp_ratio=4., - qkv_bias=False, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0., - norm_layer=nn.LayerNorm, - init_values=None, - num_patches=196, - tubelet_size=2, - with_cp=False, - cos_attn=False): - super().__init__() - self.num_classes = num_classes - assert num_classes == 3 * tubelet_size * patch_size**2 - # num_features for consistency with other models - self.num_features = self.embed_dim = embed_dim - self.patch_size = patch_size - self.with_cp = with_cp - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) - ] # stochastic depth decay rule - self.blocks = nn.ModuleList([ - Block( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[i], - norm_layer=norm_layer, - init_values=init_values, - cos_attn=cos_attn) for i in range(depth) - ]) - self.norm = norm_layer(embed_dim) - self.head = nn.Linear( - embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def get_num_layers(self): - return len(self.blocks) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token'} - - def get_classifier(self): - return self.head - - def reset_classifier(self, num_classes, global_pool=''): - self.num_classes = num_classes - self.head = nn.Linear( - self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - def forward(self, x, return_token_num): - for blk in self.blocks: - if self.with_cp: - x = cp.checkpoint(blk, x) - else: - x = blk(x) - - if return_token_num > 0: - # only return the mask tokens predict pixels - x = self.head(self.norm(x[:, -return_token_num:])) - else: - # [B, N, 3*16^2] - x = self.head(self.norm(x)) - return x - - -class PretrainVisionTransformer(nn.Module): - """ Vision Transformer with support for patch or hybrid CNN input stage - """ - - def __init__( - self, - img_size=224, - patch_size=16, - encoder_in_chans=3, - encoder_num_classes=0, - encoder_embed_dim=768, - encoder_depth=12, - encoder_num_heads=12, - decoder_num_classes=1536, # decoder_num_classes=768 - decoder_embed_dim=512, - decoder_depth=8, - decoder_num_heads=8, - mlp_ratio=4., - qkv_bias=False, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0., - norm_layer=nn.LayerNorm, - init_values=0., - use_learnable_pos_emb=False, - tubelet_size=2, - num_classes=0, # avoid the error from create_fn in timm - in_chans=0, # avoid the error from create_fn in timm - with_cp=False, - all_frames=16, - cos_attn=False, - ): - super().__init__() - self.encoder = PretrainVisionTransformerEncoder( - img_size=img_size, - patch_size=patch_size, - in_chans=encoder_in_chans, - num_classes=encoder_num_classes, - embed_dim=encoder_embed_dim, - depth=encoder_depth, - num_heads=encoder_num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop_rate=drop_rate, - attn_drop_rate=attn_drop_rate, - drop_path_rate=drop_path_rate, - norm_layer=norm_layer, - init_values=init_values, - tubelet_size=tubelet_size, - use_learnable_pos_emb=use_learnable_pos_emb, - with_cp=with_cp, - all_frames=all_frames, - cos_attn=cos_attn) - - self.decoder = PretrainVisionTransformerDecoder( - patch_size=patch_size, - num_patches=self.encoder.patch_embed.num_patches, - num_classes=decoder_num_classes, - embed_dim=decoder_embed_dim, - depth=decoder_depth, - num_heads=decoder_num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop_rate=drop_rate, - attn_drop_rate=attn_drop_rate, - drop_path_rate=drop_path_rate, - norm_layer=norm_layer, - init_values=init_values, - tubelet_size=tubelet_size, - with_cp=with_cp, - cos_attn=cos_attn) - - self.encoder_to_decoder = nn.Linear( - encoder_embed_dim, decoder_embed_dim, bias=False) - - self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) - - self.pos_embed = get_sinusoid_encoding_table( - self.encoder.patch_embed.num_patches, decoder_embed_dim) - - trunc_normal_(self.mask_token, std=.02) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def get_num_layers(self): - return len(self.blocks) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token', 'mask_token'} - - def forward(self, x, mask, decode_mask=None): - decode_vis = mask if decode_mask is None else ~decode_mask - - x_vis = self.encoder(x, mask) # [B, N_vis, C_e] - x_vis = self.encoder_to_decoder(x_vis) # [B, N_vis, C_d] - B, N_vis, C = x_vis.shape - - # we don't unshuffle the correct visible token order, - # but shuffle the pos embedding accorddingly. - expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to( - x.device).clone().detach() - pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C) - pos_emd_mask = expand_pos_embed[decode_vis].reshape(B, -1, C) - - # [B, N, C_d] - x_full = torch.cat( - [x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1) - # NOTE: if N_mask==0, the shape of x is [B, N_mask, 3 * 16 * 16] - x = self.decoder(x_full, pos_emd_mask.shape[1]) - - return x - - -def pretrain_videomae_small_patch16_224(pretrained=False, **kwargs): - model = PretrainVisionTransformer( - img_size=224, - patch_size=16, - encoder_embed_dim=384, - encoder_depth=12, - encoder_num_heads=6, - encoder_num_classes=0, - decoder_num_classes=1536, # 16 * 16 * 3 * 2 - decoder_embed_dim=192, - decoder_num_heads=3, - mlp_ratio=4, - qkv_bias=True, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - **kwargs) - model.default_cfg = _cfg() - if pretrained: - checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") - model.load_state_dict(checkpoint["model"]) - return model - - -def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs): - model = PretrainVisionTransformer( - img_size=224, - patch_size=16, - encoder_embed_dim=768, - encoder_depth=12, - encoder_num_heads=12, - encoder_num_classes=0, - decoder_num_classes=1536, # 16 * 16 * 3 * 2 - decoder_embed_dim=384, - decoder_num_heads=6, - mlp_ratio=4, - qkv_bias=True, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - **kwargs) - model.default_cfg = _cfg() - if pretrained: - checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") - model.load_state_dict(checkpoint["model"]) - return model - - -def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs): - model = PretrainVisionTransformer( - img_size=224, - patch_size=16, - encoder_embed_dim=1024, - encoder_depth=24, - encoder_num_heads=16, - encoder_num_classes=0, - decoder_num_classes=1536, # 16 * 16 * 3 * 2 - decoder_embed_dim=512, - decoder_num_heads=8, - mlp_ratio=4, - qkv_bias=True, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - **kwargs) - model.default_cfg = _cfg() - if pretrained: - checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") - model.load_state_dict(checkpoint["model"]) - return model - - -def pretrain_videomae_huge_patch16_224(pretrained=False, **kwargs): - model = PretrainVisionTransformer( - img_size=224, - patch_size=16, - encoder_embed_dim=1280, - encoder_depth=32, - encoder_num_heads=16, - encoder_num_classes=0, - decoder_num_classes=1536, # 16 * 16 * 3 * 2 - decoder_embed_dim=512, - decoder_num_heads=8, - mlp_ratio=4, - qkv_bias=True, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - **kwargs) - model.default_cfg = _cfg() - if pretrained: - checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") - model.load_state_dict(checkpoint["model"]) - return model - - -def pretrain_videomae_giant_patch14_224(pretrained=False, **kwargs): - model = PretrainVisionTransformer( - img_size=224, - patch_size=14, - encoder_embed_dim=1408, - encoder_depth=40, - encoder_num_heads=16, - encoder_num_classes=0, - decoder_num_classes=1176, # 14 * 14 * 3 * 2, - decoder_embed_dim=512, - decoder_num_heads=8, - mlp_ratio=48 / 11, - qkv_bias=True, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - **kwargs) - model.default_cfg = _cfg() - if pretrained: - checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") - model.load_state_dict(checkpoint["model"]) - return model +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp + +from .videomaev2_finetune import ( + Block, + PatchEmbed, + _cfg, + get_sinusoid_encoding_table, +) + +from .videomaev2_finetune import trunc_normal_ as __call_trunc_normal_ + +def trunc_normal_(tensor, mean=0., std=1.): + __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) + + +class PretrainVisionTransformerEncoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=0, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + tubelet_size=2, + use_learnable_pos_emb=False, + with_cp=False, + all_frames=16, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches + 1, embed_dim)) + else: + # sine-cosine positional embeddings + self.pos_embed = get_sinusoid_encoding_table( + num_patches, embed_dim) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=.02) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, mask): + x = self.patch_embed(x) + + x = x + self.pos_embed.type_as(x).to(x.device).clone().detach() + + B, _, C = x.shape + x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible + + for blk in self.blocks: + if self.with_cp: + x_vis = cp.checkpoint(blk, x_vis) + else: + x_vis = blk(x_vis) + + x_vis = self.norm(x_vis) + return x_vis + + def forward(self, x, mask): + x = self.forward_features(x, mask) + x = self.head(x) + return x + + +class PretrainVisionTransformerDecoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + patch_size=16, + num_classes=768, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + num_patches=196, + tubelet_size=2, + with_cp=False, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + assert num_classes == 3 * tubelet_size * patch_size**2 + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_size = patch_size + self.with_cp = with_cp + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward(self, x, return_token_num): + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + if return_token_num > 0: + # only return the mask tokens predict pixels + x = self.head(self.norm(x[:, -return_token_num:])) + else: + # [B, N, 3*16^2] + x = self.head(self.norm(x)) + return x + + +class PretrainVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__( + self, + img_size=224, + patch_size=16, + encoder_in_chans=3, + encoder_num_classes=0, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + decoder_num_classes=1536, # decoder_num_classes=768 + decoder_embed_dim=512, + decoder_depth=8, + decoder_num_heads=8, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=0., + use_learnable_pos_emb=False, + tubelet_size=2, + num_classes=0, # avoid the error from create_fn in timm + in_chans=0, # avoid the error from create_fn in timm + with_cp=False, + all_frames=16, + cos_attn=False, + ): + super().__init__() + self.encoder = PretrainVisionTransformerEncoder( + img_size=img_size, + patch_size=patch_size, + in_chans=encoder_in_chans, + num_classes=encoder_num_classes, + embed_dim=encoder_embed_dim, + depth=encoder_depth, + num_heads=encoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + use_learnable_pos_emb=use_learnable_pos_emb, + with_cp=with_cp, + all_frames=all_frames, + cos_attn=cos_attn) + + self.decoder = PretrainVisionTransformerDecoder( + patch_size=patch_size, + num_patches=self.encoder.patch_embed.num_patches, + num_classes=decoder_num_classes, + embed_dim=decoder_embed_dim, + depth=decoder_depth, + num_heads=decoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + with_cp=with_cp, + cos_attn=cos_attn) + + self.encoder_to_decoder = nn.Linear( + encoder_embed_dim, decoder_embed_dim, bias=False) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) + + self.pos_embed = get_sinusoid_encoding_table( + self.encoder.patch_embed.num_patches, decoder_embed_dim) + + trunc_normal_(self.mask_token, std=.02) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token', 'mask_token'} + + def forward(self, x, mask, decode_mask=None): + decode_vis = mask if decode_mask is None else ~decode_mask + + x_vis = self.encoder(x, mask) # [B, N_vis, C_e] + x_vis = self.encoder_to_decoder(x_vis) # [B, N_vis, C_d] + B, N_vis, C = x_vis.shape + + # we don't unshuffle the correct visible token order, + # but shuffle the pos embedding accorddingly. + expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to( + x.device).clone().detach() + pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C) + pos_emd_mask = expand_pos_embed[decode_vis].reshape(B, -1, C) + + # [B, N, C_d] + x_full = torch.cat( + [x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1) + # NOTE: if N_mask==0, the shape of x is [B, N_mask, 3 * 16 * 16] + x = self.decoder(x_full, pos_emd_mask.shape[1]) + + return x + + +def pretrain_videomae_small_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=384, + encoder_depth=12, + encoder_num_heads=6, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=192, + decoder_num_heads=3, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=384, + decoder_num_heads=6, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1024, + encoder_depth=24, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_huge_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1280, + encoder_depth=32, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_giant_patch14_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=14, + encoder_embed_dim=1408, + encoder_depth=40, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1176, # 14 * 14 * 3 * 2, + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model diff --git a/latentsync/trepa/utils/data_utils.py b/latentsync/trepa/utils/data_utils.py index 2bd0c04..7848a24 100644 --- a/latentsync/trepa/utils/data_utils.py +++ b/latentsync/trepa/utils/data_utils.py @@ -1,321 +1,321 @@ -import os -import math -import os.path as osp -import random -import pickle -import warnings - -import glob -import numpy as np -from PIL import Image - -import torch -import torch.utils.data as data -import torch.nn.functional as F -import torch.distributed as dist -from torchvision.datasets.video_utils import VideoClips - -IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG'] -VID_EXTENSIONS = ['.avi', '.mp4', '.webm', '.mov', '.mkv', '.m4v'] - - -def get_dataloader(data_path, image_folder, resolution=128, sequence_length=16, sample_every_n_frames=1, - batch_size=16, num_workers=8): - data = VideoData(data_path, image_folder, resolution, sequence_length, sample_every_n_frames, batch_size, num_workers) - loader = data._dataloader() - return loader - - -def is_image_file(filename): - return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) - - -def get_parent_dir(path): - return osp.basename(osp.dirname(path)) - - -def preprocess(video, resolution, sequence_length=None, in_channels=3, sample_every_n_frames=1): - # video: THWC, {0, ..., 255} - assert in_channels == 3 - video = video.permute(0, 3, 1, 2).float() / 255. # TCHW - t, c, h, w = video.shape - - # temporal crop - if sequence_length is not None: - assert sequence_length <= t - video = video[:sequence_length] - - # skip frames - if sample_every_n_frames > 1: - video = video[::sample_every_n_frames] - - # scale shorter side to resolution - scale = resolution / min(h, w) - if h < w: - target_size = (resolution, math.ceil(w * scale)) - else: - target_size = (math.ceil(h * scale), resolution) - video = F.interpolate(video, size=target_size, mode='bilinear', - align_corners=False, antialias=True) - - # center crop - t, c, h, w = video.shape - w_start = (w - resolution) // 2 - h_start = (h - resolution) // 2 - video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution] - video = video.permute(1, 0, 2, 3).contiguous() # CTHW - - return {'video': video} - - -def preprocess_image(image): - # [0, 1] => [-1, 1] - img = torch.from_numpy(image) - return img - - -class VideoData(data.Dataset): - """ Class to create dataloaders for video datasets - - Args: - data_path: Path to the folder with video frames or videos. - image_folder: If True, the data is stored as images in folders. - resolution: Resolution of the returned videos. - sequence_length: Length of extracted video sequences. - sample_every_n_frames: Sample every n frames from the video. - batch_size: Batch size. - num_workers: Number of workers for the dataloader. - shuffle: If True, shuffle the data. - """ - - def __init__(self, data_path: str, image_folder: bool, resolution: int, sequence_length: int, - sample_every_n_frames: int, batch_size: int, num_workers: int, shuffle: bool = True): - super().__init__() - self.data_path = data_path - self.image_folder = image_folder - self.resolution = resolution - self.sequence_length = sequence_length - self.sample_every_n_frames = sample_every_n_frames - self.batch_size = batch_size - self.num_workers = num_workers - self.shuffle = shuffle - - def _dataset(self): - ''' - Initializes and return the dataset. - ''' - if self.image_folder: - Dataset = FrameDataset - dataset = Dataset(self.data_path, self.sequence_length, - resolution=self.resolution, sample_every_n_frames=self.sample_every_n_frames) - else: - Dataset = VideoDataset - dataset = Dataset(self.data_path, self.sequence_length, - resolution=self.resolution, sample_every_n_frames=self.sample_every_n_frames) - return dataset - - def _dataloader(self): - ''' - Initializes and returns the dataloader. - ''' - dataset = self._dataset() - if dist.is_initialized(): - sampler = data.distributed.DistributedSampler( - dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank() - ) - else: - sampler = None - dataloader = data.DataLoader( - dataset, - batch_size=self.batch_size, - num_workers=self.num_workers, - pin_memory=True, - sampler=sampler, - shuffle=sampler is None and self.shuffle is True - ) - return dataloader - - -class VideoDataset(data.Dataset): - """ - Generic dataset for videos files stored in folders. - Videos of the same class are expected to be stored in a single folder. Multiple folders can exist in the provided directory. - The class depends on `torchvision.datasets.video_utils.VideoClips` to load the videos. - Returns BCTHW videos in the range [0, 1]. - - Args: - data_folder: Path to the folder with corresponding videos stored. - sequence_length: Length of extracted video sequences. - resolution: Resolution of the returned videos. - sample_every_n_frames: Sample every n frames from the video. - """ - - def __init__(self, data_folder: str, sequence_length: int = 16, resolution: int = 128, sample_every_n_frames: int = 1): - super().__init__() - self.sequence_length = sequence_length - self.resolution = resolution - self.sample_every_n_frames = sample_every_n_frames - - folder = data_folder - files = sum([glob.glob(osp.join(folder, '**', f'*{ext}'), recursive=True) - for ext in VID_EXTENSIONS], []) - - warnings.filterwarnings('ignore') - cache_file = osp.join(folder, f"metadata_{sequence_length}.pkl") - if not osp.exists(cache_file): - clips = VideoClips(files, sequence_length, num_workers=4) - try: - pickle.dump(clips.metadata, open(cache_file, 'wb')) - except: - print(f"Failed to save metadata to {cache_file}") - else: - metadata = pickle.load(open(cache_file, 'rb')) - clips = VideoClips(files, sequence_length, - _precomputed_metadata=metadata) - - self._clips = clips - # instead of uniformly sampling from all possible clips, we sample uniformly from all possible videos - self._clips.get_clip_location = self.get_random_clip_from_video - - def get_random_clip_from_video(self, idx: int) -> tuple: - ''' - Sample a random clip starting index from the video. - - Args: - idx: Index of the video. - ''' - # Note that some videos may not contain enough frames, we skip those videos here. - while self._clips.clips[idx].shape[0] <= 0: - idx += 1 - n_clip = self._clips.clips[idx].shape[0] - clip_id = random.randint(0, n_clip - 1) - return idx, clip_id - - def __len__(self): - return self._clips.num_videos() - - def __getitem__(self, idx): - resolution = self.resolution - while True: - try: - video, _, _, idx = self._clips.get_clip(idx) - except Exception as e: - print(idx, e) - idx = (idx + 1) % self._clips.num_clips() - continue - break - - return dict(**preprocess(video, resolution, sample_every_n_frames=self.sample_every_n_frames)) - - -class FrameDataset(data.Dataset): - """ - Generic dataset for videos stored as images. The loading will iterates over all the folders and subfolders - in the provided directory. Each leaf folder is assumed to contain frames from a single video. - - Args: - data_folder: path to the folder with video frames. The folder - should contain folders with frames from each video. - sequence_length: length of extracted video sequences - resolution: resolution of the returned videos - sample_every_n_frames: sample every n frames from the video - """ - - def __init__(self, data_folder, sequence_length, resolution=64, sample_every_n_frames=1): - self.resolution = resolution - self.sequence_length = sequence_length - self.sample_every_n_frames = sample_every_n_frames - self.data_all = self.load_video_frames(data_folder) - self.video_num = len(self.data_all) - - def __getitem__(self, index): - batch_data = self.getTensor(index) - return_list = {'video': batch_data} - - return return_list - - def load_video_frames(self, dataroot: str) -> list: - ''' - Loads all the video frames under the dataroot and returns a list of all the video frames. - - Args: - dataroot: The root directory containing the video frames. - - Returns: - A list of all the video frames. - - ''' - data_all = [] - frame_list = os.walk(dataroot) - for _, meta in enumerate(frame_list): - root = meta[0] - try: - frames = sorted(meta[2], key=lambda item: int(item.split('.')[0].split('_')[-1])) - except: - print(meta[0], meta[2]) - if len(frames) < max(0, self.sequence_length * self.sample_every_n_frames): - continue - frames = [ - os.path.join(root, item) for item in frames - if is_image_file(item) - ] - if len(frames) > max(0, self.sequence_length * self.sample_every_n_frames): - data_all.append(frames) - - return data_all - - def getTensor(self, index: int) -> torch.Tensor: - ''' - Returns a tensor of the video frames at the given index. - - Args: - index: The index of the video frames to return. - - Returns: - A BCTHW tensor in the range `[0, 1]` of the video frames at the given index. - - ''' - video = self.data_all[index] - video_len = len(video) - - # load the entire video when sequence_length = -1, whiel the sample_every_n_frames has to be 1 - if self.sequence_length == -1: - assert self.sample_every_n_frames == 1 - start_idx = 0 - end_idx = video_len - else: - n_frames_interval = self.sequence_length * self.sample_every_n_frames - start_idx = random.randint(0, video_len - n_frames_interval) - end_idx = start_idx + n_frames_interval - img = Image.open(video[0]) - h, w = img.height, img.width - - if h > w: - half = (h - w) // 2 - cropsize = (0, half, w, half + w) # left, upper, right, lower - elif w > h: - half = (w - h) // 2 - cropsize = (half, 0, half + h, h) - - images = [] - for i in range(start_idx, end_idx, - self.sample_every_n_frames): - path = video[i] - img = Image.open(path) - - if h != w: - img = img.crop(cropsize) - - img = img.resize( - (self.resolution, self.resolution), - Image.ANTIALIAS) - img = np.asarray(img, dtype=np.float32) - img /= 255. - img_tensor = preprocess_image(img).unsqueeze(0) - images.append(img_tensor) - - video_clip = torch.cat(images).permute(3, 0, 1, 2) - return video_clip - - def __len__(self): - return self.video_num +import os +import math +import os.path as osp +import random +import pickle +import warnings + +import glob +import numpy as np +from PIL import Image + +import torch +import torch.utils.data as data +import torch.nn.functional as F +import torch.distributed as dist +from torchvision.datasets.video_utils import VideoClips + +IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG'] +VID_EXTENSIONS = ['.avi', '.mp4', '.webm', '.mov', '.mkv', '.m4v'] + + +def get_dataloader(data_path, image_folder, resolution=128, sequence_length=16, sample_every_n_frames=1, + batch_size=16, num_workers=8): + data = VideoData(data_path, image_folder, resolution, sequence_length, sample_every_n_frames, batch_size, num_workers) + loader = data._dataloader() + return loader + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def get_parent_dir(path): + return osp.basename(osp.dirname(path)) + + +def preprocess(video, resolution, sequence_length=None, in_channels=3, sample_every_n_frames=1): + # video: THWC, {0, ..., 255} + assert in_channels == 3 + video = video.permute(0, 3, 1, 2).float() / 255. # TCHW + t, c, h, w = video.shape + + # temporal crop + if sequence_length is not None: + assert sequence_length <= t + video = video[:sequence_length] + + # skip frames + if sample_every_n_frames > 1: + video = video[::sample_every_n_frames] + + # scale shorter side to resolution + scale = resolution / min(h, w) + if h < w: + target_size = (resolution, math.ceil(w * scale)) + else: + target_size = (math.ceil(h * scale), resolution) + video = F.interpolate(video, size=target_size, mode='bilinear', + align_corners=False, antialias=True) + + # center crop + t, c, h, w = video.shape + w_start = (w - resolution) // 2 + h_start = (h - resolution) // 2 + video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution] + video = video.permute(1, 0, 2, 3).contiguous() # CTHW + + return {'video': video} + + +def preprocess_image(image): + # [0, 1] => [-1, 1] + img = torch.from_numpy(image) + return img + + +class VideoData(data.Dataset): + """ Class to create dataloaders for video datasets + + Args: + data_path: Path to the folder with video frames or videos. + image_folder: If True, the data is stored as images in folders. + resolution: Resolution of the returned videos. + sequence_length: Length of extracted video sequences. + sample_every_n_frames: Sample every n frames from the video. + batch_size: Batch size. + num_workers: Number of workers for the dataloader. + shuffle: If True, shuffle the data. + """ + + def __init__(self, data_path: str, image_folder: bool, resolution: int, sequence_length: int, + sample_every_n_frames: int, batch_size: int, num_workers: int, shuffle: bool = True): + super().__init__() + self.data_path = data_path + self.image_folder = image_folder + self.resolution = resolution + self.sequence_length = sequence_length + self.sample_every_n_frames = sample_every_n_frames + self.batch_size = batch_size + self.num_workers = num_workers + self.shuffle = shuffle + + def _dataset(self): + ''' + Initializes and return the dataset. + ''' + if self.image_folder: + Dataset = FrameDataset + dataset = Dataset(self.data_path, self.sequence_length, + resolution=self.resolution, sample_every_n_frames=self.sample_every_n_frames) + else: + Dataset = VideoDataset + dataset = Dataset(self.data_path, self.sequence_length, + resolution=self.resolution, sample_every_n_frames=self.sample_every_n_frames) + return dataset + + def _dataloader(self): + ''' + Initializes and returns the dataloader. + ''' + dataset = self._dataset() + if dist.is_initialized(): + sampler = data.distributed.DistributedSampler( + dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank() + ) + else: + sampler = None + dataloader = data.DataLoader( + dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + pin_memory=True, + sampler=sampler, + shuffle=sampler is None and self.shuffle is True + ) + return dataloader + + +class VideoDataset(data.Dataset): + """ + Generic dataset for videos files stored in folders. + Videos of the same class are expected to be stored in a single folder. Multiple folders can exist in the provided directory. + The class depends on `torchvision.datasets.video_utils.VideoClips` to load the videos. + Returns BCTHW videos in the range [0, 1]. + + Args: + data_folder: Path to the folder with corresponding videos stored. + sequence_length: Length of extracted video sequences. + resolution: Resolution of the returned videos. + sample_every_n_frames: Sample every n frames from the video. + """ + + def __init__(self, data_folder: str, sequence_length: int = 16, resolution: int = 128, sample_every_n_frames: int = 1): + super().__init__() + self.sequence_length = sequence_length + self.resolution = resolution + self.sample_every_n_frames = sample_every_n_frames + + folder = data_folder + files = sum([glob.glob(osp.join(folder, '**', f'*{ext}'), recursive=True) + for ext in VID_EXTENSIONS], []) + + warnings.filterwarnings('ignore') + cache_file = osp.join(folder, f"metadata_{sequence_length}.pkl") + if not osp.exists(cache_file): + clips = VideoClips(files, sequence_length, num_workers=4) + try: + pickle.dump(clips.metadata, open(cache_file, 'wb')) + except: + print(f"Failed to save metadata to {cache_file}") + else: + metadata = pickle.load(open(cache_file, 'rb')) + clips = VideoClips(files, sequence_length, + _precomputed_metadata=metadata) + + self._clips = clips + # instead of uniformly sampling from all possible clips, we sample uniformly from all possible videos + self._clips.get_clip_location = self.get_random_clip_from_video + + def get_random_clip_from_video(self, idx: int) -> tuple: + ''' + Sample a random clip starting index from the video. + + Args: + idx: Index of the video. + ''' + # Note that some videos may not contain enough frames, we skip those videos here. + while self._clips.clips[idx].shape[0] <= 0: + idx += 1 + n_clip = self._clips.clips[idx].shape[0] + clip_id = random.randint(0, n_clip - 1) + return idx, clip_id + + def __len__(self): + return self._clips.num_videos() + + def __getitem__(self, idx): + resolution = self.resolution + while True: + try: + video, _, _, idx = self._clips.get_clip(idx) + except Exception as e: + print(idx, e) + idx = (idx + 1) % self._clips.num_clips() + continue + break + + return dict(**preprocess(video, resolution, sample_every_n_frames=self.sample_every_n_frames)) + + +class FrameDataset(data.Dataset): + """ + Generic dataset for videos stored as images. The loading will iterates over all the folders and subfolders + in the provided directory. Each leaf folder is assumed to contain frames from a single video. + + Args: + data_folder: path to the folder with video frames. The folder + should contain folders with frames from each video. + sequence_length: length of extracted video sequences + resolution: resolution of the returned videos + sample_every_n_frames: sample every n frames from the video + """ + + def __init__(self, data_folder, sequence_length, resolution=64, sample_every_n_frames=1): + self.resolution = resolution + self.sequence_length = sequence_length + self.sample_every_n_frames = sample_every_n_frames + self.data_all = self.load_video_frames(data_folder) + self.video_num = len(self.data_all) + + def __getitem__(self, index): + batch_data = self.getTensor(index) + return_list = {'video': batch_data} + + return return_list + + def load_video_frames(self, dataroot: str) -> list: + ''' + Loads all the video frames under the dataroot and returns a list of all the video frames. + + Args: + dataroot: The root directory containing the video frames. + + Returns: + A list of all the video frames. + + ''' + data_all = [] + frame_list = os.walk(dataroot) + for _, meta in enumerate(frame_list): + root = meta[0] + try: + frames = sorted(meta[2], key=lambda item: int(item.split('.')[0].split('_')[-1])) + except: + print(meta[0], meta[2]) + if len(frames) < max(0, self.sequence_length * self.sample_every_n_frames): + continue + frames = [ + os.path.join(root, item) for item in frames + if is_image_file(item) + ] + if len(frames) > max(0, self.sequence_length * self.sample_every_n_frames): + data_all.append(frames) + + return data_all + + def getTensor(self, index: int) -> torch.Tensor: + ''' + Returns a tensor of the video frames at the given index. + + Args: + index: The index of the video frames to return. + + Returns: + A BCTHW tensor in the range `[0, 1]` of the video frames at the given index. + + ''' + video = self.data_all[index] + video_len = len(video) + + # load the entire video when sequence_length = -1, whiel the sample_every_n_frames has to be 1 + if self.sequence_length == -1: + assert self.sample_every_n_frames == 1 + start_idx = 0 + end_idx = video_len + else: + n_frames_interval = self.sequence_length * self.sample_every_n_frames + start_idx = random.randint(0, video_len - n_frames_interval) + end_idx = start_idx + n_frames_interval + img = Image.open(video[0]) + h, w = img.height, img.width + + if h > w: + half = (h - w) // 2 + cropsize = (0, half, w, half + w) # left, upper, right, lower + elif w > h: + half = (w - h) // 2 + cropsize = (half, 0, half + h, h) + + images = [] + for i in range(start_idx, end_idx, + self.sample_every_n_frames): + path = video[i] + img = Image.open(path) + + if h != w: + img = img.crop(cropsize) + + img = img.resize( + (self.resolution, self.resolution), + Image.ANTIALIAS) + img = np.asarray(img, dtype=np.float32) + img /= 255. + img_tensor = preprocess_image(img).unsqueeze(0) + images.append(img_tensor) + + video_clip = torch.cat(images).permute(3, 0, 1, 2) + return video_clip + + def __len__(self): + return self.video_num diff --git a/latentsync/trepa/utils/metric_utils.py b/latentsync/trepa/utils/metric_utils.py index 444a4a4..f685296 100644 --- a/latentsync/trepa/utils/metric_utils.py +++ b/latentsync/trepa/utils/metric_utils.py @@ -1,161 +1,161 @@ -# Adapted from https://github.com/universome/stylegan-v/blob/master/src/metrics/metric_utils.py -import os -import random -import torch -import pickle -import numpy as np - -from typing import List, Tuple - -def seed_everything(seed): - random.seed(seed) - os.environ['PYTHONHASHSEED'] = str(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - - -class FeatureStats: - ''' - Class to store statistics of features, including all features and mean/covariance. - - Args: - capture_all: Whether to store all the features. - capture_mean_cov: Whether to store mean and covariance. - max_items: Maximum number of items to store. - ''' - def __init__(self, capture_all: bool = False, capture_mean_cov: bool = False, max_items: int = None): - ''' - ''' - self.capture_all = capture_all - self.capture_mean_cov = capture_mean_cov - self.max_items = max_items - self.num_items = 0 - self.num_features = None - self.all_features = None - self.raw_mean = None - self.raw_cov = None - - def set_num_features(self, num_features: int): - ''' - Set the number of features diminsions. - - Args: - num_features: Number of features diminsions. - ''' - if self.num_features is not None: - assert num_features == self.num_features - else: - self.num_features = num_features - self.all_features = [] - self.raw_mean = np.zeros([num_features], dtype=np.float64) - self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64) - - def is_full(self) -> bool: - ''' - Check if the maximum number of samples is reached. - - Returns: - True if the storage is full, False otherwise. - ''' - return (self.max_items is not None) and (self.num_items >= self.max_items) - - def append(self, x: np.ndarray): - ''' - Add the newly computed features to the list. Update the mean and covariance. - - Args: - x: New features to record. - ''' - x = np.asarray(x, dtype=np.float32) - assert x.ndim == 2 - if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items): - if self.num_items >= self.max_items: - return - x = x[:self.max_items - self.num_items] - - self.set_num_features(x.shape[1]) - self.num_items += x.shape[0] - if self.capture_all: - self.all_features.append(x) - if self.capture_mean_cov: - x64 = x.astype(np.float64) - self.raw_mean += x64.sum(axis=0) - self.raw_cov += x64.T @ x64 - - def append_torch(self, x: torch.Tensor, rank: int, num_gpus: int): - ''' - Add the newly computed PyTorch features to the list. Update the mean and covariance. - - Args: - x: New features to record. - rank: Rank of the current GPU. - num_gpus: Total number of GPUs. - ''' - assert isinstance(x, torch.Tensor) and x.ndim == 2 - assert 0 <= rank < num_gpus - if num_gpus > 1: - ys = [] - for src in range(num_gpus): - y = x.clone() - torch.distributed.broadcast(y, src=src) - ys.append(y) - x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples - self.append(x.cpu().numpy()) - - def get_all(self) -> np.ndarray: - ''' - Get all the stored features as NumPy Array. - - Returns: - Concatenation of the stored features. - ''' - assert self.capture_all - return np.concatenate(self.all_features, axis=0) - - def get_all_torch(self) -> torch.Tensor: - ''' - Get all the stored features as PyTorch Tensor. - - Returns: - Concatenation of the stored features. - ''' - return torch.from_numpy(self.get_all()) - - def get_mean_cov(self) -> Tuple[np.ndarray, np.ndarray]: - ''' - Get the mean and covariance of the stored features. - - Returns: - Mean and covariance of the stored features. - ''' - assert self.capture_mean_cov - mean = self.raw_mean / self.num_items - cov = self.raw_cov / self.num_items - cov = cov - np.outer(mean, mean) - return mean, cov - - def save(self, pkl_file: str): - ''' - Save the features and statistics to a pickle file. - - Args: - pkl_file: Path to the pickle file. - ''' - with open(pkl_file, 'wb') as f: - pickle.dump(self.__dict__, f) - - @staticmethod - def load(pkl_file: str) -> 'FeatureStats': - ''' - Load the features and statistics from a pickle file. - - Args: - pkl_file: Path to the pickle file. - ''' - with open(pkl_file, 'rb') as f: - s = pickle.load(f) - obj = FeatureStats(capture_all=s['capture_all'], max_items=s['max_items']) - obj.__dict__.update(s) - print('Loaded %d features from %s' % (obj.num_items, pkl_file)) - return obj +# Adapted from https://github.com/universome/stylegan-v/blob/master/src/metrics/metric_utils.py +import os +import random +import torch +import pickle +import numpy as np + +from typing import List, Tuple + +def seed_everything(seed): + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + + +class FeatureStats: + ''' + Class to store statistics of features, including all features and mean/covariance. + + Args: + capture_all: Whether to store all the features. + capture_mean_cov: Whether to store mean and covariance. + max_items: Maximum number of items to store. + ''' + def __init__(self, capture_all: bool = False, capture_mean_cov: bool = False, max_items: int = None): + ''' + ''' + self.capture_all = capture_all + self.capture_mean_cov = capture_mean_cov + self.max_items = max_items + self.num_items = 0 + self.num_features = None + self.all_features = None + self.raw_mean = None + self.raw_cov = None + + def set_num_features(self, num_features: int): + ''' + Set the number of features diminsions. + + Args: + num_features: Number of features diminsions. + ''' + if self.num_features is not None: + assert num_features == self.num_features + else: + self.num_features = num_features + self.all_features = [] + self.raw_mean = np.zeros([num_features], dtype=np.float64) + self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64) + + def is_full(self) -> bool: + ''' + Check if the maximum number of samples is reached. + + Returns: + True if the storage is full, False otherwise. + ''' + return (self.max_items is not None) and (self.num_items >= self.max_items) + + def append(self, x: np.ndarray): + ''' + Add the newly computed features to the list. Update the mean and covariance. + + Args: + x: New features to record. + ''' + x = np.asarray(x, dtype=np.float32) + assert x.ndim == 2 + if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items): + if self.num_items >= self.max_items: + return + x = x[:self.max_items - self.num_items] + + self.set_num_features(x.shape[1]) + self.num_items += x.shape[0] + if self.capture_all: + self.all_features.append(x) + if self.capture_mean_cov: + x64 = x.astype(np.float64) + self.raw_mean += x64.sum(axis=0) + self.raw_cov += x64.T @ x64 + + def append_torch(self, x: torch.Tensor, rank: int, num_gpus: int): + ''' + Add the newly computed PyTorch features to the list. Update the mean and covariance. + + Args: + x: New features to record. + rank: Rank of the current GPU. + num_gpus: Total number of GPUs. + ''' + assert isinstance(x, torch.Tensor) and x.ndim == 2 + assert 0 <= rank < num_gpus + if num_gpus > 1: + ys = [] + for src in range(num_gpus): + y = x.clone() + torch.distributed.broadcast(y, src=src) + ys.append(y) + x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples + self.append(x.cpu().numpy()) + + def get_all(self) -> np.ndarray: + ''' + Get all the stored features as NumPy Array. + + Returns: + Concatenation of the stored features. + ''' + assert self.capture_all + return np.concatenate(self.all_features, axis=0) + + def get_all_torch(self) -> torch.Tensor: + ''' + Get all the stored features as PyTorch Tensor. + + Returns: + Concatenation of the stored features. + ''' + return torch.from_numpy(self.get_all()) + + def get_mean_cov(self) -> Tuple[np.ndarray, np.ndarray]: + ''' + Get the mean and covariance of the stored features. + + Returns: + Mean and covariance of the stored features. + ''' + assert self.capture_mean_cov + mean = self.raw_mean / self.num_items + cov = self.raw_cov / self.num_items + cov = cov - np.outer(mean, mean) + return mean, cov + + def save(self, pkl_file: str): + ''' + Save the features and statistics to a pickle file. + + Args: + pkl_file: Path to the pickle file. + ''' + with open(pkl_file, 'wb') as f: + pickle.dump(self.__dict__, f) + + @staticmethod + def load(pkl_file: str) -> 'FeatureStats': + ''' + Load the features and statistics from a pickle file. + + Args: + pkl_file: Path to the pickle file. + ''' + with open(pkl_file, 'rb') as f: + s = pickle.load(f) + obj = FeatureStats(capture_all=s['capture_all'], max_items=s['max_items']) + obj.__dict__.update(s) + print('Loaded %d features from %s' % (obj.num_items, pkl_file)) + return obj diff --git a/latentsync/utils/affine_transform.py b/latentsync/utils/affine_transform.py index 0e18107..b051eb5 100644 --- a/latentsync/utils/affine_transform.py +++ b/latentsync/utils/affine_transform.py @@ -1,138 +1,144 @@ -# Adapted from https://github.com/guanjz20/StyleSync/blob/main/utils.py - -import numpy as np -import cv2 - - -def transformation_from_points(points1, points0, smooth=True, p_bias=None): - points2 = np.array(points0) - points2 = points2.astype(np.float64) - points1 = points1.astype(np.float64) - c1 = np.mean(points1, axis=0) - c2 = np.mean(points2, axis=0) - points1 -= c1 - points2 -= c2 - s1 = np.std(points1) - s2 = np.std(points2) - points1 /= s1 - points2 /= s2 - U, S, Vt = np.linalg.svd(np.matmul(points1.T, points2)) - R = (np.matmul(U, Vt)).T - sR = (s2 / s1) * R - T = c2.reshape(2, 1) - (s2 / s1) * np.matmul(R, c1.reshape(2, 1)) - M = np.concatenate((sR, T), axis=1) - if smooth: - bias = points2[2] - points1[2] - if p_bias is None: - p_bias = bias - else: - bias = p_bias * 0.2 + bias * 0.8 - p_bias = bias - M[:, 2] = M[:, 2] + bias - return M, p_bias - - -class AlignRestore(object): - def __init__(self, align_points=3): - if align_points == 3: - self.upscale_factor = 1 - self.crop_ratio = (2.8, 2.8) - self.face_template = np.array([[19 - 2, 30 - 10], [56 + 2, 30 - 10], [37.5, 45 - 5]]) - self.face_template = self.face_template * 2.8 - # self.face_size = (int(100 * self.crop_ratio[0]), int(100 * self.crop_ratio[1])) - self.face_size = (int(75 * self.crop_ratio[0]), int(100 * self.crop_ratio[1])) - self.p_bias = None - - def process(self, img, lmk_align=None, smooth=True, align_points=3): - aligned_face, affine_matrix = self.align_warp_face(img, lmk_align, smooth) - restored_img = self.restore_img(img, aligned_face, affine_matrix) - cv2.imwrite("restored.jpg", restored_img) - cv2.imwrite("aligned.jpg", aligned_face) - return aligned_face, restored_img - - def align_warp_face(self, img, lmks3, smooth=True, border_mode="constant"): - affine_matrix, self.p_bias = transformation_from_points(lmks3, self.face_template, smooth, self.p_bias) - if border_mode == "constant": - border_mode = cv2.BORDER_CONSTANT - elif border_mode == "reflect101": - border_mode = cv2.BORDER_REFLECT101 - elif border_mode == "reflect": - border_mode = cv2.BORDER_REFLECT - cropped_face = cv2.warpAffine( - img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=[127, 127, 127] - ) - return cropped_face, affine_matrix - - def align_warp_face2(self, img, landmark, border_mode="constant"): - affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template)[0] - if border_mode == "constant": - border_mode = cv2.BORDER_CONSTANT - elif border_mode == "reflect101": - border_mode = cv2.BORDER_REFLECT101 - elif border_mode == "reflect": - border_mode = cv2.BORDER_REFLECT - cropped_face = cv2.warpAffine( - img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132) - ) - return cropped_face, affine_matrix - - def restore_img(self, input_img, face, affine_matrix): - h, w, _ = input_img.shape - h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) - upsample_img = cv2.resize(input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) - inverse_affine = cv2.invertAffineTransform(affine_matrix) - inverse_affine *= self.upscale_factor - if self.upscale_factor > 1: - extra_offset = 0.5 * self.upscale_factor - else: - extra_offset = 0 - inverse_affine[:, 2] += extra_offset - inv_restored = cv2.warpAffine(face, inverse_affine, (w_up, h_up)) - mask = np.ones((self.face_size[1], self.face_size[0]), dtype=np.float32) - inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) - inv_mask_erosion = cv2.erode( - inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8) - ) - pasted_face = inv_mask_erosion[:, :, None] * inv_restored - total_face_area = np.sum(inv_mask_erosion) - w_edge = int(total_face_area**0.5) // 20 - erosion_radius = w_edge * 2 - inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) - blur_size = w_edge * 2 - inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) - inv_soft_mask = inv_soft_mask[:, :, None] - upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img - if np.max(upsample_img) > 256: - upsample_img = upsample_img.astype(np.uint16) - else: - upsample_img = upsample_img.astype(np.uint8) - return upsample_img - - -class laplacianSmooth: - def __init__(self, smoothAlpha=0.3): - self.smoothAlpha = smoothAlpha - self.pts_last = None - - def smooth(self, pts_cur): - if self.pts_last is None: - self.pts_last = pts_cur.copy() - return pts_cur.copy() - x1 = min(pts_cur[:, 0]) - x2 = max(pts_cur[:, 0]) - y1 = min(pts_cur[:, 1]) - y2 = max(pts_cur[:, 1]) - width = x2 - x1 - pts_update = [] - for i in range(len(pts_cur)): - x_new, y_new = pts_cur[i] - x_old, y_old = self.pts_last[i] - tmp = (x_new - x_old) ** 2 + (y_new - y_old) ** 2 - w = np.exp(-tmp / (width * self.smoothAlpha)) - x = x_old * w + x_new * (1 - w) - y = y_old * w + y_new * (1 - w) - pts_update.append([x, y]) - pts_update = np.array(pts_update) - self.pts_last = pts_update.copy() - - return pts_update +# Adapted from https://github.com/guanjz20/StyleSync/blob/main/utils.py + +import numpy as np +import cv2 + + +def transformation_from_points(points1, points0, smooth=True, p_bias=None): + points2 = np.array(points0) + points2 = points2.astype(np.float64) + points1 = points1.astype(np.float64) + c1 = np.mean(points1, axis=0) + c2 = np.mean(points2, axis=0) + points1 -= c1 + points2 -= c2 + s1 = np.std(points1) + s2 = np.std(points2) + points1 /= s1 + points2 /= s2 + U, S, Vt = np.linalg.svd(np.matmul(points1.T, points2)) + R = (np.matmul(U, Vt)).T + sR = (s2 / s1) * R + T = c2.reshape(2, 1) - (s2 / s1) * np.matmul(R, c1.reshape(2, 1)) + M = np.concatenate((sR, T), axis=1) + if smooth: + bias = points2[2] - points1[2] + if p_bias is None: + p_bias = bias + else: + bias = p_bias * 0.2 + bias * 0.8 + p_bias = bias + M[:, 2] = M[:, 2] + bias + return M, p_bias + + +class AlignRestore(object): + def __init__(self, align_points=3): + if align_points == 3: + self.upscale_factor = 1 + ratio = 2.8 + self.crop_ratio = (ratio, ratio) + self.face_template = np.array([[19 - 2, 30 - 10], [56 + 2, 30 - 10], [37.5, 45 - 5]]) + self.face_template = self.face_template * ratio + self.face_size = (int(75 * self.crop_ratio[0]), int(100 * self.crop_ratio[1])) + self.p_bias = None + + def process(self, img, lmk_align=None, smooth=True, align_points=3): + aligned_face, affine_matrix = self.align_warp_face(img, lmk_align, smooth) + restored_img = self.restore_img(img, aligned_face, affine_matrix) + cv2.imwrite("restored.jpg", restored_img) + cv2.imwrite("aligned.jpg", aligned_face) + return aligned_face, restored_img + + def align_warp_face(self, img, lmks3, smooth=True, border_mode="constant"): + affine_matrix, self.p_bias = transformation_from_points(lmks3, self.face_template, smooth, self.p_bias) + if border_mode == "constant": + border_mode = cv2.BORDER_CONSTANT + elif border_mode == "reflect101": + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == "reflect": + border_mode = cv2.BORDER_REFLECT + + cropped_face = cv2.warpAffine( + img, + affine_matrix, + self.face_size, + flags=cv2.INTER_LANCZOS4, + borderMode=border_mode, + borderValue=[127, 127, 127], + ) + return cropped_face, affine_matrix + + def align_warp_face2(self, img, landmark, border_mode="constant"): + affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template)[0] + if border_mode == "constant": + border_mode = cv2.BORDER_CONSTANT + elif border_mode == "reflect101": + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == "reflect": + border_mode = cv2.BORDER_REFLECT + cropped_face = cv2.warpAffine( + img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132) + ) + return cropped_face, affine_matrix + + def restore_img(self, input_img, face, affine_matrix): + h, w, _ = input_img.shape + h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) + upsample_img = cv2.resize(input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + inverse_affine = cv2.invertAffineTransform(affine_matrix) + inverse_affine *= self.upscale_factor + if self.upscale_factor > 1: + extra_offset = 0.5 * self.upscale_factor + else: + extra_offset = 0 + inverse_affine[:, 2] += extra_offset + inv_restored = cv2.warpAffine(face, inverse_affine, (w_up, h_up), flags=cv2.INTER_LANCZOS4) + mask = np.ones((self.face_size[1], self.face_size[0]), dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) + inv_mask_erosion = cv2.erode( + inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8) + ) + pasted_face = inv_mask_erosion[:, :, None] * inv_restored + total_face_area = np.sum(inv_mask_erosion) + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + inv_soft_mask = inv_soft_mask[:, :, None] + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img + if np.max(upsample_img) > 256: + upsample_img = upsample_img.astype(np.uint16) + else: + upsample_img = upsample_img.astype(np.uint8) + return upsample_img + + +class laplacianSmooth: + def __init__(self, smoothAlpha=0.3): + self.smoothAlpha = smoothAlpha + self.pts_last = None + + def smooth(self, pts_cur): + if self.pts_last is None: + self.pts_last = pts_cur.copy() + return pts_cur.copy() + x1 = min(pts_cur[:, 0]) + x2 = max(pts_cur[:, 0]) + y1 = min(pts_cur[:, 1]) + y2 = max(pts_cur[:, 1]) + width = x2 - x1 + pts_update = [] + for i in range(len(pts_cur)): + x_new, y_new = pts_cur[i] + x_old, y_old = self.pts_last[i] + tmp = (x_new - x_old) ** 2 + (y_new - y_old) ** 2 + w = np.exp(-tmp / (width * self.smoothAlpha)) + x = x_old * w + x_new * (1 - w) + y = y_old * w + y_new * (1 - w) + pts_update.append([x, y]) + pts_update = np.array(pts_update) + self.pts_last = pts_update.copy() + + return pts_update diff --git a/latentsync/utils/audio.py b/latentsync/utils/audio.py index 4403bd5..d700f82 100644 --- a/latentsync/utils/audio.py +++ b/latentsync/utils/audio.py @@ -1,194 +1,194 @@ -# Adapted from https://github.com/Rudrabha/Wav2Lip/blob/master/audio.py - -import librosa -import librosa.filters -import numpy as np -from scipy import signal -from scipy.io import wavfile -from omegaconf import OmegaConf -import torch - -audio_config_path = "configs/audio.yaml" - -config = OmegaConf.load(audio_config_path) - - -def load_wav(path, sr): - return librosa.core.load(path, sr=sr)[0] - - -def save_wav(wav, path, sr): - wav *= 32767 / max(0.01, np.max(np.abs(wav))) - # proposed by @dsmiller - wavfile.write(path, sr, wav.astype(np.int16)) - - -def save_wavenet_wav(wav, path, sr): - librosa.output.write_wav(path, wav, sr=sr) - - -def preemphasis(wav, k, preemphasize=True): - if preemphasize: - return signal.lfilter([1, -k], [1], wav) - return wav - - -def inv_preemphasis(wav, k, inv_preemphasize=True): - if inv_preemphasize: - return signal.lfilter([1], [1, -k], wav) - return wav - - -def get_hop_size(): - hop_size = config.audio.hop_size - if hop_size is None: - assert config.audio.frame_shift_ms is not None - hop_size = int(config.audio.frame_shift_ms / 1000 * config.audio.sample_rate) - return hop_size - - -def linearspectrogram(wav): - D = _stft(preemphasis(wav, config.audio.preemphasis, config.audio.preemphasize)) - S = _amp_to_db(np.abs(D)) - config.audio.ref_level_db - - if config.audio.signal_normalization: - return _normalize(S) - return S - - -def melspectrogram(wav): - D = _stft(preemphasis(wav, config.audio.preemphasis, config.audio.preemphasize)) - S = _amp_to_db(_linear_to_mel(np.abs(D))) - config.audio.ref_level_db - - if config.audio.signal_normalization: - return _normalize(S) - return S - - -def _lws_processor(): - import lws - - return lws.lws(config.audio.n_fft, get_hop_size(), fftsize=config.audio.win_size, mode="speech") - - -def _stft(y): - if config.audio.use_lws: - return _lws_processor(config.audio).stft(y).T - else: - return librosa.stft(y=y, n_fft=config.audio.n_fft, hop_length=get_hop_size(), win_length=config.audio.win_size) - - -########################################################## -# Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) -def num_frames(length, fsize, fshift): - """Compute number of time frames of spectrogram""" - pad = fsize - fshift - if length % fshift == 0: - M = (length + pad * 2 - fsize) // fshift + 1 - else: - M = (length + pad * 2 - fsize) // fshift + 2 - return M - - -def pad_lr(x, fsize, fshift): - """Compute left and right padding""" - M = num_frames(len(x), fsize, fshift) - pad = fsize - fshift - T = len(x) + 2 * pad - r = (M - 1) * fshift + fsize - T - return pad, pad + r - - -########################################################## -# Librosa correct padding -def librosa_pad_lr(x, fsize, fshift): - return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] - - -# Conversions -_mel_basis = None - - -def _linear_to_mel(spectogram): - global _mel_basis - if _mel_basis is None: - _mel_basis = _build_mel_basis() - return np.dot(_mel_basis, spectogram) - - -def _build_mel_basis(): - assert config.audio.fmax <= config.audio.sample_rate // 2 - return librosa.filters.mel( - sr=config.audio.sample_rate, - n_fft=config.audio.n_fft, - n_mels=config.audio.num_mels, - fmin=config.audio.fmin, - fmax=config.audio.fmax, - ) - - -def _amp_to_db(x): - min_level = np.exp(config.audio.min_level_db / 20 * np.log(10)) - return 20 * np.log10(np.maximum(min_level, x)) - - -def _db_to_amp(x): - return np.power(10.0, (x) * 0.05) - - -def _normalize(S): - if config.audio.allow_clipping_in_normalization: - if config.audio.symmetric_mels: - return np.clip( - (2 * config.audio.max_abs_value) * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)) - - config.audio.max_abs_value, - -config.audio.max_abs_value, - config.audio.max_abs_value, - ) - else: - return np.clip( - config.audio.max_abs_value * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)), - 0, - config.audio.max_abs_value, - ) - - assert S.max() <= 0 and S.min() - config.audio.min_level_db >= 0 - if config.audio.symmetric_mels: - return (2 * config.audio.max_abs_value) * ( - (S - config.audio.min_level_db) / (-config.audio.min_level_db) - ) - config.audio.max_abs_value - else: - return config.audio.max_abs_value * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)) - - -def _denormalize(D): - if config.audio.allow_clipping_in_normalization: - if config.audio.symmetric_mels: - return ( - (np.clip(D, -config.audio.max_abs_value, config.audio.max_abs_value) + config.audio.max_abs_value) - * -config.audio.min_level_db - / (2 * config.audio.max_abs_value) - ) + config.audio.min_level_db - else: - return ( - np.clip(D, 0, config.audio.max_abs_value) * -config.audio.min_level_db / config.audio.max_abs_value - ) + config.audio.min_level_db - - if config.audio.symmetric_mels: - return ( - (D + config.audio.max_abs_value) * -config.audio.min_level_db / (2 * config.audio.max_abs_value) - ) + config.audio.min_level_db - else: - return (D * -config.audio.min_level_db / config.audio.max_abs_value) + config.audio.min_level_db - - -def get_melspec_overlap(audio_samples, melspec_length=52): - mel_spec_overlap = melspectrogram(audio_samples.numpy()) - mel_spec_overlap = torch.from_numpy(mel_spec_overlap) - i = 0 - mel_spec_overlap_list = [] - while i + melspec_length < mel_spec_overlap.shape[1] - 3: - mel_spec_overlap_list.append(mel_spec_overlap[:, i : i + melspec_length].unsqueeze(0)) - i += 3 - mel_spec_overlap = torch.stack(mel_spec_overlap_list) - return mel_spec_overlap +# Adapted from https://github.com/Rudrabha/Wav2Lip/blob/master/audio.py + +import librosa +import librosa.filters +import numpy as np +from scipy import signal +from scipy.io import wavfile +from omegaconf import OmegaConf +import torch + +audio_config_path = "configs/audio.yaml" + +config = OmegaConf.load(audio_config_path) + + +def load_wav(path, sr): + return librosa.core.load(path, sr=sr)[0] + + +def save_wav(wav, path, sr): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + # proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + + +def save_wavenet_wav(wav, path, sr): + librosa.output.write_wav(path, wav, sr=sr) + + +def preemphasis(wav, k, preemphasize=True): + if preemphasize: + return signal.lfilter([1, -k], [1], wav) + return wav + + +def inv_preemphasis(wav, k, inv_preemphasize=True): + if inv_preemphasize: + return signal.lfilter([1], [1, -k], wav) + return wav + + +def get_hop_size(): + hop_size = config.audio.hop_size + if hop_size is None: + assert config.audio.frame_shift_ms is not None + hop_size = int(config.audio.frame_shift_ms / 1000 * config.audio.sample_rate) + return hop_size + + +def linearspectrogram(wav): + D = _stft(preemphasis(wav, config.audio.preemphasis, config.audio.preemphasize)) + S = _amp_to_db(np.abs(D)) - config.audio.ref_level_db + + if config.audio.signal_normalization: + return _normalize(S) + return S + + +def melspectrogram(wav): + D = _stft(preemphasis(wav, config.audio.preemphasis, config.audio.preemphasize)) + S = _amp_to_db(_linear_to_mel(np.abs(D))) - config.audio.ref_level_db + + if config.audio.signal_normalization: + return _normalize(S) + return S + + +def _lws_processor(): + import lws + + return lws.lws(config.audio.n_fft, get_hop_size(), fftsize=config.audio.win_size, mode="speech") + + +def _stft(y): + if config.audio.use_lws: + return _lws_processor(config.audio).stft(y).T + else: + return librosa.stft(y=y, n_fft=config.audio.n_fft, hop_length=get_hop_size(), win_length=config.audio.win_size) + + +########################################################## +# Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) +def num_frames(length, fsize, fshift): + """Compute number of time frames of spectrogram""" + pad = fsize - fshift + if length % fshift == 0: + M = (length + pad * 2 - fsize) // fshift + 1 + else: + M = (length + pad * 2 - fsize) // fshift + 2 + return M + + +def pad_lr(x, fsize, fshift): + """Compute left and right padding""" + M = num_frames(len(x), fsize, fshift) + pad = fsize - fshift + T = len(x) + 2 * pad + r = (M - 1) * fshift + fsize - T + return pad, pad + r + + +########################################################## +# Librosa correct padding +def librosa_pad_lr(x, fsize, fshift): + return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] + + +# Conversions +_mel_basis = None + + +def _linear_to_mel(spectogram): + global _mel_basis + if _mel_basis is None: + _mel_basis = _build_mel_basis() + return np.dot(_mel_basis, spectogram) + + +def _build_mel_basis(): + assert config.audio.fmax <= config.audio.sample_rate // 2 + return librosa.filters.mel( + sr=config.audio.sample_rate, + n_fft=config.audio.n_fft, + n_mels=config.audio.num_mels, + fmin=config.audio.fmin, + fmax=config.audio.fmax, + ) + + +def _amp_to_db(x): + min_level = np.exp(config.audio.min_level_db / 20 * np.log(10)) + return 20 * np.log10(np.maximum(min_level, x)) + + +def _db_to_amp(x): + return np.power(10.0, (x) * 0.05) + + +def _normalize(S): + if config.audio.allow_clipping_in_normalization: + if config.audio.symmetric_mels: + return np.clip( + (2 * config.audio.max_abs_value) * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)) + - config.audio.max_abs_value, + -config.audio.max_abs_value, + config.audio.max_abs_value, + ) + else: + return np.clip( + config.audio.max_abs_value * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)), + 0, + config.audio.max_abs_value, + ) + + assert S.max() <= 0 and S.min() - config.audio.min_level_db >= 0 + if config.audio.symmetric_mels: + return (2 * config.audio.max_abs_value) * ( + (S - config.audio.min_level_db) / (-config.audio.min_level_db) + ) - config.audio.max_abs_value + else: + return config.audio.max_abs_value * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)) + + +def _denormalize(D): + if config.audio.allow_clipping_in_normalization: + if config.audio.symmetric_mels: + return ( + (np.clip(D, -config.audio.max_abs_value, config.audio.max_abs_value) + config.audio.max_abs_value) + * -config.audio.min_level_db + / (2 * config.audio.max_abs_value) + ) + config.audio.min_level_db + else: + return ( + np.clip(D, 0, config.audio.max_abs_value) * -config.audio.min_level_db / config.audio.max_abs_value + ) + config.audio.min_level_db + + if config.audio.symmetric_mels: + return ( + (D + config.audio.max_abs_value) * -config.audio.min_level_db / (2 * config.audio.max_abs_value) + ) + config.audio.min_level_db + else: + return (D * -config.audio.min_level_db / config.audio.max_abs_value) + config.audio.min_level_db + + +def get_melspec_overlap(audio_samples, melspec_length=52): + mel_spec_overlap = melspectrogram(audio_samples.numpy()) + mel_spec_overlap = torch.from_numpy(mel_spec_overlap) + i = 0 + mel_spec_overlap_list = [] + while i + melspec_length < mel_spec_overlap.shape[1] - 3: + mel_spec_overlap_list.append(mel_spec_overlap[:, i : i + melspec_length].unsqueeze(0)) + i += 3 + mel_spec_overlap = torch.stack(mel_spec_overlap_list) + return mel_spec_overlap diff --git a/latentsync/utils/av_reader.py b/latentsync/utils/av_reader.py index 1543244..7bab2d3 100644 --- a/latentsync/utils/av_reader.py +++ b/latentsync/utils/av_reader.py @@ -1,157 +1,157 @@ -# We modified the original AVReader class of decord to solve the problem of memory leak. -# For more details, refer to: https://github.com/dmlc/decord/issues/208 - -import numpy as np -from decord.video_reader import VideoReader -from decord.audio_reader import AudioReader - -from decord.ndarray import cpu -from decord import ndarray as _nd -from decord.bridge import bridge_out - - -class AVReader(object): - """Individual audio video reader with convenient indexing function. - - Parameters - ---------- - uri: str - Path of file. - ctx: decord.Context - The context to decode the file, can be decord.cpu() or decord.gpu(). - sample_rate: int, default is -1 - Desired output sample rate of the audio, unchanged if `-1` is specified. - mono: bool, default is True - Desired output channel layout of the audio. `True` is mono layout. `False` is unchanged. - width : int, default is -1 - Desired output width of the video, unchanged if `-1` is specified. - height : int, default is -1 - Desired output height of the video, unchanged if `-1` is specified. - num_threads : int, default is 0 - Number of decoding thread, auto if `0` is specified. - fault_tol : int, default is -1 - The threshold of corupted and recovered frames. This is to prevent silent fault - tolerance when for example 50% frames of a video cannot be decoded and duplicate - frames are returned. You may find the fault tolerant feature sweet in many cases, - but not for training models. Say `N = # recovered frames` - If `fault_tol` < 0, nothing will happen. - If 0 < `fault_tol` < 1.0, if N > `fault_tol * len(video)`, raise `DECORDLimitReachedError`. - If 1 < `fault_tol`, if N > `fault_tol`, raise `DECORDLimitReachedError`. - """ - - def __init__( - self, uri, ctx=cpu(0), sample_rate=44100, mono=True, width=-1, height=-1, num_threads=0, fault_tol=-1 - ): - self.__audio_reader = AudioReader(uri, ctx, sample_rate, mono) - self.__audio_reader.add_padding() - if hasattr(uri, "read"): - uri.seek(0) - self.__video_reader = VideoReader(uri, ctx, width, height, num_threads, fault_tol) - self.__video_reader.seek(0) - - def __len__(self): - """Get length of the video. Note that sometimes FFMPEG reports inaccurate number of frames, - we always follow what FFMPEG reports. - Returns - ------- - int - The number of frames in the video file. - """ - return len(self.__video_reader) - - def __getitem__(self, idx): - """Get audio samples and video frame at `idx`. - - Parameters - ---------- - idx : int or slice - The frame index, can be negative which means it will index backwards, - or slice of frame indices. - - Returns - ------- - (ndarray/list of ndarray, ndarray) - First element is samples of shape CxS or a list of length N containing samples of shape CxS, - where N is the number of frames, C is the number of channels, - S is the number of samples of the corresponding frame. - - Second element is Frame of shape HxWx3 or batch of image frames with shape NxHxWx3, - where N is the length of the slice. - """ - assert self.__video_reader is not None and self.__audio_reader is not None - if isinstance(idx, slice): - return self.get_batch(range(*idx.indices(len(self.__video_reader)))) - if idx < 0: - idx += len(self.__video_reader) - if idx >= len(self.__video_reader) or idx < 0: - raise IndexError("Index: {} out of bound: {}".format(idx, len(self.__video_reader))) - audio_start_idx, audio_end_idx = self.__video_reader.get_frame_timestamp(idx) - audio_start_idx = self.__audio_reader._time_to_sample(audio_start_idx) - audio_end_idx = self.__audio_reader._time_to_sample(audio_end_idx) - results = (self.__audio_reader[audio_start_idx:audio_end_idx], self.__video_reader[idx]) - self.__video_reader.seek(0) - return results - - def get_batch(self, indices): - """Get entire batch of audio samples and video frames. - - Parameters - ---------- - indices : list of integers - A list of frame indices. If negative indices detected, the indices will be indexed from backward - Returns - ------- - (list of ndarray, ndarray) - First element is a list of length N containing samples of shape CxS, - where N is the number of frames, C is the number of channels, - S is the number of samples of the corresponding frame. - - Second element is Frame of shape HxWx3 or batch of image frames with shape NxHxWx3, - where N is the length of the slice. - - """ - assert self.__video_reader is not None and self.__audio_reader is not None - indices = self._validate_indices(indices) - audio_arr = [] - prev_video_idx = None - prev_audio_end_idx = None - for idx in list(indices): - frame_start_time, frame_end_time = self.__video_reader.get_frame_timestamp(idx) - # timestamp and sample conversion could have some error that could cause non-continuous audio - # we detect if retrieving continuous frame and make the audio continuous - if prev_video_idx and idx == prev_video_idx + 1: - audio_start_idx = prev_audio_end_idx - else: - audio_start_idx = self.__audio_reader._time_to_sample(frame_start_time) - audio_end_idx = self.__audio_reader._time_to_sample(frame_end_time) - audio_arr.append(self.__audio_reader[audio_start_idx:audio_end_idx]) - prev_video_idx = idx - prev_audio_end_idx = audio_end_idx - results = (audio_arr, self.__video_reader.get_batch(indices)) - self.__video_reader.seek(0) - return results - - def _get_slice(self, sl): - audio_arr = np.empty(shape=(self.__audio_reader.shape()[0], 0), dtype="float32") - for idx in list(sl): - audio_start_idx, audio_end_idx = self.__video_reader.get_frame_timestamp(idx) - audio_start_idx = self.__audio_reader._time_to_sample(audio_start_idx) - audio_end_idx = self.__audio_reader._time_to_sample(audio_end_idx) - audio_arr = np.concatenate( - (audio_arr, self.__audio_reader[audio_start_idx:audio_end_idx].asnumpy()), axis=1 - ) - results = (bridge_out(_nd.array(audio_arr)), self.__video_reader.get_batch(sl)) - self.__video_reader.seek(0) - return results - - def _validate_indices(self, indices): - """Validate int64 integers and convert negative integers to positive by backward search""" - assert self.__video_reader is not None and self.__audio_reader is not None - indices = np.array(indices, dtype=np.int64) - # process negative indices - indices[indices < 0] += len(self.__video_reader) - if not (indices >= 0).all(): - raise IndexError("Invalid negative indices: {}".format(indices[indices < 0] + len(self.__video_reader))) - if not (indices < len(self.__video_reader)).all(): - raise IndexError("Out of bound indices: {}".format(indices[indices >= len(self.__video_reader)])) - return indices +# We modified the original AVReader class of decord to solve the problem of memory leak. +# For more details, refer to: https://github.com/dmlc/decord/issues/208 + +import numpy as np +from decord.video_reader import VideoReader +from decord.audio_reader import AudioReader + +from decord.ndarray import cpu +from decord import ndarray as _nd +from decord.bridge import bridge_out + + +class AVReader(object): + """Individual audio video reader with convenient indexing function. + + Parameters + ---------- + uri: str + Path of file. + ctx: decord.Context + The context to decode the file, can be decord.cpu() or decord.gpu(). + sample_rate: int, default is -1 + Desired output sample rate of the audio, unchanged if `-1` is specified. + mono: bool, default is True + Desired output channel layout of the audio. `True` is mono layout. `False` is unchanged. + width : int, default is -1 + Desired output width of the video, unchanged if `-1` is specified. + height : int, default is -1 + Desired output height of the video, unchanged if `-1` is specified. + num_threads : int, default is 0 + Number of decoding thread, auto if `0` is specified. + fault_tol : int, default is -1 + The threshold of corupted and recovered frames. This is to prevent silent fault + tolerance when for example 50% frames of a video cannot be decoded and duplicate + frames are returned. You may find the fault tolerant feature sweet in many cases, + but not for training models. Say `N = # recovered frames` + If `fault_tol` < 0, nothing will happen. + If 0 < `fault_tol` < 1.0, if N > `fault_tol * len(video)`, raise `DECORDLimitReachedError`. + If 1 < `fault_tol`, if N > `fault_tol`, raise `DECORDLimitReachedError`. + """ + + def __init__( + self, uri, ctx=cpu(0), sample_rate=44100, mono=True, width=-1, height=-1, num_threads=0, fault_tol=-1 + ): + self.__audio_reader = AudioReader(uri, ctx, sample_rate, mono) + self.__audio_reader.add_padding() + if hasattr(uri, "read"): + uri.seek(0) + self.__video_reader = VideoReader(uri, ctx, width, height, num_threads, fault_tol) + self.__video_reader.seek(0) + + def __len__(self): + """Get length of the video. Note that sometimes FFMPEG reports inaccurate number of frames, + we always follow what FFMPEG reports. + Returns + ------- + int + The number of frames in the video file. + """ + return len(self.__video_reader) + + def __getitem__(self, idx): + """Get audio samples and video frame at `idx`. + + Parameters + ---------- + idx : int or slice + The frame index, can be negative which means it will index backwards, + or slice of frame indices. + + Returns + ------- + (ndarray/list of ndarray, ndarray) + First element is samples of shape CxS or a list of length N containing samples of shape CxS, + where N is the number of frames, C is the number of channels, + S is the number of samples of the corresponding frame. + + Second element is Frame of shape HxWx3 or batch of image frames with shape NxHxWx3, + where N is the length of the slice. + """ + assert self.__video_reader is not None and self.__audio_reader is not None + if isinstance(idx, slice): + return self.get_batch(range(*idx.indices(len(self.__video_reader)))) + if idx < 0: + idx += len(self.__video_reader) + if idx >= len(self.__video_reader) or idx < 0: + raise IndexError("Index: {} out of bound: {}".format(idx, len(self.__video_reader))) + audio_start_idx, audio_end_idx = self.__video_reader.get_frame_timestamp(idx) + audio_start_idx = self.__audio_reader._time_to_sample(audio_start_idx) + audio_end_idx = self.__audio_reader._time_to_sample(audio_end_idx) + results = (self.__audio_reader[audio_start_idx:audio_end_idx], self.__video_reader[idx]) + self.__video_reader.seek(0) + return results + + def get_batch(self, indices): + """Get entire batch of audio samples and video frames. + + Parameters + ---------- + indices : list of integers + A list of frame indices. If negative indices detected, the indices will be indexed from backward + Returns + ------- + (list of ndarray, ndarray) + First element is a list of length N containing samples of shape CxS, + where N is the number of frames, C is the number of channels, + S is the number of samples of the corresponding frame. + + Second element is Frame of shape HxWx3 or batch of image frames with shape NxHxWx3, + where N is the length of the slice. + + """ + assert self.__video_reader is not None and self.__audio_reader is not None + indices = self._validate_indices(indices) + audio_arr = [] + prev_video_idx = None + prev_audio_end_idx = None + for idx in list(indices): + frame_start_time, frame_end_time = self.__video_reader.get_frame_timestamp(idx) + # timestamp and sample conversion could have some error that could cause non-continuous audio + # we detect if retrieving continuous frame and make the audio continuous + if prev_video_idx and idx == prev_video_idx + 1: + audio_start_idx = prev_audio_end_idx + else: + audio_start_idx = self.__audio_reader._time_to_sample(frame_start_time) + audio_end_idx = self.__audio_reader._time_to_sample(frame_end_time) + audio_arr.append(self.__audio_reader[audio_start_idx:audio_end_idx]) + prev_video_idx = idx + prev_audio_end_idx = audio_end_idx + results = (audio_arr, self.__video_reader.get_batch(indices)) + self.__video_reader.seek(0) + return results + + def _get_slice(self, sl): + audio_arr = np.empty(shape=(self.__audio_reader.shape()[0], 0), dtype="float32") + for idx in list(sl): + audio_start_idx, audio_end_idx = self.__video_reader.get_frame_timestamp(idx) + audio_start_idx = self.__audio_reader._time_to_sample(audio_start_idx) + audio_end_idx = self.__audio_reader._time_to_sample(audio_end_idx) + audio_arr = np.concatenate( + (audio_arr, self.__audio_reader[audio_start_idx:audio_end_idx].asnumpy()), axis=1 + ) + results = (bridge_out(_nd.array(audio_arr)), self.__video_reader.get_batch(sl)) + self.__video_reader.seek(0) + return results + + def _validate_indices(self, indices): + """Validate int64 integers and convert negative integers to positive by backward search""" + assert self.__video_reader is not None and self.__audio_reader is not None + indices = np.array(indices, dtype=np.int64) + # process negative indices + indices[indices < 0] += len(self.__video_reader) + if not (indices >= 0).all(): + raise IndexError("Invalid negative indices: {}".format(indices[indices < 0] + len(self.__video_reader))) + if not (indices < len(self.__video_reader)).all(): + raise IndexError("Out of bound indices: {}".format(indices[indices >= len(self.__video_reader)])) + return indices diff --git a/latentsync/utils/image_processor.py b/latentsync/utils/image_processor.py index 5d7fcc7..ae03d9f 100644 --- a/latentsync/utils/image_processor.py +++ b/latentsync/utils/image_processor.py @@ -1,241 +1,344 @@ -# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -from torchvision import transforms -import cv2 -from einops import rearrange -import mediapipe as mp -import torch -import numpy as np -from typing import Union -from .affine_transform import AlignRestore, laplacianSmooth -import face_alignment - -""" -If you are enlarging the image, you should prefer to use INTER_LINEAR or INTER_CUBIC interpolation. If you are shrinking the image, you should prefer to use INTER_AREA interpolation. -https://stackoverflow.com/questions/23853632/which-kind-of-interpolation-best-for-resizing-image -""" - -def load_fixed_mask(resolution: int) -> torch.Tensor: - mask_image = cv2.imread(os.path.join(os.path.dirname(__file__), "mask.png")) - mask_image = cv2.cvtColor(mask_image, cv2.COLOR_BGR2RGB) - mask_image = cv2.resize(mask_image, (resolution, resolution), interpolation=cv2.INTER_AREA) / 255.0 - mask_image = rearrange(torch.from_numpy(mask_image), "h w c -> c h w") - return mask_image - -class ImageProcessor: - def __init__(self, resolution: int = 512, mask: str = "fix_mask", device: str = "cpu", mask_image=None): - self.resolution = resolution - self.resize = transforms.Resize( - (resolution, resolution), interpolation=transforms.InterpolationMode.BILINEAR, antialias=True - ) - self.normalize = transforms.Normalize([0.5], [0.5], inplace=True) - self.mask = mask - - if mask in ["mouth", "face", "eye"]: - self.face_mesh = mp.solutions.face_mesh.FaceMesh(static_image_mode=True) # Process single image - if mask == "fix_mask": - self.face_mesh = None - self.smoother = laplacianSmooth() - self.restorer = AlignRestore() - - if mask_image is None: - self.mask_image = load_fixed_mask(resolution) - else: - self.mask_image = mask_image - - if device != "cpu": - self.fa = face_alignment.FaceAlignment( - face_alignment.LandmarksType.TWO_D, flip_input=False, device=device - ) - self.face_mesh = None - else: - self.face_mesh = None - self.fa = None - - def detect_facial_landmarks(self, image: np.ndarray): - height, width, _ = image.shape - results = self.face_mesh.process(image) - if not results.multi_face_landmarks: # Face not detected - print("Skipping frame: No face detected") - return None # Return None instead of raising an error - face_landmarks = results.multi_face_landmarks[0] # Only use the first face in the image - landmark_coordinates = [ - (int(landmark.x * width), int(landmark.y * height)) for landmark in face_landmarks.landmark - ] # x means width, y means height - return landmark_coordinates - - def preprocess_one_masked_image(self, image: torch.Tensor) -> np.ndarray: - image = self.resize(image) - - if self.mask == "mouth" or self.mask == "face": - landmark_coordinates = self.detect_facial_landmarks(image) - if landmark_coordinates is None: # No face detected - return None, None, None # Skip this frame - - if self.mask == "mouth": - surround_landmarks = mouth_surround_landmarks - else: - surround_landmarks = face_surround_landmarks - - points = [landmark_coordinates[landmark] for landmark in surround_landmarks] - points = np.array(points) - mask = np.ones((self.resolution, self.resolution)) - mask = cv2.fillPoly(mask, pts=[points], color=(0, 0, 0)) - mask = torch.from_numpy(mask) - mask = mask.unsqueeze(0) - elif self.mask == "half": - mask = torch.ones((self.resolution, self.resolution)) - height = mask.shape[0] - mask[height // 2 :, :] = 0 - mask = mask.unsqueeze(0) - elif self.mask == "eye": - mask = torch.ones((self.resolution, self.resolution)) - landmark_coordinates = self.detect_facial_landmarks(image) - if landmark_coordinates is None: # No face detected - return None, None, None # Skip this frame - y = landmark_coordinates[195][1] - mask[y:, :] = 0 - mask = mask.unsqueeze(0) - else: - raise ValueError("Invalid mask type") - - image = image.to(dtype=torch.float32) - pixel_values = self.normalize(image / 255.0) - masked_pixel_values = pixel_values * mask - mask = 1 - mask - - return pixel_values, masked_pixel_values, mask - - def affine_transform(self, image: torch.Tensor): - # Convert image to numpy array if necessary - if isinstance(image, torch.Tensor): - image = rearrange(image, "c h w -> h w c").numpy() - - # Detect facial landmarks - if self.fa is None: - landmark_coordinates = self.detect_facial_landmarks(image) - if landmark_coordinates is None: # No face detected - return None, None, None # Skip this frame - lm68 = mediapipe_lm478_to_face_alignment_lm68(landmark_coordinates) - else: - detected_faces = self.fa.get_landmarks(image) - if detected_faces is None: # No face detected - return None, None, None # Skip this frame - lm68 = detected_faces[0] - - # Perform affine transformation - points = self.smoother.smooth(lm68) - lmk3_ = np.zeros((3, 2)) - lmk3_[0] = points[17:22].mean(0) - lmk3_[1] = points[22:27].mean(0) - lmk3_[2] = points[27:36].mean(0) - face, affine_matrix = self.restorer.align_warp_face( - image.copy(), lmks3=lmk3_, smooth=True, border_mode="constant" - ) - box = [0, 0, face.shape[1], face.shape[0]] # x1, y1, x2, y2 - face = cv2.resize(face, (self.resolution, self.resolution), interpolation=cv2.INTER_CUBIC) - face = rearrange(torch.from_numpy(face), "h w c -> c h w") - return face, box, affine_matrix - - def preprocess_fixed_mask_image(self, image: torch.Tensor, affine_transform=False): - if affine_transform: - result = self.affine_transform(image) - if result is None: # No face detected - return None, None, None # Skip this frame - image, _, _ = result - else: - image = self.resize(image) - pixel_values = self.normalize(image / 255.0) - masked_pixel_values = pixel_values * self.mask_image - return pixel_values, masked_pixel_values, self.mask_image[0:1] - - def prepare_masks_and_masked_images(self, images: Union[torch.Tensor, np.ndarray], affine_transform=False): - if isinstance(images, np.ndarray): - images = torch.from_numpy(images) - if images.shape[3] == 3: - images = rearrange(images, "b h w c -> b c h w") - - pixel_values_list, masked_pixel_values_list, masks_list = [], [], [] - for image in images: - if self.mask == "fix_mask": - result = self.preprocess_fixed_mask_image(image, affine_transform=affine_transform) - else: - result = self.preprocess_one_masked_image(image) - - if result is not None: # Skip frames where no face is detected - pixel_values, masked_pixel_values, mask = result - pixel_values_list.append(pixel_values) - masked_pixel_values_list.append(masked_pixel_values) - masks_list.append(mask) - - if not pixel_values_list: # If no valid frames were processed - return None, None, None - - return torch.stack(pixel_values_list), torch.stack(masked_pixel_values_list), torch.stack(masks_list) - - def process_images(self, images: Union[torch.Tensor, np.ndarray]): - if isinstance(images, np.ndarray): - images = torch.from_numpy(images) - if images.shape[3] == 3: - images = rearrange(images, "b h w c -> b c h w") - images = self.resize(images) - pixel_values = self.normalize(images / 255.0) - return pixel_values - - def close(self): - if self.face_mesh is not None: - self.face_mesh.close() - -def mediapipe_lm478_to_face_alignment_lm68(lm478, return_2d=True): - """ - lm478: [B, 478, 3] or [478,3] - """ - landmarks_extracted = [] - for index in landmark_points_68: - x = lm478[index][0] - y = lm478[index][1] - landmarks_extracted.append((x, y)) - return np.array(landmarks_extracted) - -landmark_points_68 = [ - 162, 234, 93, 58, 172, 136, 149, 148, 152, 377, 378, 365, 397, 288, 323, 454, 389, 71, 63, 105, 66, 107, 336, 296, 334, 293, 301, 168, 197, 5, 4, 75, 97, 2, 326, 305, 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380, 61, 39, 37, 0, 267, 269, 291, 405, 314, 17, 84, 181, 78, 82, 13, 312, 308, 317, 14, 87, -] - -# Refer to https://storage.googleapis.com/mediapipe-assets/documentation/mediapipe_face_landmark_fullsize.png -mouth_surround_landmarks = [ - 164, 165, 167, 92, 186, 57, 43, 106, 182, 83, 18, 313, 406, 335, 273, 287, 410, 322, 391, 393, -] - -face_surround_landmarks = [ - 152, 377, 400, 378, 379, 365, 397, 288, 435, 433, 411, 425, 423, 327, 326, 94, 97, 98, 203, 205, 187, 213, 215, 58, 172, 136, 150, 149, 176, 148, -] - -if __name__ == "__main__": - image_processor = ImageProcessor(512, mask="fix_mask") - video = cv2.VideoCapture("/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/original/val/RD_Radio57_000.mp4") - while True: - ret, frame = video.read() - if not ret: - break - - frame = rearrange(torch.Tensor(frame).type(torch.uint8), "h w c -> c h w") - result = image_processor.affine_transform(frame) - - if result is not None: # Only process frames where a face is detected - face, _, _ = result - face = (rearrange(face, "c h w -> h w c").detach().cpu().numpy()).astype(np.uint8) - cv2.imwrite("face.jpg", face) - break \ No newline at end of file +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torchvision import transforms +import cv2 +from einops import rearrange +import mediapipe as mp +import torch +import numpy as np +from typing import Union +from .affine_transform import AlignRestore, laplacianSmooth +import face_alignment + +""" +If you are enlarging the image, you should prefer to use INTER_LINEAR or INTER_CUBIC interpolation. If you are shrinking the image, you should prefer to use INTER_AREA interpolation. +https://stackoverflow.com/questions/23853632/which-kind-of-interpolation-best-for-resizing-image +""" + + +def load_fixed_mask(resolution: int, mask_image_path="latentsync/utils/mask.png") -> torch.Tensor: + mask_image = cv2.imread(mask_image_path) + mask_image = cv2.cvtColor(mask_image, cv2.COLOR_BGR2RGB) + mask_image = cv2.resize(mask_image, (resolution, resolution), interpolation=cv2.INTER_LANCZOS4) / 255.0 + mask_image = rearrange(torch.from_numpy(mask_image), "h w c -> c h w") + return mask_image + + +class ImageProcessor: + def __init__(self, resolution: int = 512, mask: str = "fix_mask", device: str = "cpu", mask_image=None): + self.resolution = resolution + self.resize = transforms.Resize( + (resolution, resolution), interpolation=transforms.InterpolationMode.BILINEAR, antialias=True + ) + self.normalize = transforms.Normalize([0.5], [0.5], inplace=True) + self.mask = mask + + if mask in ["mouth", "face", "eye"]: + self.face_mesh = mp.solutions.face_mesh.FaceMesh(static_image_mode=True) # Process single image + if mask == "fix_mask": + self.face_mesh = None + self.smoother = laplacianSmooth() + self.restorer = AlignRestore() + + if mask_image is None: + self.mask_image = load_fixed_mask(resolution) + else: + self.mask_image = mask_image + + if device != "cpu": + self.fa = face_alignment.FaceAlignment( + face_alignment.LandmarksType.TWO_D, flip_input=False, device=device + ) + self.face_mesh = None + else: + # self.face_mesh = mp.solutions.face_mesh.FaceMesh(static_image_mode=True) # Process single image + self.face_mesh = None + self.fa = None + + def detect_facial_landmarks(self, image: np.ndarray): + height, width, _ = image.shape + results = self.face_mesh.process(image) + if not results.multi_face_landmarks: # Face not detected + raise RuntimeError("Face not detected") + face_landmarks = results.multi_face_landmarks[0] # Only use the first face in the image + landmark_coordinates = [ + (int(landmark.x * width), int(landmark.y * height)) for landmark in face_landmarks.landmark + ] # x means width, y means height + return landmark_coordinates + + def preprocess_one_masked_image(self, image: torch.Tensor) -> np.ndarray: + image = self.resize(image) + + if self.mask == "mouth" or self.mask == "face": + landmark_coordinates = self.detect_facial_landmarks(image) + if self.mask == "mouth": + surround_landmarks = mouth_surround_landmarks + else: + surround_landmarks = face_surround_landmarks + + points = [landmark_coordinates[landmark] for landmark in surround_landmarks] + points = np.array(points) + mask = np.ones((self.resolution, self.resolution)) + mask = cv2.fillPoly(mask, pts=[points], color=(0, 0, 0)) + mask = torch.from_numpy(mask) + mask = mask.unsqueeze(0) + elif self.mask == "half": + mask = torch.ones((self.resolution, self.resolution)) + height = mask.shape[0] + mask[height // 2 :, :] = 0 + mask = mask.unsqueeze(0) + elif self.mask == "eye": + mask = torch.ones((self.resolution, self.resolution)) + landmark_coordinates = self.detect_facial_landmarks(image) + y = landmark_coordinates[195][1] + mask[y:, :] = 0 + mask = mask.unsqueeze(0) + else: + raise ValueError("Invalid mask type") + + image = image.to(dtype=torch.float32) + pixel_values = self.normalize(image / 255.0) + masked_pixel_values = pixel_values * mask + mask = 1 - mask + + return pixel_values, masked_pixel_values, mask + + def affine_transform(self, image: torch.Tensor, allow_multi_faces: bool = True) -> np.ndarray: + # image = rearrange(image, "c h w-> h w c").numpy() + if self.fa is None: + landmark_coordinates = np.array(self.detect_facial_landmarks(image)) + lm68 = mediapipe_lm478_to_face_alignment_lm68(landmark_coordinates) + else: + detected_faces = self.fa.get_landmarks(image) + if detected_faces is None: + raise RuntimeError("Face not detected") + if not allow_multi_faces and len(detected_faces) > 1: + raise RuntimeError("More than one face detected") + lm68 = detected_faces[0] + + points = self.smoother.smooth(lm68) + lmk3_ = np.zeros((3, 2)) + lmk3_[0] = points[17:22].mean(0) + lmk3_[1] = points[22:27].mean(0) + lmk3_[2] = points[27:36].mean(0) + # print(lmk3_) + face, affine_matrix = self.restorer.align_warp_face( + image.copy(), lmks3=lmk3_, smooth=True, border_mode="constant" + ) + box = [0, 0, face.shape[1], face.shape[0]] # x1, y1, x2, y2 + face = cv2.resize(face, (self.resolution, self.resolution), interpolation=cv2.INTER_LANCZOS4) + face = rearrange(torch.from_numpy(face), "h w c -> c h w") + return face, box, affine_matrix + + def preprocess_fixed_mask_image(self, image: torch.Tensor, affine_transform=False): + if affine_transform: + image, _, _ = self.affine_transform(image) + else: + image = self.resize(image) + pixel_values = self.normalize(image / 255.0) + masked_pixel_values = pixel_values * self.mask_image + return pixel_values, masked_pixel_values, self.mask_image[0:1] + + def prepare_masks_and_masked_images(self, images: Union[torch.Tensor, np.ndarray], affine_transform=False): + if isinstance(images, np.ndarray): + images = torch.from_numpy(images) + if images.shape[3] == 3: + images = rearrange(images, "f h w c -> f c h w") + if self.mask == "fix_mask": + results = [self.preprocess_fixed_mask_image(image, affine_transform=affine_transform) for image in images] + else: + results = [self.preprocess_one_masked_image(image) for image in images] + + pixel_values_list, masked_pixel_values_list, masks_list = list(zip(*results)) + return torch.stack(pixel_values_list), torch.stack(masked_pixel_values_list), torch.stack(masks_list) + + def process_images(self, images: Union[torch.Tensor, np.ndarray]): + if isinstance(images, np.ndarray): + images = torch.from_numpy(images) + if images.shape[3] == 3: + images = rearrange(images, "f h w c -> f c h w") + images = self.resize(images) + pixel_values = self.normalize(images / 255.0) + return pixel_values + + def close(self): + if self.face_mesh is not None: + self.face_mesh.close() + + +def mediapipe_lm478_to_face_alignment_lm68(lm478, return_2d=True): + """ + lm478: [B, 478, 3] or [478,3] + """ + # lm478[..., 0] *= W + # lm478[..., 1] *= H + landmarks_extracted = [] + for index in landmark_points_68: + x = lm478[index][0] + y = lm478[index][1] + landmarks_extracted.append((x, y)) + return np.array(landmarks_extracted) + + +landmark_points_68 = [ + 162, + 234, + 93, + 58, + 172, + 136, + 149, + 148, + 152, + 377, + 378, + 365, + 397, + 288, + 323, + 454, + 389, + 71, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 301, + 168, + 197, + 5, + 4, + 75, + 97, + 2, + 326, + 305, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 61, + 39, + 37, + 0, + 267, + 269, + 291, + 405, + 314, + 17, + 84, + 181, + 78, + 82, + 13, + 312, + 308, + 317, + 14, + 87, +] + + +# Refer to https://storage.googleapis.com/mediapipe-assets/documentation/mediapipe_face_landmark_fullsize.png +mouth_surround_landmarks = [ + 164, + 165, + 167, + 92, + 186, + 57, + 43, + 106, + 182, + 83, + 18, + 313, + 406, + 335, + 273, + 287, + 410, + 322, + 391, + 393, +] + +face_surround_landmarks = [ + 152, + 377, + 400, + 378, + 379, + 365, + 397, + 288, + 435, + 433, + 411, + 425, + 423, + 327, + 326, + 94, + 97, + 98, + 203, + 205, + 187, + 213, + 215, + 58, + 172, + 136, + 150, + 149, + 176, + 148, +] + +if __name__ == "__main__": + image_processor = ImageProcessor(512, mask="fix_mask") + video = cv2.VideoCapture("assets/demo1_video.mp4") + while True: + ret, frame = video.read() + # if not ret: + # break + + # cv2.imwrite("image.jpg", frame) + + frame = rearrange(torch.Tensor(frame).type(torch.uint8), "h w c -> c h w") + # face, masked_face, _ = image_processor.preprocess_fixed_mask_image(frame, affine_transform=True) + face, _, _ = image_processor.affine_transform(frame) + + break + + face = (rearrange(face, "c h w -> h w c").detach().cpu().numpy()).astype(np.uint8) + cv2.imwrite("face.jpg", face) + + # masked_face = (rearrange(masked_face, "c h w -> h w c").detach().cpu().numpy()).astype(np.uint8) + # cv2.imwrite("masked_face.jpg", masked_face) diff --git a/latentsync/utils/mask2.png b/latentsync/utils/mask2.png new file mode 100644 index 0000000..e6bb9ad Binary files /dev/null and b/latentsync/utils/mask2.png differ diff --git a/latentsync/utils/mask3.png b/latentsync/utils/mask3.png new file mode 100644 index 0000000..f93f03d Binary files /dev/null and b/latentsync/utils/mask3.png differ diff --git a/latentsync/utils/mask4.png b/latentsync/utils/mask4.png new file mode 100644 index 0000000..9b1f8d2 Binary files /dev/null and b/latentsync/utils/mask4.png differ diff --git a/latentsync/utils/util.py b/latentsync/utils/util.py index dc36594..68b620c 100644 --- a/latentsync/utils/util.py +++ b/latentsync/utils/util.py @@ -1,378 +1,260 @@ -# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import imageio -import numpy as np -import json -from typing import Union -import matplotlib.pyplot as plt - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision -import torch.distributed as dist -from torchvision import transforms - -from tqdm import tqdm -from einops import rearrange -import cv2 -from decord import AudioReader, VideoReader -import shutil -import subprocess - - -# Machine epsilon for a float32 (single precision) -eps = np.finfo(np.float32).eps - - -def read_json(filepath: str): - with open(filepath) as f: - json_dict = json.load(f) - return json_dict - - -def read_video(video_path: str, change_fps=True, use_decord=True): - if change_fps: - # Create temp directory next to the video file - temp_dir = os.path.join(os.path.dirname(video_path), "temp") - if os.path.exists(temp_dir): - shutil.rmtree(temp_dir) - os.makedirs(temp_dir, exist_ok=True) - - # Normalize paths for Windows - video_path = os.path.normpath(video_path).replace('\\', '/') - temp_video = os.path.join(temp_dir, 'video.mp4').replace('\\', '/') - - # Use proper path quoting for FFmpeg - command = f'ffmpeg -loglevel error -y -nostdin -i "{video_path}" -r 25 -crf 18 "{temp_video}"' - - print(f"Running FFmpeg command: {command}") - subprocess.run(command, shell=True, check=True) - target_video_path = temp_video - else: - target_video_path = video_path - - print(f"Reading video from: {target_video_path}") - - if use_decord: - return read_video_decord(target_video_path) - else: - return read_video_cv2(target_video_path) - - -def read_video_decord(video_path: str): - vr = VideoReader(video_path) - video_frames = vr[:].asnumpy() - vr.seek(0) - return video_frames - - -def read_video_cv2(video_path: str): - # Open the video file - video_path = os.path.normpath(video_path) - print(f"Opening video with CV2: {video_path}") - cap = cv2.VideoCapture(video_path) - - # Check if the video was opened successfully - if not cap.isOpened(): - print(f"Error: Could not open video at path: {video_path}") - return np.array([]) - - frames = [] - frame_count = 0 - - while True: - # Read a frame - ret, frame = cap.read() - - # If frame is read correctly ret is True - if not ret: - break - - # Convert BGR to RGB - frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frames.append(frame_rgb) - frame_count += 1 - - # Release the video capture object - cap.release() - - print(f"Successfully read {frame_count} frames from video") - return np.array(frames) - - -def read_audio(audio_path: str, audio_sample_rate: int = 16000): - if audio_path is None: - raise ValueError("Audio path is required.") - ar = AudioReader(audio_path, sample_rate=audio_sample_rate, mono=True) - - # To access the audio samples - audio_samples = torch.from_numpy(ar[:].asnumpy()) - audio_samples = audio_samples.squeeze(0) - - return audio_samples - - -def write_video(video_output_path: str, video_frames: np.ndarray, fps: int): - height, width = video_frames[0].shape[:2] - out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) - # out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*"vp09"), fps, (width, height)) - for frame in video_frames: - frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) - out.write(frame) - out.release() - - -def init_dist(backend="nccl", **kwargs): - """Initializes distributed environment.""" - rank = int(os.environ["RANK"]) - num_gpus = torch.cuda.device_count() - if num_gpus == 0: - raise RuntimeError("No GPUs available for training.") - local_rank = rank % num_gpus - torch.cuda.set_device(local_rank) - dist.init_process_group(backend=backend, **kwargs) - - return local_rank - - -def zero_rank_print(s): - if dist.is_initialized() and dist.get_rank() == 0: - print("### " + s) - - -def zero_rank_log(logger, message: str): - if dist.is_initialized() and dist.get_rank() == 0: - logger.info(message) - - -def make_audio_window(audio_embeddings: torch.Tensor, window_size: int): - audio_window = [] - end_idx = audio_embeddings.shape[1] - window_size + 1 - for i in range(end_idx): - audio_window.append(audio_embeddings[:, i : i + window_size, :]) - audio_window = torch.stack(audio_window) - audio_window = rearrange(audio_window, "f b w d -> b f w d") - return audio_window - - -def check_video_fps(video_path: str): - cam = cv2.VideoCapture(video_path) - fps = cam.get(cv2.CAP_PROP_FPS) - if fps != 25: - raise ValueError(f"Video FPS is not 25, it is {fps}. Please convert the video to 25 FPS.") - - -def tailor_tensor_to_length(tensor: torch.Tensor, length: int): - if len(tensor) == length: - return tensor - elif len(tensor) > length: - return tensor[:length] - else: - return torch.cat([tensor, tensor[-1].repeat(length - len(tensor))]) - - -def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8): - videos = rearrange(videos, "b c f h w -> f b c h w") - outputs = [] - for x in videos: - x = torchvision.utils.make_grid(x, nrow=n_rows) - x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) - if rescale: - x = (x + 1.0) / 2.0 # -1,1 -> 0,1 - x = (x * 255).numpy().astype(np.uint8) - outputs.append(x) - - os.makedirs(os.path.dirname(path), exist_ok=True) - imageio.mimsave(path, outputs, fps=fps) - - -def interpolate_features(features: torch.Tensor, output_len: int) -> torch.Tensor: - features = features.cpu().numpy() - input_len, num_features = features.shape - - input_timesteps = np.linspace(0, 10, input_len) - output_timesteps = np.linspace(0, 10, output_len) - output_features = np.zeros((output_len, num_features)) - for feat in range(num_features): - output_features[:, feat] = np.interp(output_timesteps, input_timesteps, features[:, feat]) - return torch.from_numpy(output_features) - - -# DDIM Inversion -@torch.no_grad() -def init_prompt(prompt, pipeline): - uncond_input = pipeline.tokenizer( - [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, return_tensors="pt" - ) - uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0] - text_input = pipeline.tokenizer( - [prompt], - padding="max_length", - max_length=pipeline.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0] - context = torch.cat([uncond_embeddings, text_embeddings]) - - return context - - -def reversed_forward(ddim_scheduler, pred_noise, timesteps, x_t): - # Compute alphas, betas - alpha_prod_t = ddim_scheduler.alphas_cumprod[timesteps] - beta_prod_t = 1 - alpha_prod_t - - # 3. compute predicted original sample from predicted noise also called - # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf - if ddim_scheduler.config.prediction_type == "epsilon": - beta_prod_t = beta_prod_t[:, None, None, None, None] - alpha_prod_t = alpha_prod_t[:, None, None, None, None] - pred_original_sample = (x_t - beta_prod_t ** (0.5) * pred_noise) / alpha_prod_t ** (0.5) - else: - raise NotImplementedError("This prediction type is not implemented yet") - - # Clip "predicted x_0" - if ddim_scheduler.config.clip_sample: - pred_original_sample = torch.clamp(pred_original_sample, -1, 1) - return pred_original_sample - - -def next_step( - model_output: Union[torch.FloatTensor, np.ndarray], - timestep: int, - sample: Union[torch.FloatTensor, np.ndarray], - ddim_scheduler, -): - timestep, next_timestep = ( - min(timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), - timestep, - ) - alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod - alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep] - beta_prod_t = 1 - alpha_prod_t - next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5 - next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output - next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction - return next_sample - - -def get_noise_pred_single(latents, t, context, unet): - noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"] - return noise_pred - - -@torch.no_grad() -def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt): - context = init_prompt(prompt, pipeline) - uncond_embeddings, cond_embeddings = context.chunk(2) - all_latent = [latent] - latent = latent.clone().detach() - for i in tqdm(range(num_inv_steps)): - t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1] - noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet) - latent = next_step(noise_pred, t, latent, ddim_scheduler) - all_latent.append(latent) - return all_latent - - -@torch.no_grad() -def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""): - ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt) - return ddim_latents - - -def plot_loss_chart(save_path: str, *args): - # Creating the plot - plt.figure() - for loss_line in args: - plt.plot(loss_line[1], loss_line[2], label=loss_line[0]) - plt.xlabel("Step") - plt.ylabel("Loss") - plt.legend() - - # Save the figure to a file - plt.savefig(save_path) - - # Close the figure to free memory - plt.close() - - -CRED = "\033[91m" -CEND = "\033[0m" - - -def red_text(text: str): - return f"{CRED}{text}{CEND}" - - -log_loss = nn.BCELoss(reduction="none") - - -def cosine_loss(vision_embeds, audio_embeds, y): - sims = nn.functional.cosine_similarity(vision_embeds, audio_embeds) - # sims[sims!=sims] = 0 # remove nan - # sims = sims.clamp(0, 1) - loss = log_loss(sims.unsqueeze(1), y).squeeze() - return loss - - -def save_image(image, save_path): - # input size (C, H, W) - image = (image / 2 + 0.5).clamp(0, 1) - image = (image * 255).to(torch.uint8) - image = transforms.ToPILImage()(image) - # Save the image copy - image.save(save_path) - - # Close the image file - image.close() - - -def gather_loss(loss, device): - # Sum the local loss across all processes - local_loss = loss.item() - global_loss = torch.tensor(local_loss, dtype=torch.float32).to(device) - dist.all_reduce(global_loss, op=dist.ReduceOp.SUM) - - # Calculate the average loss across all processes - global_average_loss = global_loss.item() / dist.get_world_size() - return global_average_loss - - -def gather_video_paths_recursively(input_dir): - print(f"Recursively gathering video paths of {input_dir} ...") - paths = [] - gather_video_paths(input_dir, paths) - return paths - - -def gather_video_paths(input_dir, paths): - for file in sorted(os.listdir(input_dir)): - if file.endswith(".mp4"): - filepath = os.path.join(input_dir, file) - paths.append(filepath) - elif os.path.isdir(os.path.join(input_dir, file)): - gather_video_paths(os.path.join(input_dir, file), paths) - - -def count_video_time(video_path): - video = cv2.VideoCapture(video_path) - - frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT) - fps = video.get(cv2.CAP_PROP_FPS) - return frame_count / fps +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import imageio +import numpy as np +import json +from typing import Union +import matplotlib.pyplot as plt + +import torch +import torch.nn as nn +import torchvision +import torch.distributed as dist +from torchvision import transforms + +from einops import rearrange +import cv2 +from decord import AudioReader, VideoReader +import shutil +import subprocess + + +# Machine epsilon for a float32 (single precision) +eps = np.finfo(np.float32).eps + + +def read_json(filepath: str): + with open(filepath) as f: + json_dict = json.load(f) + return json_dict + + +def read_video(video_path: str, change_fps=True, use_decord=True): + if change_fps: + temp_dir = "temp" + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + os.makedirs(temp_dir, exist_ok=True) + command = ( + f"ffmpeg -loglevel error -y -nostdin -i {video_path} -r 25 -crf 18 {os.path.join(temp_dir, 'video.mp4')}" + ) + subprocess.run(command, shell=True) + target_video_path = os.path.join(temp_dir, "video.mp4") + else: + target_video_path = video_path + + if use_decord: + return read_video_decord(target_video_path) + else: + return read_video_cv2(target_video_path) + + +def read_video_decord(video_path: str): + vr = VideoReader(video_path) + video_frames = vr[:].asnumpy() + vr.seek(0) + return video_frames + + +def read_video_cv2(video_path: str): + # Open the video file + cap = cv2.VideoCapture(video_path) + + # Check if the video was opened successfully + if not cap.isOpened(): + print("Error: Could not open video.") + return np.array([]) + + frames = [] + + while True: + # Read a frame + ret, frame = cap.read() + + # If frame is read correctly ret is True + if not ret: + break + + # Convert BGR to RGB + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + frames.append(frame_rgb) + + # Release the video capture object + cap.release() + + return np.array(frames) + + +def read_audio(audio_path: str, audio_sample_rate: int = 16000): + if audio_path is None: + raise ValueError("Audio path is required.") + ar = AudioReader(audio_path, sample_rate=audio_sample_rate, mono=True) + + # To access the audio samples + audio_samples = torch.from_numpy(ar[:].asnumpy()) + audio_samples = audio_samples.squeeze(0) + + return audio_samples + + +def write_video(video_output_path: str, video_frames: np.ndarray, fps: int): + height, width = video_frames[0].shape[:2] + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) + # out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*"vp09"), fps, (width, height)) + for frame in video_frames: + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + out.write(frame) + out.release() + + +def init_dist(backend="nccl", **kwargs): + """Initializes distributed environment.""" + rank = int(os.environ["RANK"]) + num_gpus = torch.cuda.device_count() + if num_gpus == 0: + raise RuntimeError("No GPUs available for training.") + local_rank = rank % num_gpus + torch.cuda.set_device(local_rank) + dist.init_process_group(backend=backend, **kwargs) + + return local_rank + + +def zero_rank_print(s): + if dist.is_initialized() and dist.get_rank() == 0: + print("### " + s) + + +def zero_rank_log(logger, message: str): + if dist.is_initialized() and dist.get_rank() == 0: + logger.info(message) + + +def check_video_fps(video_path: str): + cam = cv2.VideoCapture(video_path) + fps = cam.get(cv2.CAP_PROP_FPS) + if fps != 25: + raise ValueError(f"Video FPS is not 25, it is {fps}. Please convert the video to 25 FPS.") + + +def one_step_sampling(ddim_scheduler, pred_noise, timesteps, x_t): + # Compute alphas, betas + alpha_prod_t = ddim_scheduler.alphas_cumprod[timesteps].to(dtype=pred_noise.dtype) + beta_prod_t = 1 - alpha_prod_t + + # 3. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (12) from https://arxiv.org/abs/2010.02502 + if ddim_scheduler.config.prediction_type == "epsilon": + beta_prod_t = beta_prod_t[:, None, None, None, None] + alpha_prod_t = alpha_prod_t[:, None, None, None, None] + pred_original_sample = (x_t - beta_prod_t ** (0.5) * pred_noise) / alpha_prod_t ** (0.5) + else: + raise NotImplementedError("This prediction type is not implemented yet") + + # Clip "predicted x_0" + if ddim_scheduler.config.clip_sample: + pred_original_sample = torch.clamp(pred_original_sample, -1, 1) + return pred_original_sample + + +def plot_loss_chart(save_path: str, *args): + # Creating the plot + plt.figure() + for loss_line in args: + plt.plot(loss_line[1], loss_line[2], label=loss_line[0]) + plt.xlabel("Step") + plt.ylabel("Loss") + plt.legend() + + # Save the figure to a file + plt.savefig(save_path) + + # Close the figure to free memory + plt.close() + + +CRED = "\033[91m" +CEND = "\033[0m" + + +def red_text(text: str): + return f"{CRED}{text}{CEND}" + + +log_loss = nn.BCELoss(reduction="none") + + +def cosine_loss(vision_embeds, audio_embeds, y): + sims = nn.functional.cosine_similarity(vision_embeds, audio_embeds) + # sims[sims!=sims] = 0 # remove nan + # sims = sims.clamp(0, 1) + loss = log_loss(sims.unsqueeze(1), y).squeeze() + return loss + + +def save_image(image, save_path): + # input size (C, H, W) + image = (image / 2 + 0.5).clamp(0, 1) + image = (image * 255).to(torch.uint8) + image = transforms.ToPILImage()(image) + # Save the image copy + image.save(save_path) + + # Close the image file + image.close() + + +def gather_loss(loss, device): + # Sum the local loss across all processes + local_loss = loss.item() + global_loss = torch.tensor(local_loss, dtype=torch.float32).to(device) + dist.all_reduce(global_loss, op=dist.ReduceOp.SUM) + + # Calculate the average loss across all processes + global_average_loss = global_loss.item() / dist.get_world_size() + return global_average_loss + + +def gather_video_paths_recursively(input_dir): + print(f"Recursively gathering video paths of {input_dir} ...") + paths = [] + gather_video_paths(input_dir, paths) + return paths + + +def gather_video_paths(input_dir, paths): + for file in sorted(os.listdir(input_dir)): + if file.endswith(".mp4"): + filepath = os.path.join(input_dir, file) + paths.append(filepath) + elif os.path.isdir(os.path.join(input_dir, file)): + gather_video_paths(os.path.join(input_dir, file), paths) + + +def count_video_time(video_path): + video = cv2.VideoCapture(video_path) + + frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT) + fps = video.get(cv2.CAP_PROP_FPS) + return frame_count / fps + + +def check_ffmpeg_installed(): + # Run the ffmpeg command with the -version argument to check if it's installed + result = subprocess.run("ffmpeg -version", stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) + if not result.returncode == 0: + raise FileNotFoundError("ffmpeg not found, please install it by:\n $ conda install -c conda-forge ffmpeg") diff --git a/latentsync/whisper/audio2feature.py b/latentsync/whisper/audio2feature.py index 8df3c4f..dfbce89 100644 --- a/latentsync/whisper/audio2feature.py +++ b/latentsync/whisper/audio2feature.py @@ -1,166 +1,162 @@ -# Adapted from https://github.com/TMElyralab/MuseTalk/blob/main/musetalk/whisper/audio2feature.py - -from .whisper import load_model -import numpy as np -import torch -import os - - -class Audio2Feature: - def __init__( - self, - model_path="checkpoints/whisper/tiny.pt", - device=None, - audio_cache_dir=None, - num_frames=16, - ): - self.model = load_model(model_path, device) - self.audio_cache_dir = audio_cache_dir - self.num_frames = num_frames - self.embedding_dim = self.model.dims.n_audio_state - - def get_sliced_feature(self, feature_array, vid_idx, audio_feat_length=[2, 2], fps=25): - """ - Get sliced features based on a given index - :param feature_array: - :param start_idx: the start index of the feature - :param audio_feat_length: - :return: - """ - length = len(feature_array) - selected_feature = [] - selected_idx = [] - - center_idx = int(vid_idx * 50 / fps) - left_idx = center_idx - audio_feat_length[0] * 2 - right_idx = center_idx + (audio_feat_length[1] + 1) * 2 - - for idx in range(left_idx, right_idx): - idx = max(0, idx) - idx = min(length - 1, idx) - x = feature_array[idx] - selected_feature.append(x) - selected_idx.append(idx) - - selected_feature = torch.cat(selected_feature, dim=0) - selected_feature = selected_feature.reshape(-1, self.embedding_dim) # 50*384 - return selected_feature, selected_idx - - def get_sliced_feature_sparse(self, feature_array, vid_idx, audio_feat_length=[2, 2], fps=25): - """ - Get sliced features based on a given index - :param feature_array: - :param start_idx: the start index of the feature - :param audio_feat_length: - :return: - """ - length = len(feature_array) - selected_feature = [] - selected_idx = [] - - for dt in range(-audio_feat_length[0], audio_feat_length[1] + 1): - left_idx = int((vid_idx + dt) * 50 / fps) - if left_idx < 1 or left_idx > length - 1: - left_idx = max(0, left_idx) - left_idx = min(length - 1, left_idx) - - x = feature_array[left_idx] - x = x[np.newaxis, :, :] - x = np.repeat(x, 2, axis=0) - selected_feature.append(x) - selected_idx.append(left_idx) - selected_idx.append(left_idx) - else: - x = feature_array[left_idx - 1 : left_idx + 1] - selected_feature.append(x) - selected_idx.append(left_idx - 1) - selected_idx.append(left_idx) - selected_feature = np.concatenate(selected_feature, axis=0) - selected_feature = selected_feature.reshape(-1, self.embedding_dim) # 50*384 - selected_feature = torch.from_numpy(selected_feature) - return selected_feature, selected_idx - - def feature2chunks(self, feature_array, fps, audio_feat_length=[2, 2]): - whisper_chunks = [] - whisper_idx_multiplier = 50.0 / fps - i = 0 - print(f"video in {fps} FPS, audio idx in 50FPS") - - while True: - start_idx = int(i * whisper_idx_multiplier) - selected_feature, selected_idx = self.get_sliced_feature( - feature_array=feature_array, vid_idx=i, audio_feat_length=audio_feat_length, fps=fps - ) - # print(f"i:{i},selected_idx {selected_idx}") - whisper_chunks.append(selected_feature) - i += 1 - if start_idx > len(feature_array): - break - - return whisper_chunks - - def _audio2feat(self, audio_path: str): - # get the sample rate of the audio - result = self.model.transcribe(audio_path) - embed_list = [] - for emb in result["segments"]: - encoder_embeddings = emb["encoder_embeddings"] - encoder_embeddings = encoder_embeddings.transpose(0, 2, 1, 3) - encoder_embeddings = encoder_embeddings.squeeze(0) - start_idx = int(emb["start"]) - end_idx = int(emb["end"]) - emb_end_idx = int((end_idx - start_idx) / 2) - embed_list.append(encoder_embeddings[:emb_end_idx]) - concatenated_array = torch.from_numpy(np.concatenate(embed_list, axis=0)) - return concatenated_array - - def audio2feat(self, audio_path): - if self.audio_cache_dir == "" or self.audio_cache_dir is None: - return self._audio2feat(audio_path) - - audio_cache_path = os.path.join(self.audio_cache_dir, os.path.basename(audio_path) + ".pt") - - if os.path.isfile(audio_cache_path): - try: - audio_feat = torch.load(audio_cache_path) - except Exception as e: - print(f"{type(e).__name__} - {e} - {audio_cache_path}") - os.remove(audio_cache_path) - audio_feat = self._audio2feat(audio_path) - torch.save(audio_feat, audio_cache_path) - else: - audio_feat = self._audio2feat(audio_path) - torch.save(audio_feat, audio_cache_path) - - return audio_feat - - def crop_overlap_audio_window(self, audio_feat, start_index): - selected_feature_list = [] - for i in range(start_index, start_index + self.num_frames): - selected_feature, selected_idx = self.get_sliced_feature( - feature_array=audio_feat, vid_idx=i, audio_feat_length=[2, 2], fps=25 - ) - selected_feature_list.append(selected_feature) - mel_overlap = torch.stack(selected_feature_list) - return mel_overlap - - -if __name__ == "__main__": - audio_encoder = Audio2Feature(model_path="checkpoints/whisper/tiny.pt") - audio_path = "./validation/hdtf_val.mp4" - array = audio_encoder.audio2feat(audio_path) - print(array.shape) - fps = 25 - whisper_idx_multiplier = 50.0 / fps - - i = 0 - print(f"video in {fps} FPS, audio idx in 50FPS") - while True: - start_idx = int(i * whisper_idx_multiplier) - selected_feature, selected_idx = audio_encoder.get_sliced_feature( - feature_array=array, vid_idx=i, audio_feat_length=[2, 2], fps=fps - ) - print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}") - i += 1 - if start_idx > len(array): - break +# Adapted from https://github.com/TMElyralab/MuseTalk/blob/main/musetalk/whisper/audio2feature.py + +from .whisper import load_model +import numpy as np +import torch +import os + + +class Audio2Feature: + def __init__( + self, + model_path="checkpoints/whisper/tiny.pt", + device=None, + audio_embeds_cache_dir=None, + num_frames=16, + audio_feat_length=[2, 2], + ): + self.model = load_model(model_path, device) + self.audio_embeds_cache_dir = audio_embeds_cache_dir + self.num_frames = num_frames + self.embedding_dim = self.model.dims.n_audio_state + self.audio_feat_length = audio_feat_length + + def get_sliced_feature(self, feature_array, vid_idx, fps=25): + """ + Get sliced features based on a given index + :param feature_array: + :param start_idx: the start index of the feature + :param audio_feat_length: + :return: + """ + length = len(feature_array) + selected_feature = [] + selected_idx = [] + + center_idx = int(vid_idx * 50 / fps) + left_idx = center_idx - self.audio_feat_length[0] * 2 + right_idx = center_idx + (self.audio_feat_length[1] + 1) * 2 + + for idx in range(left_idx, right_idx): + idx = max(0, idx) + idx = min(length - 1, idx) + x = feature_array[idx] + selected_feature.append(x) + selected_idx.append(idx) + + selected_feature = torch.cat(selected_feature, dim=0) + selected_feature = selected_feature.reshape(-1, self.embedding_dim) # 50*384 + return selected_feature, selected_idx + + def get_sliced_feature_sparse(self, feature_array, vid_idx, fps=25): + """ + Get sliced features based on a given index + :param feature_array: + :param start_idx: the start index of the feature + :param audio_feat_length: + :return: + """ + length = len(feature_array) + selected_feature = [] + selected_idx = [] + + for dt in range(-self.audio_feat_length[0], self.audio_feat_length[1] + 1): + left_idx = int((vid_idx + dt) * 50 / fps) + if left_idx < 1 or left_idx > length - 1: + left_idx = max(0, left_idx) + left_idx = min(length - 1, left_idx) + + x = feature_array[left_idx] + x = x[np.newaxis, :, :] + x = np.repeat(x, 2, axis=0) + selected_feature.append(x) + selected_idx.append(left_idx) + selected_idx.append(left_idx) + else: + x = feature_array[left_idx - 1 : left_idx + 1] + selected_feature.append(x) + selected_idx.append(left_idx - 1) + selected_idx.append(left_idx) + selected_feature = np.concatenate(selected_feature, axis=0) + selected_feature = selected_feature.reshape(-1, self.embedding_dim) # 50*384 + selected_feature = torch.from_numpy(selected_feature) + return selected_feature, selected_idx + + def feature2chunks(self, feature_array, fps): + whisper_chunks = [] + whisper_idx_multiplier = 50.0 / fps + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + + while True: + start_idx = int(i * whisper_idx_multiplier) + selected_feature, selected_idx = self.get_sliced_feature(feature_array=feature_array, vid_idx=i, fps=fps) + # print(f"i:{i},selected_idx {selected_idx}") + whisper_chunks.append(selected_feature) + i += 1 + if start_idx > len(feature_array): + break + + return whisper_chunks + + def _audio2feat(self, audio_path: str): + # get the sample rate of the audio + result = self.model.transcribe(audio_path) + embed_list = [] + for emb in result["segments"]: + encoder_embeddings = emb["encoder_embeddings"] + encoder_embeddings = encoder_embeddings.transpose(0, 2, 1, 3) + encoder_embeddings = encoder_embeddings.squeeze(0) + start_idx = int(emb["start"]) + end_idx = int(emb["end"]) + emb_end_idx = int((end_idx - start_idx) / 2) + embed_list.append(encoder_embeddings[:emb_end_idx]) + concatenated_array = torch.from_numpy(np.concatenate(embed_list, axis=0)) + return concatenated_array + + def audio2feat(self, audio_path): + if self.audio_embeds_cache_dir == "" or self.audio_embeds_cache_dir is None: + return self._audio2feat(audio_path) + + audio_embeds_cache_path = os.path.join(self.audio_embeds_cache_dir, os.path.basename(audio_path) + ".pt") + + if os.path.isfile(audio_embeds_cache_path): + try: + audio_feat = torch.load(audio_embeds_cache_path, weights_only=True) + except Exception as e: + print(f"{type(e).__name__} - {e} - {audio_embeds_cache_path}") + os.remove(audio_embeds_cache_path) + audio_feat = self._audio2feat(audio_path) + torch.save(audio_feat, audio_embeds_cache_path) + else: + audio_feat = self._audio2feat(audio_path) + torch.save(audio_feat, audio_embeds_cache_path) + + return audio_feat + + def crop_overlap_audio_window(self, audio_feat, start_index): + selected_feature_list = [] + for i in range(start_index, start_index + self.num_frames): + selected_feature, selected_idx = self.get_sliced_feature(feature_array=audio_feat, vid_idx=i, fps=25) + selected_feature_list.append(selected_feature) + mel_overlap = torch.stack(selected_feature_list) + return mel_overlap + + +if __name__ == "__main__": + audio_encoder = Audio2Feature(model_path="checkpoints/whisper/tiny.pt") + audio_path = "assets/demo1_audio.wav" + array = audio_encoder.audio2feat(audio_path) + print(array.shape) + fps = 25 + whisper_idx_multiplier = 50.0 / fps + + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + while True: + start_idx = int(i * whisper_idx_multiplier) + selected_feature, selected_idx = audio_encoder.get_sliced_feature(feature_array=array, vid_idx=i, fps=fps) + print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}") + i += 1 + if start_idx > len(array): + break diff --git a/latentsync/whisper/whisper/__init__.py b/latentsync/whisper/whisper/__init__.py index 60c88fb..b48311d 100644 --- a/latentsync/whisper/whisper/__init__.py +++ b/latentsync/whisper/whisper/__init__.py @@ -1,119 +1,122 @@ -import hashlib -import io -import os -import urllib -import warnings -from typing import List, Optional, Union - -import torch -from tqdm import tqdm - -from .audio import load_audio, log_mel_spectrogram, pad_or_trim -from .decoding import DecodingOptions, DecodingResult, decode, detect_language -from .model import Whisper, ModelDimensions -from .transcribe import transcribe - - -_MODELS = { - "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", - "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", - "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", - "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", - "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", - "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", - "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", - "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", - "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", - "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", - "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", - "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", -} - - -def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: - os.makedirs(root, exist_ok=True) - - expected_sha256 = url.split("/")[-2] - download_target = os.path.join(root, os.path.basename(url)) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError(f"{download_target} exists and is not a regular file") - - if os.path.isfile(download_target): - model_bytes = open(download_target, "rb").read() - if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: - return model_bytes if in_memory else download_target - else: - warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") - - with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm( - total=int(source.info().get("Content-Length")), ncols=80, unit="iB", unit_scale=True, unit_divisor=1024 - ) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - model_bytes = open(download_target, "rb").read() - if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: - raise RuntimeError( - "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." - ) - - return model_bytes if in_memory else download_target - - -def available_models() -> List[str]: - """Returns the names of available models""" - return list(_MODELS.keys()) - - -def load_model( - name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False -) -> Whisper: - """ - Load a Whisper ASR model - - Parameters - ---------- - name : str - one of the official model names listed by `whisper.available_models()`, or - path to a model checkpoint containing the model dimensions and the model state_dict. - device : Union[str, torch.device] - the PyTorch device to put the model into - download_root: str - path to download the model files; by default, it uses "~/.cache/whisper" - in_memory: bool - whether to preload the model weights into host memory - - Returns - ------- - model : Whisper - The Whisper ASR model instance - """ - - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - if download_root is None: - download_root = os.getenv("XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache", "whisper")) - - if name in _MODELS: - checkpoint_file = _download(_MODELS[name], download_root, in_memory) - elif os.path.isfile(name): - checkpoint_file = open(name, "rb").read() if in_memory else name - else: - raise RuntimeError(f"Model {name} not found; available models = {available_models()}") - - with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp: - checkpoint = torch.load(fp, map_location=device) - del checkpoint_file - - dims = ModelDimensions(**checkpoint["dims"]) - model = Whisper(dims) - model.load_state_dict(checkpoint["model_state_dict"]) - - return model.to(device) +import hashlib +import io +import os +import urllib +import warnings +from typing import List, Optional, Union + +import torch +from tqdm import tqdm + +from .audio import load_audio, log_mel_spectrogram, pad_or_trim +from .decoding import DecodingOptions, DecodingResult, decode, detect_language +from .model import Whisper, ModelDimensions +from .transcribe import transcribe + + +_MODELS = { + "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", + "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", + "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", + "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", + "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", + "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", + "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", + "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", + "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", +} + + +def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: + os.makedirs(root, exist_ok=True) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, os.path.basename(url)) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes if in_memory else download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length")), ncols=80, unit="iB", unit_scale=True, unit_divisor=1024 + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError( + "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + ) + + return model_bytes if in_memory else download_target + + +def available_models() -> List[str]: + """Returns the names of available models""" + return list(_MODELS.keys()) + + +def load_model( + name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False +) -> Whisper: + """ + Load a Whisper ASR model + + Parameters + ---------- + name : str + one of the official model names listed by `whisper.available_models()`, or + path to a model checkpoint containing the model dimensions and the model state_dict. + device : Union[str, torch.device] + the PyTorch device to put the model into + download_root: str + path to download the model files; by default, it uses "~/.cache/whisper" + in_memory: bool + whether to preload the model weights into host memory + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if download_root is None: + download_root = os.getenv("XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache", "whisper")) + + if name in _MODELS: + checkpoint_file = _download(_MODELS[name], download_root, in_memory) + elif os.path.isfile(name): + checkpoint_file = open(name, "rb").read() if in_memory else name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + with io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") as fp: + checkpoint = torch.load(fp, map_location=device, weights_only=True) + del checkpoint_file + + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + + del checkpoint + torch.cuda.empty_cache() + + return model.to(device) diff --git a/latentsync/whisper/whisper/__main__.py b/latentsync/whisper/whisper/__main__.py index bc9b04a..1036dbe 100644 --- a/latentsync/whisper/whisper/__main__.py +++ b/latentsync/whisper/whisper/__main__.py @@ -1,4 +1,4 @@ -from .transcribe import cli - - -cli() +from .transcribe import cli + + +cli() diff --git a/latentsync/whisper/whisper/assets/gpt2/merges.txt b/latentsync/whisper/whisper/assets/gpt2/merges.txt index 6636bda..a508d86 100644 --- a/latentsync/whisper/whisper/assets/gpt2/merges.txt +++ b/latentsync/whisper/whisper/assets/gpt2/merges.txt @@ -1,50001 +1,50001 @@ -#version: 0.2 - Trained by `huggingface/tokenizers` -Ġ t -Ġ a -h e -i n -r e -o n -Ġt he -e r -Ġ s -a t -Ġ w -Ġ o -e n -Ġ c -i t -i s -a n -o r -e s -Ġ b -e d -Ġ f -in g -Ġ p -o u -Ġa n -a l -a r -Ġt o -Ġ m -Ġo f -Ġ in -Ġ d -Ġ h -Ġan d -i c -a s -l e -Ġt h -i on -o m -l l -en t -Ġ n -Ġ l -s t -Ġ re -v e -Ġ e -r o -l y -Ġb e -Ġ g -Ġ T -c t -Ġ S -i d -o t -Ġ I -u t -e t -Ġ A -Ġ is -Ġ on -i m -a m -o w -a y -a d -s e -Ġth at -Ġ C -i g -Ġf or -a c -Ġ y -v er -u r -Ġ u -l d -Ġs t -Ġ M -' s -Ġ he -Ġ it -at ion -it h -i r -c e -Ġy ou -i l -Ġ B -Ġw h -o l -Ġ P -Ġw ith -Ġ 1 -t er -c h -Ġa s -Ġw e -Ġ ( -n d -i ll -Ġ D -i f -Ġ 2 -a g -er s -k e -Ġ " -Ġ H -e m -Ġc on -Ġ W -Ġ R -he r -Ġw as -Ġ r -o d -Ġ F -u l -at e -Ġa t -r i -p p -o re -ĠT he -Ġs e -u s -Ġp ro -Ġh a -u m -Ġa re -Ġd e -a in -an d -Ġo r -ig h -es t -is t -a b -r om -Ġ N -t h -Ġc om -Ġ G -u n -o p -0 0 -Ġ L -Ġn ot -es s -Ġe x -Ġ v -re s -Ġ E -e w -it y -an t -Ġb y -e l -o s -or t -o c -q u -Ġf rom -Ġha ve -Ġs u -i ve -ou ld -Ġs h -Ġth is -n t -r a -p e -igh t -ar t -m ent -Ġa l -u st -en d -- - -al l -Ġ O -ac k -Ġc h -Ġ le -i es -re d -ar d -â Ģ -ou t -Ġ J -Ġa b -e ar -i v -al ly -ou r -o st -g h -p t -Ġp l -as t -Ġc an -a k -om e -u d -T he -Ġh is -Ġd o -Ġg o -Ġh as -g e -' t -Ġ U -r ou -Ġs a -Ġ j -Ġb ut -Ġw or -Ġa ll -e ct -Ġ k -am e -Ġw ill -o k -Ġw he -Ġthe y -id e -0 1 -f f -ic h -p l -t her -Ġt r -. . -Ġin t -i e -u re -ag e -Ġn e -i al -a p -in e -ic e -Ġm e -Ġo ut -an s -on e -on g -ion s -Ġwh o -Ġ K -Ġu p -Ġthe ir -Ġa d -Ġ 3 -Ġu s -at ed -ou s -Ġm ore -u e -o g -ĠS t -in d -i ke -Ġs o -im e -p er -. 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n -Ġth rough -ĠT r -_ _ -Ġw ay -Ġd on -Ġ , -Ġ1 0 -as ed -Ġas s -ub lic -Ġre g -ĠA nd -i x -Ġ very -Ġin clud -ot her -Ġim p -ot h -Ġsu b -ĠâĢ Ķ -Ġbe ing -ar g -ĠW h -= = -ib le -Ġdo es -an ge -r am -Ġ 9 -er t -p s -it ed -ation al -Ġb r -Ġd own -Ġman y -ak ing -Ġc all -ur ing -it ies -Ġp h -ic s -al s -Ġde c -at ive -en er -Ġbe fore -il ity -Ġwe ll -Ġm uch -ers on -Ġth ose -Ġsu ch -Ġ ke -Ġ end -ĠB ut -as on -t ing -Ġl ong -e f -Ġth ink -y s -Ġbe l -Ġs m -it s -a x -Ġo wn -Ġpro v -Ġs et -if e -ment s -b le -w ard -Ġsh ow -Ġp res -m s -om et -Ġo b -Ġs ay -ĠS h -t s -f ul -Ġe ff -Ġg u -Ġin st -u nd -re n -c ess -Ġ ent -ĠY ou -Ġgo od -Ġst art -in ce -Ġm ade -t t -st em -ol og -u p -Ġ | -um p -Ġhe l -ver n -ul ar -u ally -Ġa c -Ġm on -Ġl ast -Ġ2 00 -1 0 -Ġst ud -u res -ĠA r -sel f -ar s -mer ic -u es -c y -Ġm in -oll ow -Ġc ol -i o -Ġm od -Ġc ount -ĠC om -he s -Ġf in -a ir -i er -âĢ Ķ -re ad -an k -at ch -e ver -Ġst r -Ġpo int -or k -ĠN ew -Ġs ur -o ol -al k -em ent -Ġus ed -ra ct -we en -Ġs ame -ou n -ĠA l -c i -Ġdiff ere -Ġwh ile ----- ---- -Ġg ame -ce pt -Ġs im -.. . -Ġin ter -e k -Ġre port -Ġpro du -Ġst ill -l ed -a h -Ġhe re -Ġwor ld -Ġth ough -Ġn um -ar ch -im es -al e -ĠS e -ĠI f -/ / -ĠL e -Ġre t -Ġre f -Ġtr ans -n er -ut ion -ter s -Ġt ake -ĠC l -Ġcon f -w ay -a ve -Ġgo ing -Ġs l -u g -ĠA meric -Ġspe c -Ġh and -Ġbet ween -ist s -ĠD e -o ot -I t -Ġe ar -Ġagain st -Ġh igh -g an -a z -at her -Ġex p -Ġo p -Ġin s -Ġg r -Ġhel p -Ġre qu -et s -in s -ĠP ro -is m -Ġf ound -l and -at a -us s -am es -Ġp erson -Ġg reat -p r -Ġs ign -ĠA n -' ve -Ġs omet -Ġs er -h ip -Ġr un -Ġ : -Ġt er -ire ct -Ġf ollow -Ġd et -ic es -Ġf ind -1 2 -Ġm em -Ġc r -e red -e x -Ġex t -ut h -en se -c o -Ġte am -v ing -ou se -as h -at t -v ed -Ġsy stem -ĠA s -d er -iv es -m in -Ġle ad -ĠB l -c ent -Ġa round -Ġgo vern -Ġc ur -vel op -an y -Ġc our -al th -ag es -iz e -Ġc ar -od e -Ġl aw -Ġre ad -' m -c on -Ġre al -Ġsupp ort -Ġ1 2 -.. .. -Ġre ally -n ess -Ġf act -Ġd ay -Ġb oth -y ing -Ġs erv -ĠF or -Ġth 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-ĠTyr ann -ĠSabb ath -ĠNich ols -Ġra pt -ih ar -Ġshe lling -Ġillum inate -Ġdent ist -ut or -ĠInteg ration -Ġwh ims -ĠLiter ary -Be aut -Ġp archment -ag ara -Br and -Ġder og -â̦ ) -ĠNor se -Ġunw itting -Ġc uc -Ġborder line -Ġupset ting -Ġrec ourse -Ġd raped -ĠRad ar -Ġcold er -ĠPep si -im inary -], [ -65 8 -V i -ĠF rem -ĠP es -Ġveter inary -ĠT ED -ĠEp idem -n ova -k id -Ġdev out -o ct -j ad -M oh -ĠP AY -Ġge ometric -Ġ3 23 -Ġcircum ference -ich ick -19 75 -ĠY uri -ĠSh all -ĠH over -un in -S pr -Ġg raft -ĠHapp iness -Ġdisadvant ages -att acks -Ġhub s -ĠStar Craft -é ĸ -Ġgall eries -ĠKor ra -Ġgrocer ies -ĠGors uch -Ġrap ists -Ġfun gi -ĠTyph oon -V ector -ĠEm press -b attle -4 68 -Ġparas ite -ĠBom ber -S G -ex ist -ĠP f -Ġun se -Ġsurge ons -B irth -ĠUn sure -ĠPrint ed -ĠBehavior al -ĠA ster -Pak istan -Ġun ethical -Ġs v -ĠIo T -Ġlay outs -P ain -Ġconst ants -ĠL W -ĠB ake -Ġtow els -Ġdeterior ation -ĠBol ivia -Ġblind ed -ĠW arden -ĠMist ress -Ġon stage -Ġcl ans -ĠB EST -19 60 -Ġant ique 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ub -Or ange -Ġfingert ips -ĠWood en -ĠK idd -ĠJ PM -ĠDam n -C ow -c odes -48 2 -Ġiniti ating -ĠEl k -ĠCut ting -Ġabsent ee -ĠV ance -ĠLil ith -G UI -Ġobsc ured -Ġdwar ves -ĠCh op -ĠB oko -Val ues -Ġmult imedia -Ġbrew ed -Reg ular -CRIP TION -ĠMort al -Ġa pex -Ġtravel er -Ġbo ils -Ġspray ing -Rep resent -ĠStars hip -4 28 -Ġdisappro val -Ġshadow y -Ġlament ed -ĠRe place -ĠFran ç -67 7 -d or -Ġunst oppable -Ġcoh orts -gy n -ĠClass ics -ĠAm ph -Ġsl uggish -ĠAdd iction -ĠPad res -Ġins cription -Ġin human -min us -ĠJere miah -at ars -Ter ror -ĠT os -ĠSh arma -ast a -c atch -Ġpl umbing -ĠTim bers -Sh ar -H al -ĠO sc -Ġcou pling -hum ans -Ġsp onge -Ġid ols -ĠSp a -ĠAdv ocate -ĠBe ats -lu a -Ġtick ing -Ġload er -ĠG ron -8 10 -Ġstim ulated -Ġside bar -ĠManufact urer -ore And -19 73 -Ġpra ises -ĠFl ores -dis able -ĠElect rical -ra ise -E th -Ġmigr ated -Ġlect urer -K ids -ĠCa vern -Ġk ettle -Ġgly c -ĠMand ela -ĠF ully -å§ « -FIN EST -Ġsquee zing -ĠRy der -amp oo -oreAnd Online -Inst oreAndOnline 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-Ġreform ing -Ġfrust rations -ĠDead pool -ĠBel ichick -ract or -ĠRagnar ok -ĠD rupal -ĠApp roximately -19 20 -ĠHub ble -arm or -ĠSar as -ĠJon as -Ġnostalg ic -Ġfeas ibility -Sah aran -Ġorb iting -Ġ9 70 -R u -Ġsh in -ĠInvestig ators -Ġinconsist encies -ĠP AN -B G -Ġgraz ing -Ġdetect ors -ĠStart up -ĠFun ny -ĠNa omi -Consider ing -Ġh og -ut f -ce mic -Ġfort ified -ĠFun ctions -Ġcod ec -nut rition -H at -" ! -micro soft -55 8 -ĠTh in -ĠA CE -Al ias -ĠO PS -p apers -P K -ãĢ İ -Ġimpro bable -N orthern -equ al -Ġlook out -Ġty res -ĠMod ified -ĠK op -Abs olutely -Ġbuild up -sil ver -Ġaud i -Ġgro tesque -ĠSab er -ĠPres byter -ON Y -Ġglac iers -ĠSho als -ĠK ass -ĠH RC -ĠNic ol -ĠL unch -ĠF oss -âĸ Ĵ -AD RA -ĠOne Plus -o ing -ground s -Ġincident al -Ġdatas ets -68 9 -ĠClarks on -Ġassemb ling -ĠCorrect ions -Ġdrink ers -Ġqual ifiers -Ġle ash -Ġunf ounded -ĠH undred -Ġkick off -T i -Ġrecon cil -ĠGr ants -ĠCompl iance -ĠDexter ity -Ġ19 06 -w arn -D allas -Max imum -n ard -av ia -be aut -ens itivity -tr ace -Ġpione ers -ĠF ract -ãĢ ı -Ġpre cept -Ġgloss y -ĠI EEE -Ac ross -Ġ6 80 -S leep -che on -Ġsatir ical -ĠMin otaur -ĠCla ude -Ġr é -ape go -Ġcar rot -ĠSem in -ino a -Ġz o -Ind ependent -Ġdiagn oses -ĠC ue -M AR -Ġrend ition -ĠK ik -Ġpath ology -Ġselect s -Link edIn -Ġass ay -ĠD res -Ġtext ual -post ed -IT AL -ĠM aul -N eal -Ġinter connected -Ġerr atic -ĠVir us -Ġ5 30 -Ġenvironmental ists -ĠP helps -Ġeng agements -ĠIN ST -Ġeconom ical -nox ious -Ġg earing -izz y -Ġfavor ably -ĠMcG ill -T erm -Ġh anged -Ġball park -ĠRe yes -Ġbe ware -ĠP sal -ĠMass acre -q i -Ġin accessible -acly sm -Ġfr ay -ill ac -Ġbitter ly -ĠCert ification -Mich igan -Ġir respective -al ore -Em pty -Ġendorse ments -Ġund et -f g -equ ipped -Ġmerc iless -ĠC ust -Ġimm ature -Ġvou cher -ĠBlack well -Ñ ı -h awk -dis ciplinary -ile e -ĠMak oto -ĠD ude -ãĥĩ ãĤ£ -Y ears -Ġin ver -Ġsh aman -ĠY ong -ip el -ell en -ĠCath y -br ids -Ġs arc -65 1 -N ear -Ġground work -Ġam az -Ġ4 15 -ĠHunting ton -hew s -ĠB ung -Ġarbit rarily -ĠW it -ĠAl berto -Ġdis qualified -best os -46 1 -Ġp c -Ġ28 4 -ro bat -Rob in -Ġh ugs -ĠTrans ition -ĠOcc asionally -Ġ3 26 -ĠWh ilst -ĠLe y -Ġspaces hip -cs v -Ġun successfully -ĠA u -le ck -ĠWing ed -ĠGrizz lies -. � -Ġne arer -ĠSorce ress -ĠInd igo -El se -8 40 -let es -Co ach -Ġup bringing -ĠK es -Ġseparat ist -Ġrac ists -Ġch ained -Ġabst inence -lear ning -Ġrein stated -Ġsymm etry -Ġremind ers -ĠChe vy -Ġm ont -Ġexempl ary -ĠT OR -Z X -Ġqual itative -ĠSt amp -ĠSav annah -ĠRoss i -Ġp aed -Ġdispens aries -ĠWall s -ĠCh ronic -Ġcompliment ary -ĠBeir ut -Ġ+ --- -igs list -Ġcrypt ographic -mas ters -ĠCap itals -Ġmax imal -Ġent ropy -Point s -Ġcombat ants -l ip -ĠGl ob -ĠB MC -ph ase -th ank -HT TP -Ġcomm uter -Ġ\( \ -.. / -ĠReg ener -ĠDO I -ĠActiv ision -Ġsl it -os al -RE M -Ġch ants -Y u -Ke ys -Bre xit -ĠFor ced -Ari zona -Ġsquad ron -IS O -ĠMal one -Ġ3 38 -Ġcontrast ing -Ġt idal -Ġlib el -Ġimpl anted -Ġupro ar -ĠC ater -Ġpropos itions -M anchester -ĠEuro s -it amin -G il -ĠEl ven -ĠSe 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", -al on -K rist -Ġmeas urable -Ġimagin able -Ġapost les -Y N -7 60 -Ġster oid -Ġspecific ity -ĠL ocated -ĠBeck er -ĠE du -ĠDiet ary -uts ch -ĠMar ilyn -Ġbl ister -ĠM EP -ĠK oz -ĠC MS -y ahoo -ĠCar ney -Ġbo asting -ĠC aleb -By te -read s -ad en -Pro blem -ĠWood ward -S we -S up -ĠK GB -Set up -Ġtac it -Ġret ribution -Ġd ues -ĠM ü -. ? -ä¸ Ń -p ots -Ġcame o -ĠP AL -educ ation -A my -like ly -g ling -Ġconstitution ally -ĠHam m -ĠSpe ak -Ġwid gets -br ate -Ġcra ppy -ĠI ter -Ġanticip ating -ĠB out -P ixel -ĠY ep -ĠLaur ie -Ġh ut -Ġbullet in -ĠSal vation -Ġch ats -ear able -Honest ly -AL TH -onse qu -c ult -isco very -ovy ch -Ġse lves -ĠSat oshi -S ounds -Ġconver gence -ĠRosen berg -19 74 -Ġnas al -Ġfull est -Ġfer ocious -x us -ist e -AM S -Ġlobb ied -Ġso othing -ĠGun n -t oday -0 24 -Ġinspir ational -ĠN BN -p b -g ewater -or ah -all owed -ĠCol iseum -Ġspecial izing -Ġinsane ly -ĠT ape -del ay -Ġt arn -ĠP ound -Ġmel anch -Ġdeploy ments -il and -Ġless en -Ġfur ry -ĠUE FA -Ġblood shed -ĠMe ier -ither ing -Ġhe irs -ĠJ aw -ax ter -ĠPublic ations -Ġal ters -int ention -ĠWinc hester -d etermination -ĠLif etime -th in -Mon ster -7 80 -Ġapprox imation -Ġsuper markets -ĠSecond s -or os -h uge -Ġb ribe -ĠLIM ITED -un ed -Ġmis interpret -ĠIn jury -Ġ3 67 -Ġthreshold s -ĠCarn ival -Ġgastro intestinal -Ġguid eline -Ġde ceived -f eatures -Ġpurported ly -ĠRon nie -ĠNew t -Ġsp acious -as us -Ġsuperhero es -ĠCyn thia -le gged -k amp -ch io -Ġth umbnail -ĠShir ley -ill ation -Ġshe ds -ĠZ y -E PA -Ġdam s -Ġy awn -n ah -ĠPe ggy -ĠE rie -ĠJu ventus -ĠF ountain -r x -don ald -al bum -ĠComp rehensive -Ġc aching -ĠU z -ulner ability -ĠPrinc iple -ĠJ ian -ing ers -cast s -ĠOs iris -ch art -t ile -ĠTiff any -ĠPatt on -ĠWh ip -Ġovers ized -J e -ĠCind erella -ĠB orders -ĠDa esh -M ah -Ġdog ma -Ġcommun ists -v u -Coun cil -Ġfresh water -Ġw ounding -Ġdeb acle -Ġyoung ster -Ġthread ed -ĠB ots -ĠSav ings -ãģ Ĥ -ol ing -oh o -Ġillum ination -M RI -Ġlo osen -tr ump -ag ency -ur ion -Ġmoment arily -ĠCh 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-ĠInqu isition -ĠP oke -Ġforecast ing -Ġcompl ains -S ense -ĠHer mes -isc overed -Ġb ible -ĠMor ph -Ġg erm -78 5 -D ON -Ġcon gen -Ġcr ane -ĠD PR -Ġrespect fully -R oom -ĠN aw -ĠDal ai -re ason -ĠAng us -Educ ation -ĠTitan ic -Ë ľ -Ġo val -un ited -Ġthird s -Ġmoist ur -ĠC PC -M iami -Ġtent acles -ĠPol aris -ex c -ex clusive -ĠPra irie -Ġcol ossal -ĠBl end -sur prisingly -ÃŃ s -Ġindo ctr -Ġbas al -ĠMP EG -und o -Spl it -Develop ment -Ġlan tern -19 71 -Ġprov ocation -Ġang uish -ĠB ind -ĠLe ia -duc ers -ipp y -conserv ancy -Ġinitial ize -ĠTw ice -ĠSu k -Ġpred ic -Ġdi ploma -Ġsoc iop -Ing redients -Ġhamm ered -ĠIr ma -Q aida -Ġglim ps -ĠB ian -Ġst acking -Ġf end -gov track -Ġun n -dem ocratic -ig ree -Ġ5 80 -Ġ29 4 -Ġstraw berry -ID ER -Ġcher ished -ĠH ots -Ġinfer red -Ġ8 08 -ĠS ocrates -O regon -ĠR oses -ĠFO IA -Ġins ensitive -Ġ40 8 -Recomm end -ĠSh ine -Ġpain staking -UG E -ĠHell er -ĠEnter prises -I OR -ad j -N RS -L G -Ġalien ated -Ġacknowled gement -ĠA UD -ĠRen eg -Ġvou chers -Ġ9 60 -Ġm oot -ĠDim ensions -Ġc abbage -B right -g at -ĠK lu -Ġlat ent -Ġz e -ĠM eng -Ġdis perse -Ġpand emonium -H Q -Ġvirt uous -ĠLoc ations -ee per -prov ided -Ġse ams -ĠW T -iz o -PR OV -Ġtit anium -Ġrecol lection -Ġcr an -Ġ7 80 -ĠN F -49 1 -64 2 -p acking -59 8 -text ure -Sp ider -fre edom -cipl ed -ĠTAM ADRA -âĻ ¦ -aut hent -ĠW ANT -r ified -Ġr ites -Ġuter us -k iss -Ġâī ¤ -Ġsk illet -Ġdis enfranch -ĠGa al -Comp an -Ġage ing -gu ide -B alt -Ġiter ator -Ġdiscretion ary -t ips -Ġprim ates -ĠTechn ique -ĠPay ments -az el -ĠR OCK -stant ial -0 60 -Ġd mg -ĠJack ets -ĠPlay off -Ġnurs ery -ĠSy mb -art on -Ġannex ation -Color ado -Ġco ils -ĠSh oes -âĦ¢ : -ĠRo z -COM PLE -ĠEve rest -ĠTri umph -J oy -G rid -à ¼ -process or -ĠPros per -ĠSever us -ĠSelect ed -r g -ĠTay yip -St ra -Ġski ing -Ġ? ) -Ġpe g -Tes la -Ġtime frame -Ġmaster mind -ĠN B -scient ific -ĠSh it -gener ic -IN TER -N UM -Ġst roll -ĠEn ix -ĠM MR -ĠE MS -m ovie -Ĥ ª -Ġminim izing -idd ling -Ġilleg itimate -Ġprot otyp -Ġpremature ly 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-Ġd o -Ġ j -Ġha ve -Ġ B -Ġa n -Ġw ith -Ġa re -Ġ r -Ġd e -Ġs e -Ġs o -Ġ v -s t -i ll -u r -Ġl i -Ġ M -es t -o d -all y -' t -us t -Ġa s -Ġ C -c e -Ġm e -Ð ° -Ð µ -i l -Ġ H -Ġw as -t er -t h -Ġc an -an t -Ġc om -ou r -ig ht -Ġ Y -at ion -ĠA nd -o l -Ġs h -Ñ Ĥ -o p -s e -Ġn ot -ĠS o -Ġn e -u n -Ġa b -Ġli ke -Ġa t -Ġ D -i e -Ġh e -Ġc on -Ġc h -o re -Ġa l -Ġo r -Ġ qu -Ġ O -om e -r a -u l -Ġ N -p p -Ġyou r -ou ld -Ġ P -Ġf r -g e -er s -' re -Ð ¸ -Ġthe y -Ġwh at -us e -Ġa ll -ĠTh e -Ġ L -es s -e m -Ġk n -Ġj ust -ar t -Ġp ro -ver y -u m -Ġl o -Ġ ì -Ġm y -o k -Ġe x -a b -Ġth ere -Ġb ut -Ġkn ow -Ġs u -Ġ G -Ñ ģ -Ġ E -Ġm a -о Ð -Ġ en -Ġab out -ĠI t -is t -Ġw or -r i -in d -Ġon e -at e -a nd -in k -Ġl e -or t -' m -Ġ F -ic h -Ñ Ģ -id e -Ġg et -Ġ out -.. . -Ġw ill -ã ģ -i ve -Ð ½ -Ġfr om -a in -ĠW e -Ġu p -p e -re s -c a -Ġ R -Ġ if -Ġp l -Ġd on -ac k -Ġ 1 -Ġ " -Ġt r -Ġ us -ĠW h -it y -Ġ J -ĠY ou -Ġh ere -h er -Ġs ome -ou g -a k -ar d -Ġgo ing -Ġu n -m ent -Ġth ink -Ġp e -en d -Ġ ( -ca use -Ġt im -as t -à © -Ġ our -Ġw ant -am e -i es -Ġ ë -u d -in e -Ġre ally -Ġt e -Ġse e -c i -Ġb y -s o -u re -os e -Ġ [ -a re -Ġm ore -a h -on e -c k -op le -а Ð -Ġthe n -Ġth ing -Ġthe m -v en -ou nd -os t -on g -e ct -Ġr ight -a g -Ġin t -Ġpe ople -Ġwh en -ou s -p l -Ġtim e -Ġ im -Ġwh o -Ġ 2 -a p -Ġbe cause -h ing -Ġn o -ic e -Ġlo ok -Ġh as -Ġw ould -Ġh ow -ac t -Ġf e -n t -oug h -Ġp r -ĠB ut -Ġs ay -Ñ ĥ -Ġn ow -Ġm an -Ġ very -Ġwor k -i z -Ġ K -i v -it t -Ġa r -e p -Ġc l -Ġwh ich -Ġc o -an s -' ve -Ġs a -f f -' ll -Ġan y -Ġa ct -Ġy e -b er -ac h -a ge -p er -Ġal so -f er -Ġthe se -Ġa d -е Ð -th er -ac e -ic k -a ke -re at -i re -u e -Ġa g -Ġ U -u ch -ion s -r y -0 0 -n a -Ġd id -Ġqu e -Ġha d -Ġe very -ĠH e -Ġl a -Ġw ay -Ġs p -b le -ĠTh is -as s -Ġthe ir -it e -Ġne ed -Ġp art -Ġw ere -Ġb ack -i p -ow n -om et -b e -as e -Ġma ke -ir st -i a -en ce -an g -an k -Ġg ot -Ġp re -Ġcon t -Ġo ther -p t -ĠTh at -o g -Ġgo od -Ġint o -al k -Ġbe en -Ġa m -Ġo ver -u ally -Ġ â -ì Ŀ -Ġu nd -h e -w ay -Ġg r -Ñ Į -Ġd if -Ġp er -Ñ ı -ĠI n -Ġt w -on d -ar s -in t -or m -Ġl ot -Ġwh ere -Ġ à -Ġ V -Ġs omet -Ð » -en s -Ġg u -Ġa c -u g -Ñ ĭ -Ä ± -Ġf irst -re e -Ġh is -itt le -Ġim p -Ġm o -a v -Ġl ittle -ĠWh at -Ġm uch -Ġ z -Ġ ê -ab le -ĠÐ ¿ -Ġp o -Ġcom p -n e -Ġd is -Ġl et -an ce -Ġh er -Ġthing s -Ġst art -ul t -Ġa pp -Ġre s -Ġf o -Ġc ould -Ġin ter -Ġth ose -Ġd es -Ġwe ll -Ġtw o -Ġk ind -x t -res s -el y -à ¤ -Ġb r -Ġth r -ĠÐ ² -Ġ i -is h -Ġdif fer -Ġ ro -ĠS t -Ġsomet hing -Ġt ake -Ġb o -y s -Ġsh e -Ġt alk -l o -Ñ ĩ -Ġe ven -Ð º -ã Ģ -ĠÐ ½ -Ġb u -ĠI f -Ġd own -ĠC h -ad e -ation s -Ġ use -or d -Ġof f -Ġact ually -Ġs pe -d u -at ed -at er -os s -n ing -à ¼ -Ġdo es -Ġ Ñģ -Ġne w -Ġb et -ve l -c ess -p le -Ġha pp -t ing -on na -Ġ es -Ġd ay -Ġon ly -ig n -k ay -s el -ent s -ou nt -i ld -i le -Ġs c -Ġh im -Ġag ain -v ing -Ġg onna -Ġcom m -Ġh el -ot her -Ġ ke -ic al -Ġ 3 -Ġe l -Ġthr ough -Ġcom e -ar k -d ay -i er -à ³ -Ġth an -ĠThe y -Ġm ay -Ġs er -í ķ -Ġc all -Ġdiffer ent -Ġsh ould -ĠTh ere -ar y -ĠN ow -ã Ĥ -th ing -w e -or y -f ter -Ġp ut -or s -i al -ë ĭ -Ġund er -Ġin c -ĠY e -u b -f orm -Ġv ide -à ¸ -ver s -Ġfe el -à ¡ -od y -f t -f ore -Ġe m -g et -Ġsa id -it ion -Ġre c -i ous -at ch -Ġtr y -Ġhel p -Ġsh ow -Ð ´ -Ġb it -u ll -Ð ² -ÑĤ о -g r -Ġpl ay -if e -a il -ĠYe ah -Ġqu est -Ġman y -Ġp ers -Ġg reat -Ã Ń -Ġ est -n g -Ġâ Ļ -t y -l a -ĠO h -Ġ × -à ® -ĠB e -ad y -Ġm ost -ct ion -ĠN o -Ġdo ing -Ġbe ing -Ġto o -c es -Ġb l -. 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+Ġmast urb +ĠS ale +ÙĬر Ø© +ĠÎ § +ĠStra ÃŁen +Ġdi kk +Ġa fore +ĠJung kook +Ġcho ciaż +ĠDebat te +Ġweird ly +Ġvia je +reg ist +H elp +Ġkind eren +Ġform ulated +Ġenf im +ĠTow ards +ко ÑĹ +iver ing +ĠдеÑĤ и +char ger +Ġpur l +Ġacadem ically +ĠNur se +Ġdel eting +ay o +Ġref usal +Ġdepict s +ĠDr acula +Ġtoast ed +ĠZomb ie +ĠSuper ior +ĠB old +Ġquizz es +Ġg le +4 50 +Ġcome ço +yn n +Ġver st +ĠO laf +Ġpom oc +ĠS ask +ë ĺ +ĠT CP +ĠProper ty +íķĺ ì£ł +à¸ľ ม +bo om +ar os +ĠÑĢоÑģÑģ ий +ĠбÑĭв аеÑĤ +åĩº åİ» +ĠìĿ´ìķ¼ ê¸°ë¥¼ +Ġcomb ien +v acc +Ġeben falls +par a +Ġз м +Ġdesper ation +ord re +Ġש׾ ×Ļ +Ġgener ously +ĠÐŀ к +Ġorb iting +> length: - array = array.index_select(dim=axis, index=torch.arange(length)) - - if array.shape[axis] < length: - pad_widths = [(0, 0)] * array.ndim - pad_widths[axis] = (0, length - array.shape[axis]) - array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) - else: - if array.shape[axis] > length: - array = array.take(indices=range(length), axis=axis) - - if array.shape[axis] < length: - pad_widths = [(0, 0)] * array.ndim - pad_widths[axis] = (0, length - array.shape[axis]) - array = np.pad(array, pad_widths) - - return array - - -@lru_cache(maxsize=None) -def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: - """ - load the mel filterbank matrix for projecting STFT into a Mel spectrogram. - Allows decoupling librosa dependency; saved using: - - np.savez_compressed( - "mel_filters.npz", - mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), - ) - """ - assert n_mels == 80, f"Unsupported n_mels: {n_mels}" - with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f: - return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) - - -def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): - """ - Compute the log-Mel spectrogram of - - Parameters - ---------- - audio: Union[str, np.ndarray, torch.Tensor], shape = (*) - The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz - - n_mels: int - The number of Mel-frequency filters, only 80 is supported - - Returns - ------- - torch.Tensor, shape = (80, n_frames) - A Tensor that contains the Mel spectrogram - """ - if not torch.is_tensor(audio): - if isinstance(audio, str): - audio = load_audio(audio) - audio = torch.from_numpy(audio) - - window = torch.hann_window(N_FFT).to(audio.device) - stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) - - magnitudes = stft[:, :-1].abs() ** 2 - - filters = mel_filters(audio.device, n_mels) - mel_spec = filters @ magnitudes - - log_spec = torch.clamp(mel_spec, min=1e-10).log10() - log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) - log_spec = (log_spec + 4.0) / 4.0 - return log_spec +import os +from functools import lru_cache +from typing import Union + +import ffmpeg +import numpy as np +import torch +import torch.nn.functional as F + +from .utils import exact_div + +# hard-coded audio hyperparameters +SAMPLE_RATE = 16000 +N_FFT = 400 +N_MELS = 80 +HOP_LENGTH = 160 +CHUNK_LENGTH = 30 +N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk +N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input + + +def load_audio(file: str, sr: int = SAMPLE_RATE): + """ + Open an audio file and read as mono waveform, resampling as necessary + + Parameters + ---------- + file: str + The audio file to open + + sr: int + The sample rate to resample the audio if necessary + + Returns + ------- + A NumPy array containing the audio waveform, in float32 dtype. + """ + try: + # This launches a subprocess to decode audio while down-mixing and resampling as necessary. + # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. + out, _ = ( + ffmpeg.input(file, threads=0) + .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr) + .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) + ) + except ffmpeg.Error as e: + raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e + + return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 + + +def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1): + """ + Pad or trim the audio array to N_SAMPLES, as expected by the encoder. + """ + if torch.is_tensor(array): + if array.shape[axis] > length: + array = array.index_select(dim=axis, index=torch.arange(length)) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + ) + """ + assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) + + +def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + + magnitudes = stft[:, :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/latentsync/whisper/whisper/decoding.py b/latentsync/whisper/whisper/decoding.py index 1835054..31816c0 100644 --- a/latentsync/whisper/whisper/decoding.py +++ b/latentsync/whisper/whisper/decoding.py @@ -1,729 +1,729 @@ -from dataclasses import dataclass, field -from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING - -import numpy as np -import torch -import torch.nn.functional as F -from torch import Tensor -from torch.distributions import Categorical - -from .audio import CHUNK_LENGTH -from .tokenizer import Tokenizer, get_tokenizer -from .utils import compression_ratio - -if TYPE_CHECKING: - from .model import Whisper - - -@torch.no_grad() -def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]: - """ - Detect the spoken language in the audio, and return them as list of strings, along with the ids - of the most probable language tokens and the probability distribution over all language tokens. - This is performed outside the main decode loop in order to not interfere with kv-caching. - - Returns - ------- - language_tokens : Tensor, shape = (n_audio,) - ids of the most probable language tokens, which appears after the startoftranscript token. - language_probs : List[Dict[str, float]], length = n_audio - list of dictionaries containing the probability distribution over all languages. - """ - if tokenizer is None: - tokenizer = get_tokenizer(model.is_multilingual) - if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: - raise ValueError(f"This model doesn't have language tokens so it can't perform lang id") - - single = mel.ndim == 2 - if single: - mel = mel.unsqueeze(0) - - # skip encoder forward pass if already-encoded audio features were given - if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): - mel = model.encoder(mel) - - # forward pass using a single token, startoftranscript - n_audio = mel.shape[0] - x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] - logits = model.logits(x, mel)[:, 0] - - # collect detected languages; suppress all non-language tokens - mask = torch.ones(logits.shape[-1], dtype=torch.bool) - mask[list(tokenizer.all_language_tokens)] = False - logits[:, mask] = -np.inf - language_tokens = logits.argmax(dim=-1) - language_token_probs = logits.softmax(dim=-1).cpu() - language_probs = [ - { - c: language_token_probs[i, j].item() - for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) - } - for i in range(n_audio) - ] - - if single: - language_tokens = language_tokens[0] - language_probs = language_probs[0] - - return language_tokens, language_probs - - -@dataclass(frozen=True) -class DecodingOptions: - task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" - language: Optional[str] = None # language that the audio is in; uses detected language if None - - # sampling-related options - temperature: float = 0.0 - sample_len: Optional[int] = None # maximum number of tokens to sample - best_of: Optional[int] = None # number of independent samples to collect, when t > 0 - beam_size: Optional[int] = None # number of beams in beam search, when t == 0 - patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424) - - # options for ranking generations (either beams or best-of-N samples) - length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm - - # prompt, prefix, and token suppression - prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context - prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context - suppress_blank: bool = True # this will suppress blank outputs - - # list of tokens ids (or comma-separated token ids) to suppress - # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` - suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" - - # timestamp sampling options - without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only - max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this - - # implementation details - fp16: bool = True # use fp16 for most of the calculation - - -@dataclass(frozen=True) -class DecodingResult: - audio_features: Tensor - language: str - encoder_embeddings: np.ndarray - decoder_embeddings: np.ndarray - language_probs: Optional[Dict[str, float]] = None - tokens: List[int] = field(default_factory=list) - text: str = "" - avg_logprob: float = np.nan - no_speech_prob: float = np.nan - temperature: float = np.nan - compression_ratio: float = np.nan - - -class Inference: - def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: - """Perform a forward pass on the decoder and return per-token logits""" - raise NotImplementedError - - def rearrange_kv_cache(self, source_indices) -> None: - """Update the key-value cache according to the updated beams""" - raise NotImplementedError - - def cleanup_caching(self) -> None: - """Clean up any resources or hooks after decoding is finished""" - pass - - -class PyTorchInference(Inference): - def __init__(self, model: "Whisper", initial_token_length: int): - self.model: "Whisper" = model - self.initial_token_length = initial_token_length - self.kv_cache = {} - self.hooks = [] - - def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor: - if not self.kv_cache: - self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() - - if tokens.shape[-1] > self.initial_token_length: - # only need to use the last token except in the first forward pass - tokens = tokens[:, -1:] - - return_val = self.model.decoder(tokens, audio_features, - kv_cache=self.kv_cache, include_embeddings=include_embeddings) - return return_val - - def cleanup_caching(self): - for hook in self.hooks: - hook.remove() - - self.kv_cache = {} - self.hooks = [] - - def rearrange_kv_cache(self, source_indices): - for module, tensor in self.kv_cache.items(): - # update the key/value cache to contain the selected sequences - self.kv_cache[module] = tensor[source_indices].detach() - - -class SequenceRanker: - def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]: - """ - Given a list of groups of samples and their cumulative log probabilities, - return the indices of the samples in each group to select as the final result - """ - raise NotImplementedError - - -class MaximumLikelihoodRanker(SequenceRanker): - """ - Select the sample with the highest log probabilities, penalized using either - a simple length normalization or Google NMT paper's length penalty - """ - - def __init__(self, length_penalty: Optional[float]): - self.length_penalty = length_penalty - - def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): - def scores(logprobs, lengths): - result = [] - for logprob, length in zip(logprobs, lengths): - if self.length_penalty is None: - penalty = length - else: - # from the Google NMT paper - penalty = ((5 + length) / 6) ** self.length_penalty - result.append(logprob / penalty) - return result - - # get the sequence with the highest score - lengths = [[len(t) for t in s] for s in tokens] - return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] - - -class TokenDecoder: - def reset(self): - """Initialize any stateful variables for decoding a new sequence""" - - def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: - """Specify how to select the next token, based on the current trace and logits - - Parameters - ---------- - tokens : Tensor, shape = (n_batch, current_sequence_length) - all tokens in the context so far, including the prefix and sot_sequence tokens - - logits : Tensor, shape = (n_batch, vocab_size) - per-token logits of the probability distribution at the current step - - sum_logprobs : Tensor, shape = (n_batch) - cumulative log probabilities for each sequence - - Returns - ------- - tokens : Tensor, shape = (n_batch, current_sequence_length + 1) - the tokens, appended with the selected next token - - completed : bool - True if all sequences has reached the end of text - - """ - raise NotImplementedError - - def finalize( - self, tokens: Tensor, sum_logprobs: Tensor - ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: - """Finalize search and return the final candidate sequences - - Parameters - ---------- - tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) - all tokens in the context so far, including the prefix and sot_sequence - - sum_logprobs : Tensor, shape = (n_audio, n_group) - cumulative log probabilities for each sequence - - Returns - ------- - tokens : Sequence[Sequence[Tensor]], length = n_audio - sequence of Tensors containing candidate token sequences, for each audio input - - sum_logprobs : List[List[float]], length = n_audio - sequence of cumulative log probabilities corresponding to the above - - """ - raise NotImplementedError - - -class GreedyDecoder(TokenDecoder): - def __init__(self, temperature: float, eot: int): - self.temperature = temperature - self.eot = eot - - def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: - temperature = self.temperature - if temperature == 0: - next_tokens = logits.argmax(dim=-1) - else: - next_tokens = Categorical(logits=logits / temperature).sample() - - logprobs = F.log_softmax(logits.float(), dim=-1) - current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] - sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) - - next_tokens[tokens[:, -1] == self.eot] = self.eot - tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) - - completed = (tokens[:, -1] == self.eot).all() - return tokens, completed - - def finalize(self, tokens: Tensor, sum_logprobs: Tensor): - # make sure each sequence has at least one EOT token at the end - tokens = F.pad(tokens, (0, 1), value=self.eot) - return tokens, sum_logprobs.tolist() - - -class BeamSearchDecoder(TokenDecoder): - def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None): - self.beam_size = beam_size - self.eot = eot - self.inference = inference - self.patience = patience or 1.0 - self.max_candidates: int = round(beam_size * self.patience) - self.finished_sequences = None - - assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" - - def reset(self): - self.finished_sequences = None - - def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: - if tokens.shape[0] % self.beam_size != 0: - raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") - - n_audio = tokens.shape[0] // self.beam_size - if self.finished_sequences is None: # for the first update - self.finished_sequences = [{} for _ in range(n_audio)] - - logprobs = F.log_softmax(logits.float(), dim=-1) - next_tokens, source_indices, finished_sequences = [], [], [] - for i in range(n_audio): - scores, sources, finished = {}, {}, {} - - # STEP 1: calculate the cumulative log probabilities for possible candidates - for j in range(self.beam_size): - idx = i * self.beam_size + j - prefix = tokens[idx].tolist() - for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): - new_logprob = (sum_logprobs[idx] + logprob).item() - sequence = tuple(prefix + [token.item()]) - scores[sequence] = new_logprob - sources[sequence] = idx - - # STEP 2: rank the candidates and keep the top beam_size sequences for each audio - saved = 0 - for sequence in sorted(scores, key=scores.get, reverse=True): - if sequence[-1] == self.eot: - finished[sequence] = scores[sequence] - else: - sum_logprobs[len(next_tokens)] = scores[sequence] - next_tokens.append(sequence) - source_indices.append(sources[sequence]) - - saved += 1 - if saved == self.beam_size: - break - - finished_sequences.append(finished) - - tokens = torch.tensor(next_tokens, device=tokens.device) - self.inference.rearrange_kv_cache(source_indices) - - # add newly finished sequences to self.finished_sequences - assert len(self.finished_sequences) == len(finished_sequences) - for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): - for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): - if len(previously_finished) >= self.max_candidates: - break # the candidate list is full - previously_finished[seq] = newly_finished[seq] - - # mark as completed if all audio has enough number of samples - completed = all( - len(sequences) >= self.max_candidates for sequences in self.finished_sequences - ) - return tokens, completed - - def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): - # collect all finished sequences, including patience, and add unfinished ones if not enough - sum_logprobs = sum_logprobs.cpu() - for i, sequences in enumerate(self.finished_sequences): - if len(sequences) < self.beam_size: # when not enough sequences are finished - for j in list(np.argsort(sum_logprobs[i]))[::-1]: - sequence = preceding_tokens[i, j].tolist() + [self.eot] - sequences[tuple(sequence)] = sum_logprobs[i][j].item() - if len(sequences) >= self.beam_size: - break - - tokens: List[List[Tensor]] = [ - [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences - ] - sum_logprobs: List[List[float]] = [ - list(sequences.values()) for sequences in self.finished_sequences - ] - return tokens, sum_logprobs - - -class LogitFilter: - def apply(self, logits: Tensor, tokens: Tensor) -> None: - """Apply any filtering or masking to logits in-place - - Parameters - ---------- - logits : Tensor, shape = (n_batch, vocab_size) - per-token logits of the probability distribution at the current step - - tokens : Tensor, shape = (n_batch, current_sequence_length) - all tokens in the context so far, including the prefix and sot_sequence tokens - - """ - raise NotImplementedError - - -class SuppressBlank(LogitFilter): - def __init__(self, tokenizer: Tokenizer, sample_begin: int): - self.tokenizer = tokenizer - self.sample_begin = sample_begin - - def apply(self, logits: Tensor, tokens: Tensor): - if tokens.shape[1] == self.sample_begin: - logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf - - -class SuppressTokens(LogitFilter): - def __init__(self, suppress_tokens: Sequence[int]): - self.suppress_tokens = list(suppress_tokens) - - def apply(self, logits: Tensor, tokens: Tensor): - logits[:, self.suppress_tokens] = -np.inf - - -class ApplyTimestampRules(LogitFilter): - def __init__( - self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] - ): - self.tokenizer = tokenizer - self.sample_begin = sample_begin - self.max_initial_timestamp_index = max_initial_timestamp_index - - def apply(self, logits: Tensor, tokens: Tensor): - # suppress <|notimestamps|> which is handled by without_timestamps - if self.tokenizer.no_timestamps is not None: - logits[:, self.tokenizer.no_timestamps] = -np.inf - - # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly - for k in range(tokens.shape[0]): - seq = [t for t in tokens[k, self.sample_begin :].tolist()] - last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin - penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin - - if last_was_timestamp: - if penultimate_was_timestamp: # has to be non-timestamp - logits[k, self.tokenizer.timestamp_begin :] = -np.inf - else: # cannot be normal text tokens - logits[k, : self.tokenizer.eot] = -np.inf - - # apply the `max_initial_timestamp` option - if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None: - last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index - logits[:, last_allowed + 1 :] = -np.inf - - # if sum of probability over timestamps is above any other token, sample timestamp - logprobs = F.log_softmax(logits.float(), dim=-1) - for k in range(tokens.shape[0]): - timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) - max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() - if timestamp_logprob > max_text_token_logprob: - logits[k, : self.tokenizer.timestamp_begin] = -np.inf - - -class DecodingTask: - inference: Inference - sequence_ranker: SequenceRanker - decoder: TokenDecoder - logit_filters: List[LogitFilter] - - def __init__(self, model: "Whisper", options: DecodingOptions): - self.model = model - - language = options.language or "en" - tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) - self.tokenizer: Tokenizer = tokenizer - self.options: DecodingOptions = self._verify_options(options) - - self.n_group: int = options.beam_size or options.best_of or 1 - self.n_ctx: int = model.dims.n_text_ctx - self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 - - self.sot_sequence: Tuple[int] = tokenizer.sot_sequence - if self.options.without_timestamps: - self.sot_sequence = tokenizer.sot_sequence_including_notimestamps - - self.initial_tokens: Tuple[int] = self._get_initial_tokens() - self.sample_begin: int = len(self.initial_tokens) - self.sot_index: int = self.initial_tokens.index(tokenizer.sot) - - # inference: implements the forward pass through the decoder, including kv caching - self.inference = PyTorchInference(model, len(self.initial_tokens)) - - # sequence ranker: implements how to rank a group of sampled sequences - self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) - - # decoder: implements how to select the next tokens, given the autoregressive distribution - if options.beam_size is not None: - self.decoder = BeamSearchDecoder( - options.beam_size, tokenizer.eot, self.inference, options.patience - ) - else: - self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) - - # logit filters: applies various rules to suppress or penalize certain tokens - self.logit_filters = [] - if self.options.suppress_blank: - self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) - if self.options.suppress_tokens: - self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) - if not options.without_timestamps: - precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds - max_initial_timestamp_index = None - if options.max_initial_timestamp: - max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision) - self.logit_filters.append( - ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index) - ) - - def _verify_options(self, options: DecodingOptions) -> DecodingOptions: - if options.beam_size is not None and options.best_of is not None: - raise ValueError("beam_size and best_of can't be given together") - if options.temperature == 0: - if options.best_of is not None: - raise ValueError("best_of with greedy sampling (T=0) is not compatible") - if options.patience is not None and options.beam_size is None: - raise ValueError("patience requires beam_size to be given") - if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): - raise ValueError("length_penalty (alpha) should be a value between 0 and 1") - - return options - - def _get_initial_tokens(self) -> Tuple[int]: - tokens = list(self.sot_sequence) - prefix = self.options.prefix - prompt = self.options.prompt - - if prefix: - prefix_tokens = ( - self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix - ) - if self.sample_len is not None: - max_prefix_len = self.n_ctx // 2 - self.sample_len - prefix_tokens = prefix_tokens[-max_prefix_len:] - tokens = tokens + prefix_tokens - - if prompt: - prompt_tokens = ( - self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt - ) - tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens - - return tuple(tokens) - - def _get_suppress_tokens(self) -> Tuple[int]: - suppress_tokens = self.options.suppress_tokens - - if isinstance(suppress_tokens, str): - suppress_tokens = [int(t) for t in suppress_tokens.split(",")] - - if -1 in suppress_tokens: - suppress_tokens = [t for t in suppress_tokens if t >= 0] - suppress_tokens.extend(self.tokenizer.non_speech_tokens) - elif suppress_tokens is None or len(suppress_tokens) == 0: - suppress_tokens = [] # interpret empty string as an empty list - else: - assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" - - suppress_tokens.extend( - [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] - ) - if self.tokenizer.no_speech is not None: - # no-speech probability is collected separately - suppress_tokens.append(self.tokenizer.no_speech) - - return tuple(sorted(set(suppress_tokens))) - - def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False): - if self.options.fp16: - mel = mel.half() - - if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state): - # encoded audio features are given; skip audio encoding - audio_features = mel - else: - result = self.model.encoder(mel, include_embeddings) - if include_embeddings: - audio_features, embeddings = result - else: - audio_features = result - - if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): - return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") - - if include_embeddings: - return audio_features, embeddings - else: - return audio_features - - def _detect_language(self, audio_features: Tensor, tokens: Tensor): - languages = [self.options.language] * audio_features.shape[0] - lang_probs = None - - if self.options.language is None or self.options.task == "lang_id": - lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) - languages = [max(probs, key=probs.get) for probs in lang_probs] - if self.options.language is None: - tokens[:, self.sot_index + 1] = lang_tokens # write language tokens - - return languages, lang_probs - - def _main_loop(self, audio_features: Tensor, tokens: Tensor): - assert audio_features.shape[0] == tokens.shape[0] - n_batch = tokens.shape[0] - sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) - no_speech_probs = [np.nan] * n_batch - - try: - embeddings = [] - for i in range(self.sample_len): - logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True) - - if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs - probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) - no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() - - # now we need to consider the logits at the last token only - logits = logits[:, -1] - token_embeddings = token_embeddings[:, :, -1] - - # Append embeddings together - embeddings.append(token_embeddings) - - # apply the logit filters, e.g. for suppressing or applying penalty to - for logit_filter in self.logit_filters: - logit_filter.apply(logits, tokens) - - # expand the tokens tensor with the selected next tokens - tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) - - if completed or tokens.shape[-1] > self.n_ctx: - break - finally: - if completed: - embeddings = embeddings[:-1] - embeddings = np.stack(embeddings, 2) - self.inference.cleanup_caching() - - return tokens, sum_logprobs, no_speech_probs, embeddings - - @torch.no_grad() - def run(self, mel: Tensor) -> List[DecodingResult]: - self.decoder.reset() - tokenizer: Tokenizer = self.tokenizer - n_audio: int = mel.shape[0] - - # encoder forward pass - forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True) - audio_features, encoder_embeddings = forward_pass - tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) - - # detect language if requested, overwriting the language token - languages, language_probs = self._detect_language(audio_features, tokens) - if self.options.task == "lang_id": - return [ - DecodingResult(audio_features=features, language=language, language_probs=probs) - for features, language, probs in zip(audio_features, languages, language_probs) - ] - - # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling - audio_features = audio_features.repeat_interleave(self.n_group, dim=0) - tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) - - # call the main sampling loop - tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens) - - # reshape the tensors to have (n_audio, n_group) as the first two dimensions - audio_features = audio_features[:: self.n_group] - no_speech_probs = no_speech_probs[:: self.n_group] - assert audio_features.shape[0] == len(no_speech_probs) == n_audio - - tokens = tokens.reshape(n_audio, self.n_group, -1) - sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) - - # get the final candidates for each group, and slice between the first sampled token and EOT - tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) - tokens: List[List[Tensor]] = [ - [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens - ] - - # select the top-ranked sample in each group - selected = self.sequence_ranker.rank(tokens, sum_logprobs) - tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] - texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] - - sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] - avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] - - fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) - if len(set(map(len, fields))) != 1: - raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") - - return [ - DecodingResult( - audio_features=features, - language=language, - tokens=tokens, - text=text, - avg_logprob=avg_logprob, - no_speech_prob=no_speech_prob, - temperature=self.options.temperature, - compression_ratio=compression_ratio(text), - encoder_embeddings=encoder_embeddings, - decoder_embeddings=decoder_embeddings - ) - for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) - ] - - -@torch.no_grad() -def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]: - """ - Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). - - Parameters - ---------- - model: Whisper - the Whisper model instance - - mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) - A tensor containing the Mel spectrogram(s) - - options: DecodingOptions - A dataclass that contains all necessary options for decoding 30-second segments - - Returns - ------- - result: Union[DecodingResult, List[DecodingResult]] - The result(s) of decoding contained in `DecodingResult` dataclass instance(s) - """ - single = mel.ndim == 2 - if single: - mel = mel.unsqueeze(0) - - result = DecodingTask(model, options).run(mel) - - if single: - result = result[0] - - return result +from dataclasses import dataclass, field +from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from .audio import CHUNK_LENGTH +from .tokenizer import Tokenizer, get_tokenizer +from .utils import compression_ratio + +if TYPE_CHECKING: + from .model import Whisper + + +@torch.no_grad() +def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer(model.is_multilingual) + if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: + raise ValueError(f"This model doesn't have language tokens so it can't perform lang id") + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" + language: Optional[str] = None # language that the audio is in; uses detected language if None + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[int] = None # number of independent samples to collect, when t > 0 + beam_size: Optional[int] = None # number of beams in beam search, when t == 0 + patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424) + + # options for ranking generations (either beams or best-of-N samples) + length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm + + # prompt, prefix, and token suppression + prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context + prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context + suppress_blank: bool = True # this will suppress blank outputs + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + encoder_embeddings: np.ndarray + decoder_embeddings: np.ndarray + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return_val = self.model.decoder(tokens, audio_features, + kv_cache=self.kv_cache, include_embeddings=include_embeddings) + return return_val + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + for module, tensor in self.kv_cache.items(): + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = tensor[source_indices].detach() + + +class SequenceRanker: + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + temperature = self.temperature + if temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if len(sequences) < self.beam_size: # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + seq = [t for t in tokens[k, self.sample_begin :].tolist()] + last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + # apply the `max_initial_timestamp` option + if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None: + last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision) + self.logit_filters.append( + ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + prefix = self.options.prefix + prompt = self.options.prompt + + if prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt + ) + tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state): + # encoded audio features are given; skip audio encoding + audio_features = mel + else: + result = self.model.encoder(mel, include_embeddings) + if include_embeddings: + audio_features, embeddings = result + else: + audio_features = result + + if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): + return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") + + if include_embeddings: + return audio_features, embeddings + else: + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + embeddings = [] + for i in range(self.sample_len): + logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True) + + if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + token_embeddings = token_embeddings[:, :, -1] + + # Append embeddings together + embeddings.append(token_embeddings) + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + if completed: + embeddings = embeddings[:-1] + embeddings = np.stack(embeddings, 2) + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs, embeddings + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + # encoder forward pass + forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True) + audio_features, encoder_embeddings = forward_pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult(audio_features=features, language=language, language_probs=probs) + for features, language, probs in zip(audio_features, languages, language_probs) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] + + fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + encoder_embeddings=encoder_embeddings, + decoder_embeddings=decoder_embeddings + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) + ] + + +@torch.no_grad() +def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + result = DecodingTask(model, options).run(mel) + + if single: + result = result[0] + + return result diff --git a/latentsync/whisper/whisper/model.py b/latentsync/whisper/whisper/model.py index acb6a0b..52010f8 100644 --- a/latentsync/whisper/whisper/model.py +++ b/latentsync/whisper/whisper/model.py @@ -1,290 +1,290 @@ -from dataclasses import dataclass -from typing import Dict -from typing import Iterable, Optional - -import numpy as np -import torch -import torch.nn.functional as F -from torch import Tensor -from torch import nn - -from .transcribe import transcribe as transcribe_function -from .decoding import detect_language as detect_language_function, decode as decode_function - - -@dataclass -class ModelDimensions: - n_mels: int - n_audio_ctx: int - n_audio_state: int - n_audio_head: int - n_audio_layer: int - n_vocab: int - n_text_ctx: int - n_text_state: int - n_text_head: int - n_text_layer: int - - -class LayerNorm(nn.LayerNorm): - def forward(self, x: Tensor) -> Tensor: - return super().forward(x.float()).type(x.dtype) - - -class Linear(nn.Linear): - def forward(self, x: Tensor) -> Tensor: - return F.linear( - x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) - ) - - -class Conv1d(nn.Conv1d): - def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: - return super()._conv_forward( - x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) - ) - - -def sinusoids(length, channels, max_timescale=10000): - """Returns sinusoids for positional embedding""" - assert channels % 2 == 0 - log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) - inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) - scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] - return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) - - -class MultiHeadAttention(nn.Module): - def __init__(self, n_state: int, n_head: int): - super().__init__() - self.n_head = n_head - self.query = Linear(n_state, n_state) - self.key = Linear(n_state, n_state, bias=False) - self.value = Linear(n_state, n_state) - self.out = Linear(n_state, n_state) - - def forward( - self, - x: Tensor, - xa: Optional[Tensor] = None, - mask: Optional[Tensor] = None, - kv_cache: Optional[dict] = None, - ): - q = self.query(x) - - if kv_cache is None or xa is None: - # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; - # otherwise, perform key/value projections for self- or cross-attention as usual. - k = self.key(x if xa is None else xa) - v = self.value(x if xa is None else xa) - else: - # for cross-attention, calculate keys and values once and reuse in subsequent calls. - k = kv_cache.get(self.key, self.key(xa)) - v = kv_cache.get(self.value, self.value(xa)) - - wv = self.qkv_attention(q, k, v, mask) - return self.out(wv) - - def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): - n_batch, n_ctx, n_state = q.shape - scale = (n_state // self.n_head) ** -0.25 - q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale - k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale - v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) - - qk = q @ k - if mask is not None: - qk = qk + mask[:n_ctx, :n_ctx] - - w = F.softmax(qk.float(), dim=-1).to(q.dtype) - return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2) - - -class ResidualAttentionBlock(nn.Module): - def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): - super().__init__() - - self.attn = MultiHeadAttention(n_state, n_head) - self.attn_ln = LayerNorm(n_state) - - self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None - self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None - - n_mlp = n_state * 4 - self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) - self.mlp_ln = LayerNorm(n_state) - - def forward( - self, - x: Tensor, - xa: Optional[Tensor] = None, - mask: Optional[Tensor] = None, - kv_cache: Optional[dict] = None, - ): - x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache) - if self.cross_attn: - x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache) - x = x + self.mlp(self.mlp_ln(x)) - return x - - -class AudioEncoder(nn.Module): - def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): - super().__init__() - self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) - self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) - self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] - ) - self.ln_post = LayerNorm(n_state) - - def forward(self, x: Tensor, include_embeddings: bool = False): - """ - x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) - the mel spectrogram of the audio - include_embeddings: bool - whether to include intermediate steps in the output - """ - x = F.gelu(self.conv1(x)) - x = F.gelu(self.conv2(x)) - x = x.permute(0, 2, 1) - - assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" - x = (x + self.positional_embedding).to(x.dtype) - - if include_embeddings: - embeddings = [x.cpu().detach().numpy()] - - for block in self.blocks: - x = block(x) - if include_embeddings: - embeddings.append(x.cpu().detach().numpy()) - - x = self.ln_post(x) - - if include_embeddings: - embeddings = np.stack(embeddings, axis=1) - return x, embeddings - else: - return x - - -class TextDecoder(nn.Module): - def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): - super().__init__() - - self.token_embedding = nn.Embedding(n_vocab, n_state) - self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] - ) - self.ln = LayerNorm(n_state) - - mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) - self.register_buffer("mask", mask, persistent=False) - - def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False): - """ - x : torch.LongTensor, shape = (batch_size, <= n_ctx) - the text tokens - xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) - the encoded audio features to be attended on - include_embeddings : bool - Whether to include intermediate values in the output to this function - """ - offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 - x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] - x = x.to(xa.dtype) - - if include_embeddings: - embeddings = [x.cpu().detach().numpy()] - - for block in self.blocks: - x = block(x, xa, mask=self.mask, kv_cache=kv_cache) - if include_embeddings: - embeddings.append(x.cpu().detach().numpy()) - - x = self.ln(x) - logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() - - if include_embeddings: - embeddings = np.stack(embeddings, axis=1) - return logits, embeddings - else: - return logits - - -class Whisper(nn.Module): - def __init__(self, dims: ModelDimensions): - super().__init__() - self.dims = dims - self.encoder = AudioEncoder( - self.dims.n_mels, - self.dims.n_audio_ctx, - self.dims.n_audio_state, - self.dims.n_audio_head, - self.dims.n_audio_layer, - ) - self.decoder = TextDecoder( - self.dims.n_vocab, - self.dims.n_text_ctx, - self.dims.n_text_state, - self.dims.n_text_head, - self.dims.n_text_layer, - ) - - def embed_audio(self, mel: torch.Tensor): - return self.encoder.forward(mel) - - def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): - return self.decoder.forward(tokens, audio_features) - - def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: - return self.decoder(tokens, self.encoder(mel)) - - @property - def device(self): - return next(self.parameters()).device - - @property - def is_multilingual(self): - return self.dims.n_vocab == 51865 - - def install_kv_cache_hooks(self, cache: Optional[dict] = None): - """ - The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value - tensors calculated for the previous positions. This method returns a dictionary that stores - all caches, and the necessary hooks for the key and value projection modules that save the - intermediate tensors to be reused during later calculations. - - Returns - ------- - cache : Dict[nn.Module, torch.Tensor] - A dictionary object mapping the key/value projection modules to its cache - hooks : List[RemovableHandle] - List of PyTorch RemovableHandle objects to stop the hooks to be called - """ - cache = {**cache} if cache is not None else {} - hooks = [] - - def save_to_cache(module, _, output): - if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: - cache[module] = output # save as-is, for the first token or cross attention - else: - cache[module] = torch.cat([cache[module], output], dim=1).detach() - return cache[module] - - def install_hooks(layer: nn.Module): - if isinstance(layer, MultiHeadAttention): - hooks.append(layer.key.register_forward_hook(save_to_cache)) - hooks.append(layer.value.register_forward_hook(save_to_cache)) - - self.decoder.apply(install_hooks) - return cache, hooks - - detect_language = detect_language_function - transcribe = transcribe_function - decode = decode_function +from dataclasses import dataclass +from typing import Dict +from typing import Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch import nn + +from .transcribe import transcribe as transcribe_function +from .decoding import detect_language as detect_language_function, decode as decode_function + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache.get(self.key, self.key(xa)) + v = kv_cache.get(self.value, self.value(xa)) + + wv = self.qkv_attention(q, k, v, mask) + return self.out(wv) + + def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + + w = F.softmax(qk.float(), dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache) + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache) + x = x + self.mlp(self.mlp_ln(x)) + return x + + +class AudioEncoder(nn.Module): + def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor, include_embeddings: bool = False): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + include_embeddings: bool + whether to include intermediate steps in the output + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" + x = (x + self.positional_embedding).to(x.dtype) + + if include_embeddings: + embeddings = [x.cpu().detach().numpy()] + + for block in self.blocks: + x = block(x) + if include_embeddings: + embeddings.append(x.cpu().detach().numpy()) + + x = self.ln_post(x) + + if include_embeddings: + embeddings = np.stack(embeddings, axis=1) + return x, embeddings + else: + return x + + +class TextDecoder(nn.Module): + def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + the encoded audio features to be attended on + include_embeddings : bool + Whether to include intermediate values in the output to this function + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] + x = x.to(xa.dtype) + + if include_embeddings: + embeddings = [x.cpu().detach().numpy()] + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + if include_embeddings: + embeddings.append(x.cpu().detach().numpy()) + + x = self.ln(x) + logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() + + if include_embeddings: + embeddings = np.stack(embeddings, axis=1) + return logits, embeddings + else: + return logits + + +class Whisper(nn.Module): + def __init__(self, dims: ModelDimensions): + super().__init__() + self.dims = dims + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder.forward(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder.forward(tokens, audio_features) + + def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab == 51865 + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: + cache[module] = output # save as-is, for the first token or cross attention + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + transcribe = transcribe_function + decode = decode_function diff --git a/latentsync/whisper/whisper/normalizers/__init__.py b/latentsync/whisper/whisper/normalizers/__init__.py index 0b10d5a..a3d3b6e 100644 --- a/latentsync/whisper/whisper/normalizers/__init__.py +++ b/latentsync/whisper/whisper/normalizers/__init__.py @@ -1,2 +1,2 @@ -from .basic import BasicTextNormalizer -from .english import EnglishTextNormalizer +from .basic import BasicTextNormalizer +from .english import EnglishTextNormalizer diff --git a/latentsync/whisper/whisper/normalizers/basic.py b/latentsync/whisper/whisper/normalizers/basic.py index ef8d249..289a1a9 100644 --- a/latentsync/whisper/whisper/normalizers/basic.py +++ b/latentsync/whisper/whisper/normalizers/basic.py @@ -1,71 +1,71 @@ -import re -import unicodedata - -import regex - -# non-ASCII letters that are not separated by "NFKD" normalization -ADDITIONAL_DIACRITICS = { - "œ": "oe", - "Œ": "OE", - "ø": "o", - "Ø": "O", - "æ": "ae", - "Æ": "AE", - "ß": "ss", - "ẞ": "SS", - "đ": "d", - "Đ": "D", - "ð": "d", - "Ð": "D", - "þ": "th", - "Þ": "th", - "ł": "l", - "Ł": "L", -} - - -def remove_symbols_and_diacritics(s: str, keep=""): - """ - Replace any other markers, symbols, and punctuations with a space, - and drop any diacritics (category 'Mn' and some manual mappings) - """ - return "".join( - c - if c in keep - else ADDITIONAL_DIACRITICS[c] - if c in ADDITIONAL_DIACRITICS - else "" - if unicodedata.category(c) == "Mn" - else " " - if unicodedata.category(c)[0] in "MSP" - else c - for c in unicodedata.normalize("NFKD", s) - ) - - -def remove_symbols(s: str): - """ - Replace any other markers, symbols, punctuations with a space, keeping diacritics - """ - return "".join( - " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s) - ) - - -class BasicTextNormalizer: - def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): - self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols - self.split_letters = split_letters - - def __call__(self, s: str): - s = s.lower() - s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets - s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis - s = self.clean(s).lower() - - if self.split_letters: - s = " ".join(regex.findall(r"\X", s, regex.U)) - - s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space - - return s +import re +import unicodedata + +import regex + +# non-ASCII letters that are not separated by "NFKD" normalization +ADDITIONAL_DIACRITICS = { + "œ": "oe", + "Œ": "OE", + "ø": "o", + "Ø": "O", + "æ": "ae", + "Æ": "AE", + "ß": "ss", + "ẞ": "SS", + "đ": "d", + "Đ": "D", + "ð": "d", + "Ð": "D", + "þ": "th", + "Þ": "th", + "ł": "l", + "Ł": "L", +} + + +def remove_symbols_and_diacritics(s: str, keep=""): + """ + Replace any other markers, symbols, and punctuations with a space, + and drop any diacritics (category 'Mn' and some manual mappings) + """ + return "".join( + c + if c in keep + else ADDITIONAL_DIACRITICS[c] + if c in ADDITIONAL_DIACRITICS + else "" + if unicodedata.category(c) == "Mn" + else " " + if unicodedata.category(c)[0] in "MSP" + else c + for c in unicodedata.normalize("NFKD", s) + ) + + +def remove_symbols(s: str): + """ + Replace any other markers, symbols, punctuations with a space, keeping diacritics + """ + return "".join( + " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s) + ) + + +class BasicTextNormalizer: + def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): + self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols + self.split_letters = split_letters + + def __call__(self, s: str): + s = s.lower() + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = self.clean(s).lower() + + if self.split_letters: + s = " ".join(regex.findall(r"\X", s, regex.U)) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + + return s diff --git a/latentsync/whisper/whisper/normalizers/english.json b/latentsync/whisper/whisper/normalizers/english.json index bd84ae7..3eec418 100644 --- a/latentsync/whisper/whisper/normalizers/english.json +++ b/latentsync/whisper/whisper/normalizers/english.json @@ -1,1742 +1,1742 @@ -{ - "accessorise": "accessorize", - "accessorised": "accessorized", - "accessorises": "accessorizes", - "accessorising": "accessorizing", - "acclimatisation": "acclimatization", - "acclimatise": "acclimatize", - "acclimatised": "acclimatized", - "acclimatises": "acclimatizes", - "acclimatising": "acclimatizing", - "accoutrements": "accouterments", - "aeon": "eon", - "aeons": "eons", - "aerogramme": "aerogram", - "aerogrammes": "aerograms", - "aeroplane": "airplane", - "aeroplanes": "airplanes", - "aesthete": "esthete", - "aesthetes": "esthetes", - "aesthetic": "esthetic", - "aesthetically": "esthetically", - "aesthetics": "esthetics", - "aetiology": "etiology", - "ageing": "aging", - "aggrandisement": "aggrandizement", - "agonise": "agonize", - "agonised": "agonized", - "agonises": "agonizes", - "agonising": "agonizing", - "agonisingly": "agonizingly", - "almanack": "almanac", - "almanacks": "almanacs", - "aluminium": "aluminum", - "amortisable": "amortizable", - "amortisation": "amortization", - "amortisations": "amortizations", - "amortise": "amortize", - "amortised": "amortized", - "amortises": "amortizes", - "amortising": "amortizing", - "amphitheatre": "amphitheater", - "amphitheatres": "amphitheaters", - "anaemia": "anemia", - "anaemic": "anemic", - "anaesthesia": "anesthesia", - "anaesthetic": "anesthetic", - "anaesthetics": "anesthetics", - "anaesthetise": "anesthetize", - "anaesthetised": "anesthetized", - "anaesthetises": "anesthetizes", - "anaesthetising": "anesthetizing", - "anaesthetist": "anesthetist", - "anaesthetists": "anesthetists", - "anaesthetize": "anesthetize", - "anaesthetized": "anesthetized", - "anaesthetizes": "anesthetizes", - "anaesthetizing": "anesthetizing", - "analogue": "analog", - "analogues": "analogs", - "analyse": "analyze", - "analysed": "analyzed", - "analyses": "analyzes", - "analysing": "analyzing", - "anglicise": "anglicize", - "anglicised": "anglicized", - "anglicises": "anglicizes", - "anglicising": "anglicizing", - "annualised": "annualized", - "antagonise": "antagonize", - "antagonised": "antagonized", - "antagonises": "antagonizes", - "antagonising": "antagonizing", - "apologise": "apologize", - "apologised": "apologized", - "apologises": "apologizes", - "apologising": "apologizing", - "appal": "appall", - "appals": "appalls", - "appetiser": "appetizer", - "appetisers": "appetizers", - "appetising": "appetizing", - "appetisingly": "appetizingly", - "arbour": "arbor", - "arbours": "arbors", - "archeological": "archaeological", - "archaeologically": "archeologically", - "archaeologist": "archeologist", - "archaeologists": "archeologists", - "archaeology": "archeology", - "ardour": "ardor", - "armour": "armor", - "armoured": "armored", - "armourer": "armorer", - "armourers": "armorers", - "armouries": "armories", - "armoury": "armory", - "artefact": "artifact", - "artefacts": "artifacts", - "authorise": "authorize", - "authorised": "authorized", - "authorises": "authorizes", - "authorising": "authorizing", - "axe": "ax", - "backpedalled": "backpedaled", - "backpedalling": "backpedaling", - "bannister": "banister", - "bannisters": "banisters", - "baptise": "baptize", - "baptised": "baptized", - "baptises": "baptizes", - "baptising": "baptizing", - "bastardise": "bastardize", - "bastardised": "bastardized", - "bastardises": "bastardizes", - "bastardising": "bastardizing", - "battleax": "battleaxe", - "baulk": "balk", - "baulked": "balked", - "baulking": "balking", - "baulks": "balks", - "bedevilled": "bedeviled", - "bedevilling": "bedeviling", - "behaviour": "behavior", - "behavioural": "behavioral", - "behaviourism": "behaviorism", - "behaviourist": "behaviorist", - "behaviourists": "behaviorists", - "behaviours": "behaviors", - "behove": "behoove", - "behoved": "behooved", - "behoves": "behooves", - "bejewelled": "bejeweled", - "belabour": "belabor", - "belaboured": "belabored", - "belabouring": "belaboring", - "belabours": "belabors", - "bevelled": "beveled", - "bevvies": "bevies", - "bevvy": "bevy", - "biassed": "biased", - "biassing": "biasing", - "bingeing": "binging", - "bougainvillaea": "bougainvillea", - "bougainvillaeas": "bougainvilleas", - "bowdlerise": "bowdlerize", - "bowdlerised": "bowdlerized", - "bowdlerises": "bowdlerizes", - "bowdlerising": "bowdlerizing", - "breathalyse": "breathalyze", - "breathalysed": "breathalyzed", - "breathalyser": "breathalyzer", - "breathalysers": "breathalyzers", - "breathalyses": "breathalyzes", - "breathalysing": "breathalyzing", - "brutalise": "brutalize", - "brutalised": "brutalized", - "brutalises": "brutalizes", - "brutalising": "brutalizing", - "busses": "buses", - "bussing": "busing", - "caesarean": "cesarean", - "caesareans": "cesareans", - "calibre": "caliber", - "calibres": "calibers", - "calliper": "caliper", - "callipers": "calipers", - "callisthenics": "calisthenics", - "canalise": "canalize", - "canalised": "canalized", - "canalises": "canalizes", - "canalising": "canalizing", - "cancelation": "cancellation", - "cancelations": "cancellations", - "cancelled": "canceled", - "cancelling": "canceling", - "candour": "candor", - "cannibalise": "cannibalize", - "cannibalised": "cannibalized", - "cannibalises": "cannibalizes", - "cannibalising": "cannibalizing", - "canonise": "canonize", - "canonised": "canonized", - "canonises": "canonizes", - "canonising": "canonizing", - "capitalise": "capitalize", - "capitalised": "capitalized", - "capitalises": "capitalizes", - "capitalising": "capitalizing", - "caramelise": "caramelize", - "caramelised": "caramelized", - "caramelises": "caramelizes", - "caramelising": "caramelizing", - "carbonise": "carbonize", - "carbonised": "carbonized", - "carbonises": "carbonizes", - "carbonising": "carbonizing", - "carolled": "caroled", - "carolling": "caroling", - "catalogue": "catalog", - "catalogued": "cataloged", - "catalogues": "catalogs", - "cataloguing": "cataloging", - "catalyse": "catalyze", - "catalysed": "catalyzed", - "catalyses": "catalyzes", - "catalysing": "catalyzing", - "categorise": "categorize", - "categorised": "categorized", - "categorises": "categorizes", - "categorising": "categorizing", - "cauterise": "cauterize", - "cauterised": "cauterized", - "cauterises": "cauterizes", - "cauterising": "cauterizing", - "cavilled": "caviled", - "cavilling": "caviling", - "centigramme": "centigram", - "centigrammes": "centigrams", - "centilitre": "centiliter", - "centilitres": "centiliters", - "centimetre": "centimeter", - "centimetres": "centimeters", - "centralise": "centralize", - "centralised": "centralized", - "centralises": "centralizes", - "centralising": "centralizing", - "centre": "center", - "centred": "centered", - "centrefold": "centerfold", - "centrefolds": "centerfolds", - "centrepiece": "centerpiece", - "centrepieces": "centerpieces", - "centres": "centers", - "channelled": "channeled", - "channelling": "channeling", - "characterise": "characterize", - "characterised": "characterized", - "characterises": "characterizes", - "characterising": "characterizing", - "cheque": "check", - "chequebook": "checkbook", - "chequebooks": "checkbooks", - "chequered": "checkered", - "cheques": "checks", - "chilli": "chili", - "chimaera": "chimera", - "chimaeras": "chimeras", - "chiselled": "chiseled", - "chiselling": "chiseling", - "circularise": "circularize", - "circularised": "circularized", - "circularises": "circularizes", - "circularising": "circularizing", - "civilise": "civilize", - "civilised": "civilized", - "civilises": "civilizes", - "civilising": "civilizing", - "clamour": "clamor", - "clamoured": "clamored", - "clamouring": "clamoring", - "clamours": "clamors", - "clangour": "clangor", - "clarinettist": "clarinetist", - "clarinettists": "clarinetists", - "collectivise": "collectivize", - "collectivised": "collectivized", - "collectivises": "collectivizes", - "collectivising": "collectivizing", - "colonisation": "colonization", - "colonise": "colonize", - "colonised": "colonized", - "coloniser": "colonizer", - "colonisers": "colonizers", - "colonises": "colonizes", - "colonising": "colonizing", - "colour": "color", - "colourant": "colorant", - "colourants": "colorants", - "coloured": "colored", - "coloureds": "coloreds", - "colourful": "colorful", - "colourfully": "colorfully", - "colouring": "coloring", - "colourize": "colorize", - "colourized": "colorized", - "colourizes": "colorizes", - "colourizing": "colorizing", - "colourless": "colorless", - "colours": "colors", - "commercialise": "commercialize", - "commercialised": "commercialized", - "commercialises": "commercializes", - "commercialising": "commercializing", - "compartmentalise": "compartmentalize", - "compartmentalised": "compartmentalized", - "compartmentalises": "compartmentalizes", - "compartmentalising": "compartmentalizing", - "computerise": "computerize", - "computerised": "computerized", - "computerises": "computerizes", - "computerising": "computerizing", - "conceptualise": "conceptualize", - "conceptualised": "conceptualized", - "conceptualises": "conceptualizes", - "conceptualising": "conceptualizing", - "connexion": "connection", - "connexions": "connections", - "contextualise": "contextualize", - "contextualised": "contextualized", - "contextualises": "contextualizes", - "contextualising": "contextualizing", - "cosier": "cozier", - "cosies": "cozies", - "cosiest": "coziest", - "cosily": "cozily", - "cosiness": "coziness", - "cosy": "cozy", - "councillor": "councilor", - "councillors": "councilors", - "counselled": "counseled", - "counselling": "counseling", - "counsellor": "counselor", - "counsellors": "counselors", - "crenelated": "crenellated", - "criminalise": "criminalize", - "criminalised": "criminalized", - "criminalises": "criminalizes", - "criminalising": "criminalizing", - "criticise": "criticize", - "criticised": "criticized", - "criticises": "criticizes", - "criticising": "criticizing", - "crueller": "crueler", - "cruellest": "cruelest", - "crystallisation": "crystallization", - "crystallise": "crystallize", - "crystallised": "crystallized", - "crystallises": "crystallizes", - "crystallising": "crystallizing", - "cudgelled": "cudgeled", - "cudgelling": "cudgeling", - "customise": "customize", - "customised": "customized", - "customises": "customizes", - "customising": "customizing", - "cypher": "cipher", - "cyphers": "ciphers", - "decentralisation": "decentralization", - "decentralise": "decentralize", - "decentralised": "decentralized", - "decentralises": "decentralizes", - "decentralising": "decentralizing", - "decriminalisation": "decriminalization", - "decriminalise": "decriminalize", - "decriminalised": "decriminalized", - "decriminalises": "decriminalizes", - "decriminalising": "decriminalizing", - "defence": "defense", - "defenceless": "defenseless", - "defences": "defenses", - "dehumanisation": "dehumanization", - "dehumanise": "dehumanize", - "dehumanised": "dehumanized", - "dehumanises": "dehumanizes", - "dehumanising": "dehumanizing", - "demeanour": "demeanor", - "demilitarisation": "demilitarization", - "demilitarise": "demilitarize", - "demilitarised": "demilitarized", - "demilitarises": "demilitarizes", - "demilitarising": "demilitarizing", - "demobilisation": "demobilization", - "demobilise": "demobilize", - "demobilised": "demobilized", - "demobilises": "demobilizes", - "demobilising": "demobilizing", - "democratisation": "democratization", - "democratise": "democratize", - "democratised": "democratized", - "democratises": "democratizes", - "democratising": "democratizing", - "demonise": "demonize", - "demonised": "demonized", - "demonises": "demonizes", - "demonising": "demonizing", - "demoralisation": "demoralization", - "demoralise": "demoralize", - "demoralised": "demoralized", - "demoralises": "demoralizes", - "demoralising": "demoralizing", - "denationalisation": "denationalization", - "denationalise": "denationalize", - "denationalised": "denationalized", - "denationalises": "denationalizes", - "denationalising": "denationalizing", - "deodorise": "deodorize", - "deodorised": "deodorized", - "deodorises": "deodorizes", - "deodorising": "deodorizing", - "depersonalise": "depersonalize", - "depersonalised": "depersonalized", - "depersonalises": "depersonalizes", - "depersonalising": "depersonalizing", - "deputise": "deputize", - "deputised": "deputized", - "deputises": "deputizes", - "deputising": "deputizing", - "desensitisation": "desensitization", - "desensitise": "desensitize", - "desensitised": "desensitized", - "desensitises": "desensitizes", - "desensitising": "desensitizing", - "destabilisation": "destabilization", - "destabilise": "destabilize", - "destabilised": "destabilized", - "destabilises": "destabilizes", - "destabilising": "destabilizing", - "dialled": "dialed", - "dialling": "dialing", - "dialogue": "dialog", - "dialogues": "dialogs", - "diarrhoea": "diarrhea", - "digitise": "digitize", - "digitised": "digitized", - "digitises": "digitizes", - "digitising": "digitizing", - "disc": "disk", - "discolour": "discolor", - "discoloured": "discolored", - "discolouring": "discoloring", - "discolours": "discolors", - "discs": "disks", - "disembowelled": "disemboweled", - "disembowelling": "disemboweling", - "disfavour": "disfavor", - "dishevelled": "disheveled", - "dishonour": "dishonor", - "dishonourable": "dishonorable", - "dishonourably": "dishonorably", - "dishonoured": "dishonored", - "dishonouring": "dishonoring", - "dishonours": "dishonors", - "disorganisation": "disorganization", - "disorganised": "disorganized", - "distil": "distill", - "distils": "distills", - "dramatisation": "dramatization", - "dramatisations": "dramatizations", - "dramatise": "dramatize", - "dramatised": "dramatized", - "dramatises": "dramatizes", - "dramatising": "dramatizing", - "draught": "draft", - "draughtboard": "draftboard", - "draughtboards": "draftboards", - "draughtier": "draftier", - "draughtiest": "draftiest", - "draughts": "drafts", - "draughtsman": "draftsman", - "draughtsmanship": "draftsmanship", - "draughtsmen": "draftsmen", - "draughtswoman": "draftswoman", - "draughtswomen": "draftswomen", - "draughty": "drafty", - "drivelled": "driveled", - "drivelling": "driveling", - "duelled": "dueled", - "duelling": "dueling", - "economise": "economize", - "economised": "economized", - "economises": "economizes", - "economising": "economizing", - "edoema": "edema", - "editorialise": "editorialize", - "editorialised": "editorialized", - "editorialises": "editorializes", - "editorialising": "editorializing", - "empathise": "empathize", - "empathised": "empathized", - "empathises": "empathizes", - "empathising": "empathizing", - "emphasise": "emphasize", - "emphasised": "emphasized", - "emphasises": "emphasizes", - "emphasising": "emphasizing", - "enamelled": "enameled", - "enamelling": "enameling", - "enamoured": "enamored", - "encyclopaedia": "encyclopedia", - "encyclopaedias": "encyclopedias", - "encyclopaedic": "encyclopedic", - "endeavour": "endeavor", - "endeavoured": "endeavored", - "endeavouring": "endeavoring", - "endeavours": "endeavors", - "energise": "energize", - "energised": "energized", - "energises": "energizes", - "energising": "energizing", - "enrol": "enroll", - "enrols": "enrolls", - "enthral": "enthrall", - "enthrals": "enthralls", - "epaulette": "epaulet", - "epaulettes": "epaulets", - "epicentre": "epicenter", - "epicentres": "epicenters", - "epilogue": "epilog", - "epilogues": "epilogs", - "epitomise": "epitomize", - "epitomised": "epitomized", - "epitomises": "epitomizes", - "epitomising": "epitomizing", - "equalisation": "equalization", - "equalise": "equalize", - "equalised": "equalized", - "equaliser": "equalizer", - "equalisers": "equalizers", - "equalises": "equalizes", - "equalising": "equalizing", - "eulogise": "eulogize", - "eulogised": "eulogized", - "eulogises": "eulogizes", - "eulogising": "eulogizing", - "evangelise": "evangelize", - "evangelised": "evangelized", - "evangelises": "evangelizes", - "evangelising": "evangelizing", - "exorcise": "exorcize", - "exorcised": "exorcized", - "exorcises": "exorcizes", - "exorcising": "exorcizing", - "extemporisation": "extemporization", - "extemporise": "extemporize", - "extemporised": "extemporized", - "extemporises": "extemporizes", - "extemporising": "extemporizing", - "externalisation": "externalization", - "externalisations": "externalizations", - "externalise": "externalize", - "externalised": "externalized", - "externalises": "externalizes", - "externalising": "externalizing", - "factorise": "factorize", - "factorised": "factorized", - "factorises": "factorizes", - "factorising": "factorizing", - "faecal": "fecal", - "faeces": "feces", - "familiarisation": "familiarization", - "familiarise": "familiarize", - "familiarised": "familiarized", - "familiarises": "familiarizes", - "familiarising": "familiarizing", - "fantasise": "fantasize", - "fantasised": "fantasized", - "fantasises": "fantasizes", - "fantasising": "fantasizing", - "favour": "favor", - "favourable": "favorable", - "favourably": "favorably", - "favoured": "favored", - "favouring": "favoring", - "favourite": "favorite", - "favourites": "favorites", - "favouritism": "favoritism", - "favours": "favors", - "feminise": "feminize", - "feminised": "feminized", - "feminises": "feminizes", - "feminising": "feminizing", - "fertilisation": "fertilization", - "fertilise": "fertilize", - "fertilised": "fertilized", - "fertiliser": "fertilizer", - "fertilisers": "fertilizers", - "fertilises": "fertilizes", - "fertilising": "fertilizing", - "fervour": "fervor", - "fibre": "fiber", - "fibreglass": "fiberglass", - "fibres": "fibers", - "fictionalisation": "fictionalization", - "fictionalisations": "fictionalizations", - "fictionalise": "fictionalize", - "fictionalised": "fictionalized", - "fictionalises": "fictionalizes", - "fictionalising": "fictionalizing", - "fillet": "filet", - "filleted": "fileted", - "filleting": "fileting", - "fillets": "filets", - "finalisation": "finalization", - "finalise": "finalize", - "finalised": "finalized", - "finalises": "finalizes", - "finalising": "finalizing", - "flautist": "flutist", - "flautists": "flutists", - "flavour": "flavor", - "flavoured": "flavored", - "flavouring": "flavoring", - "flavourings": "flavorings", - "flavourless": "flavorless", - "flavours": "flavors", - "flavoursome": "flavorsome", - "flyer / flier": "flier / flyer", - "foetal": "fetal", - "foetid": "fetid", - "foetus": "fetus", - "foetuses": "fetuses", - "formalisation": "formalization", - "formalise": "formalize", - "formalised": "formalized", - "formalises": "formalizes", - "formalising": "formalizing", - "fossilisation": "fossilization", - "fossilise": "fossilize", - "fossilised": "fossilized", - "fossilises": "fossilizes", - "fossilising": "fossilizing", - "fraternisation": "fraternization", - "fraternise": "fraternize", - "fraternised": "fraternized", - "fraternises": "fraternizes", - "fraternising": "fraternizing", - "fulfil": "fulfill", - "fulfilment": "fulfillment", - "fulfils": "fulfills", - "funnelled": "funneled", - "funnelling": "funneling", - "galvanise": "galvanize", - "galvanised": "galvanized", - "galvanises": "galvanizes", - "galvanising": "galvanizing", - "gambolled": "gamboled", - "gambolling": "gamboling", - "gaol": "jail", - "gaolbird": "jailbird", - "gaolbirds": "jailbirds", - "gaolbreak": "jailbreak", - "gaolbreaks": "jailbreaks", - "gaoled": "jailed", - "gaoler": "jailer", - "gaolers": "jailers", - "gaoling": "jailing", - "gaols": "jails", - "gasses": "gases", - "gage": "gauge", - "gaged": "gauged", - "gages": "gauges", - "gaging": "gauging", - "generalisation": "generalization", - "generalisations": "generalizations", - "generalise": "generalize", - "generalised": "generalized", - "generalises": "generalizes", - "generalising": "generalizing", - "ghettoise": "ghettoize", - "ghettoised": "ghettoized", - "ghettoises": "ghettoizes", - "ghettoising": "ghettoizing", - "gipsies": "gypsies", - "glamorise": "glamorize", - "glamorised": "glamorized", - "glamorises": "glamorizes", - "glamorising": "glamorizing", - "glamor": "glamour", - "globalisation": "globalization", - "globalise": "globalize", - "globalised": "globalized", - "globalises": "globalizes", - "globalising": "globalizing", - "glueing": "gluing", - "goitre": "goiter", - "goitres": "goiters", - "gonorrhoea": "gonorrhea", - "gramme": "gram", - "grammes": "grams", - "gravelled": "graveled", - "grey": "gray", - "greyed": "grayed", - "greying": "graying", - "greyish": "grayish", - "greyness": "grayness", - "greys": "grays", - "grovelled": "groveled", - "grovelling": "groveling", - "groyne": "groin", - "groynes": "groins", - "gruelling": "grueling", - "gruellingly": "gruelingly", - "gryphon": "griffin", - "gryphons": "griffins", - "gynaecological": "gynecological", - "gynaecologist": "gynecologist", - "gynaecologists": "gynecologists", - "gynaecology": "gynecology", - "haematological": "hematological", - "haematologist": "hematologist", - "haematologists": "hematologists", - "haematology": "hematology", - "haemoglobin": "hemoglobin", - "haemophilia": "hemophilia", - "haemophiliac": "hemophiliac", - "haemophiliacs": "hemophiliacs", - "haemorrhage": "hemorrhage", - "haemorrhaged": "hemorrhaged", - "haemorrhages": "hemorrhages", - "haemorrhaging": "hemorrhaging", - "haemorrhoids": "hemorrhoids", - "harbour": "harbor", - "harboured": "harbored", - "harbouring": "harboring", - "harbours": "harbors", - "harmonisation": "harmonization", - "harmonise": "harmonize", - "harmonised": "harmonized", - "harmonises": "harmonizes", - "harmonising": "harmonizing", - "homoeopath": "homeopath", - "homoeopathic": "homeopathic", - "homoeopaths": "homeopaths", - "homoeopathy": "homeopathy", - "homogenise": "homogenize", - "homogenised": "homogenized", - "homogenises": "homogenizes", - "homogenising": "homogenizing", - "honour": "honor", - "honourable": "honorable", - "honourably": "honorably", - "honoured": "honored", - "honouring": "honoring", - "honours": "honors", - "hospitalisation": "hospitalization", - "hospitalise": "hospitalize", - "hospitalised": "hospitalized", - "hospitalises": "hospitalizes", - "hospitalising": "hospitalizing", - "humanise": "humanize", - "humanised": "humanized", - "humanises": "humanizes", - "humanising": "humanizing", - "humour": "humor", - "humoured": "humored", - "humouring": "humoring", - "humourless": "humorless", - "humours": "humors", - "hybridise": "hybridize", - "hybridised": "hybridized", - "hybridises": "hybridizes", - "hybridising": "hybridizing", - "hypnotise": "hypnotize", - "hypnotised": "hypnotized", - "hypnotises": "hypnotizes", - "hypnotising": "hypnotizing", - "hypothesise": "hypothesize", - "hypothesised": "hypothesized", - "hypothesises": "hypothesizes", - "hypothesising": "hypothesizing", - "idealisation": "idealization", - "idealise": "idealize", - "idealised": "idealized", - "idealises": "idealizes", - "idealising": "idealizing", - "idolise": "idolize", - "idolised": "idolized", - "idolises": "idolizes", - "idolising": "idolizing", - "immobilisation": "immobilization", - "immobilise": "immobilize", - "immobilised": "immobilized", - "immobiliser": "immobilizer", - "immobilisers": "immobilizers", - "immobilises": "immobilizes", - "immobilising": "immobilizing", - "immortalise": "immortalize", - "immortalised": "immortalized", - "immortalises": "immortalizes", - "immortalising": "immortalizing", - "immunisation": "immunization", - "immunise": "immunize", - "immunised": "immunized", - "immunises": "immunizes", - "immunising": "immunizing", - "impanelled": "impaneled", - "impanelling": "impaneling", - "imperilled": "imperiled", - "imperilling": "imperiling", - "individualise": "individualize", - "individualised": "individualized", - "individualises": "individualizes", - "individualising": "individualizing", - "industrialise": "industrialize", - "industrialised": "industrialized", - "industrialises": "industrializes", - "industrialising": "industrializing", - "inflexion": "inflection", - "inflexions": "inflections", - "initialise": "initialize", - "initialised": "initialized", - "initialises": "initializes", - "initialising": "initializing", - "initialled": "initialed", - "initialling": "initialing", - "instal": "install", - "instalment": "installment", - "instalments": "installments", - "instals": "installs", - "instil": "instill", - "instils": "instills", - "institutionalisation": "institutionalization", - "institutionalise": "institutionalize", - "institutionalised": "institutionalized", - "institutionalises": "institutionalizes", - "institutionalising": "institutionalizing", - "intellectualise": "intellectualize", - "intellectualised": "intellectualized", - "intellectualises": "intellectualizes", - "intellectualising": "intellectualizing", - "internalisation": "internalization", - "internalise": "internalize", - "internalised": "internalized", - "internalises": "internalizes", - "internalising": "internalizing", - "internationalisation": "internationalization", - "internationalise": "internationalize", - "internationalised": "internationalized", - "internationalises": "internationalizes", - "internationalising": "internationalizing", - "ionisation": "ionization", - "ionise": "ionize", - "ionised": "ionized", - "ioniser": "ionizer", - "ionisers": "ionizers", - "ionises": "ionizes", - "ionising": "ionizing", - "italicise": "italicize", - "italicised": "italicized", - "italicises": "italicizes", - "italicising": "italicizing", - "itemise": "itemize", - "itemised": "itemized", - "itemises": "itemizes", - "itemising": "itemizing", - "jeopardise": "jeopardize", - "jeopardised": "jeopardized", - "jeopardises": "jeopardizes", - "jeopardising": "jeopardizing", - "jewelled": "jeweled", - "jeweller": "jeweler", - "jewellers": "jewelers", - "jewellery": "jewelry", - "judgement": "judgment", - "kilogramme": "kilogram", - "kilogrammes": "kilograms", - "kilometre": "kilometer", - "kilometres": "kilometers", - "labelled": "labeled", - "labelling": "labeling", - "labour": "labor", - "laboured": "labored", - "labourer": "laborer", - "labourers": "laborers", - "labouring": "laboring", - "labours": "labors", - "lacklustre": "lackluster", - "legalisation": "legalization", - "legalise": "legalize", - "legalised": "legalized", - "legalises": "legalizes", - "legalising": "legalizing", - "legitimise": "legitimize", - "legitimised": "legitimized", - "legitimises": "legitimizes", - "legitimising": "legitimizing", - "leukaemia": "leukemia", - "levelled": "leveled", - "leveller": "leveler", - "levellers": "levelers", - "levelling": "leveling", - "libelled": "libeled", - "libelling": "libeling", - "libellous": "libelous", - "liberalisation": "liberalization", - "liberalise": "liberalize", - "liberalised": "liberalized", - "liberalises": "liberalizes", - "liberalising": "liberalizing", - "licence": "license", - "licenced": "licensed", - "licences": "licenses", - "licencing": "licensing", - "likeable": "likable", - "lionisation": "lionization", - "lionise": "lionize", - "lionised": "lionized", - "lionises": "lionizes", - "lionising": "lionizing", - "liquidise": "liquidize", - "liquidised": "liquidized", - "liquidiser": "liquidizer", - "liquidisers": "liquidizers", - "liquidises": "liquidizes", - "liquidising": "liquidizing", - "litre": "liter", - "litres": "liters", - "localise": "localize", - "localised": "localized", - "localises": "localizes", - "localising": "localizing", - "louvre": "louver", - "louvred": "louvered", - "louvres": "louvers", - "lustre": "luster", - "magnetise": "magnetize", - "magnetised": "magnetized", - "magnetises": "magnetizes", - "magnetising": "magnetizing", - "manoeuvrability": "maneuverability", - "manoeuvrable": "maneuverable", - "manoeuvre": "maneuver", - "manoeuvred": "maneuvered", - "manoeuvres": "maneuvers", - "manoeuvring": "maneuvering", - "manoeuvrings": "maneuverings", - "marginalisation": "marginalization", - "marginalise": "marginalize", - "marginalised": "marginalized", - "marginalises": "marginalizes", - "marginalising": "marginalizing", - "marshalled": "marshaled", - "marshalling": "marshaling", - "marvelled": "marveled", - "marvelling": "marveling", - "marvellous": "marvelous", - "marvellously": "marvelously", - "materialisation": "materialization", - "materialise": "materialize", - "materialised": "materialized", - "materialises": "materializes", - "materialising": "materializing", - "maximisation": "maximization", - "maximise": "maximize", - "maximised": "maximized", - "maximises": "maximizes", - "maximising": "maximizing", - "meagre": "meager", - "mechanisation": "mechanization", - "mechanise": "mechanize", - "mechanised": "mechanized", - "mechanises": "mechanizes", - "mechanising": "mechanizing", - "mediaeval": "medieval", - "memorialise": "memorialize", - "memorialised": "memorialized", - "memorialises": "memorializes", - "memorialising": "memorializing", - "memorise": "memorize", - "memorised": "memorized", - "memorises": "memorizes", - "memorising": "memorizing", - "mesmerise": "mesmerize", - "mesmerised": "mesmerized", - "mesmerises": "mesmerizes", - "mesmerising": "mesmerizing", - "metabolise": "metabolize", - "metabolised": "metabolized", - "metabolises": "metabolizes", - "metabolising": "metabolizing", - "metre": "meter", - "metres": "meters", - "micrometre": "micrometer", - "micrometres": "micrometers", - "militarise": "militarize", - "militarised": "militarized", - "militarises": "militarizes", - "militarising": "militarizing", - "milligramme": "milligram", - "milligrammes": "milligrams", - "millilitre": "milliliter", - "millilitres": "milliliters", - "millimetre": "millimeter", - "millimetres": "millimeters", - "miniaturisation": "miniaturization", - "miniaturise": "miniaturize", - "miniaturised": "miniaturized", - "miniaturises": "miniaturizes", - "miniaturising": "miniaturizing", - "minibusses": "minibuses", - "minimise": "minimize", - "minimised": "minimized", - "minimises": "minimizes", - "minimising": "minimizing", - "misbehaviour": "misbehavior", - "misdemeanour": "misdemeanor", - "misdemeanours": "misdemeanors", - "misspelt": "misspelled", - "mitre": "miter", - "mitres": "miters", - "mobilisation": "mobilization", - "mobilise": "mobilize", - "mobilised": "mobilized", - "mobilises": "mobilizes", - "mobilising": "mobilizing", - "modelled": "modeled", - "modeller": "modeler", - "modellers": "modelers", - "modelling": "modeling", - "modernise": "modernize", - "modernised": "modernized", - "modernises": "modernizes", - "modernising": "modernizing", - "moisturise": "moisturize", - "moisturised": "moisturized", - "moisturiser": "moisturizer", - "moisturisers": "moisturizers", - "moisturises": "moisturizes", - "moisturising": "moisturizing", - "monologue": "monolog", - "monologues": "monologs", - "monopolisation": "monopolization", - "monopolise": "monopolize", - "monopolised": "monopolized", - "monopolises": "monopolizes", - "monopolising": "monopolizing", - "moralise": "moralize", - "moralised": "moralized", - "moralises": "moralizes", - "moralising": "moralizing", - "motorised": "motorized", - "mould": "mold", - "moulded": "molded", - "moulder": "molder", - "mouldered": "moldered", - "mouldering": "moldering", - "moulders": "molders", - "mouldier": "moldier", - "mouldiest": "moldiest", - "moulding": "molding", - "mouldings": "moldings", - "moulds": "molds", - "mouldy": "moldy", - "moult": "molt", - "moulted": "molted", - "moulting": "molting", - "moults": "molts", - "moustache": "mustache", - "moustached": "mustached", - "moustaches": "mustaches", - "moustachioed": "mustachioed", - "multicoloured": "multicolored", - "nationalisation": "nationalization", - "nationalisations": "nationalizations", - "nationalise": "nationalize", - "nationalised": "nationalized", - "nationalises": "nationalizes", - "nationalising": "nationalizing", - "naturalisation": "naturalization", - "naturalise": "naturalize", - "naturalised": "naturalized", - "naturalises": "naturalizes", - "naturalising": "naturalizing", - "neighbour": "neighbor", - "neighbourhood": "neighborhood", - "neighbourhoods": "neighborhoods", - "neighbouring": "neighboring", - "neighbourliness": "neighborliness", - "neighbourly": "neighborly", - "neighbours": "neighbors", - "neutralisation": "neutralization", - "neutralise": "neutralize", - "neutralised": "neutralized", - "neutralises": "neutralizes", - "neutralising": "neutralizing", - "normalisation": "normalization", - "normalise": "normalize", - "normalised": "normalized", - "normalises": "normalizes", - "normalising": "normalizing", - "odour": "odor", - "odourless": "odorless", - "odours": "odors", - "oesophagus": "esophagus", - "oesophaguses": "esophaguses", - "oestrogen": "estrogen", - "offence": "offense", - "offences": "offenses", - "omelette": "omelet", - "omelettes": "omelets", - "optimise": "optimize", - "optimised": "optimized", - "optimises": "optimizes", - "optimising": "optimizing", - "organisation": "organization", - "organisational": "organizational", - "organisations": "organizations", - "organise": "organize", - "organised": "organized", - "organiser": "organizer", - "organisers": "organizers", - "organises": "organizes", - "organising": "organizing", - "orthopaedic": "orthopedic", - "orthopaedics": "orthopedics", - "ostracise": "ostracize", - "ostracised": "ostracized", - "ostracises": "ostracizes", - "ostracising": "ostracizing", - "outmanoeuvre": "outmaneuver", - "outmanoeuvred": "outmaneuvered", - "outmanoeuvres": "outmaneuvers", - "outmanoeuvring": "outmaneuvering", - "overemphasise": "overemphasize", - "overemphasised": "overemphasized", - "overemphasises": "overemphasizes", - "overemphasising": "overemphasizing", - "oxidisation": "oxidization", - "oxidise": "oxidize", - "oxidised": "oxidized", - "oxidises": "oxidizes", - "oxidising": "oxidizing", - "paederast": "pederast", - "paederasts": "pederasts", - "paediatric": "pediatric", - "paediatrician": "pediatrician", - "paediatricians": "pediatricians", - "paediatrics": "pediatrics", - "paedophile": "pedophile", - "paedophiles": "pedophiles", - "paedophilia": "pedophilia", - "palaeolithic": "paleolithic", - "palaeontologist": "paleontologist", - "palaeontologists": "paleontologists", - "palaeontology": "paleontology", - "panelled": "paneled", - "panelling": "paneling", - "panellist": "panelist", - "panellists": "panelists", - "paralyse": "paralyze", - "paralysed": "paralyzed", - "paralyses": "paralyzes", - "paralysing": "paralyzing", - "parcelled": "parceled", - "parcelling": "parceling", - "parlour": "parlor", - "parlours": "parlors", - "particularise": "particularize", - "particularised": "particularized", - "particularises": "particularizes", - "particularising": "particularizing", - "passivisation": "passivization", - "passivise": "passivize", - "passivised": "passivized", - "passivises": "passivizes", - "passivising": "passivizing", - "pasteurisation": "pasteurization", - "pasteurise": "pasteurize", - "pasteurised": "pasteurized", - "pasteurises": "pasteurizes", - "pasteurising": "pasteurizing", - "patronise": "patronize", - "patronised": "patronized", - "patronises": "patronizes", - "patronising": "patronizing", - "patronisingly": "patronizingly", - "pedalled": "pedaled", - "pedalling": "pedaling", - "pedestrianisation": "pedestrianization", - "pedestrianise": "pedestrianize", - "pedestrianised": "pedestrianized", - "pedestrianises": "pedestrianizes", - "pedestrianising": "pedestrianizing", - "penalise": "penalize", - "penalised": "penalized", - "penalises": "penalizes", - "penalising": "penalizing", - "pencilled": "penciled", - "pencilling": "penciling", - "personalise": "personalize", - "personalised": "personalized", - "personalises": "personalizes", - "personalising": "personalizing", - "pharmacopoeia": "pharmacopeia", - "pharmacopoeias": "pharmacopeias", - "philosophise": "philosophize", - "philosophised": "philosophized", - "philosophises": "philosophizes", - "philosophising": "philosophizing", - "philtre": "filter", - "philtres": "filters", - "phoney": "phony", - "plagiarise": "plagiarize", - "plagiarised": "plagiarized", - "plagiarises": "plagiarizes", - "plagiarising": "plagiarizing", - "plough": "plow", - "ploughed": "plowed", - "ploughing": "plowing", - "ploughman": "plowman", - "ploughmen": "plowmen", - "ploughs": "plows", - "ploughshare": "plowshare", - "ploughshares": "plowshares", - "polarisation": "polarization", - "polarise": "polarize", - "polarised": "polarized", - "polarises": "polarizes", - "polarising": "polarizing", - "politicisation": "politicization", - "politicise": "politicize", - "politicised": "politicized", - "politicises": "politicizes", - "politicising": "politicizing", - "popularisation": "popularization", - "popularise": "popularize", - "popularised": "popularized", - "popularises": "popularizes", - "popularising": "popularizing", - "pouffe": "pouf", - "pouffes": "poufs", - "practise": "practice", - "practised": "practiced", - "practises": "practices", - "practising": "practicing", - "praesidium": "presidium", - "praesidiums": "presidiums", - "pressurisation": "pressurization", - "pressurise": "pressurize", - "pressurised": "pressurized", - "pressurises": "pressurizes", - "pressurising": "pressurizing", - "pretence": "pretense", - "pretences": "pretenses", - "primaeval": "primeval", - "prioritisation": "prioritization", - "prioritise": "prioritize", - "prioritised": "prioritized", - "prioritises": "prioritizes", - "prioritising": "prioritizing", - "privatisation": "privatization", - "privatisations": "privatizations", - "privatise": "privatize", - "privatised": "privatized", - "privatises": "privatizes", - "privatising": "privatizing", - "professionalisation": "professionalization", - "professionalise": "professionalize", - "professionalised": "professionalized", - "professionalises": "professionalizes", - "professionalising": "professionalizing", - "programme": "program", - "programmes": "programs", - "prologue": "prolog", - "prologues": "prologs", - "propagandise": "propagandize", - "propagandised": "propagandized", - "propagandises": "propagandizes", - "propagandising": "propagandizing", - "proselytise": "proselytize", - "proselytised": "proselytized", - "proselytiser": "proselytizer", - "proselytisers": "proselytizers", - "proselytises": "proselytizes", - "proselytising": "proselytizing", - "psychoanalyse": "psychoanalyze", - "psychoanalysed": "psychoanalyzed", - "psychoanalyses": "psychoanalyzes", - "psychoanalysing": "psychoanalyzing", - "publicise": "publicize", - "publicised": "publicized", - "publicises": "publicizes", - "publicising": "publicizing", - "pulverisation": "pulverization", - "pulverise": "pulverize", - "pulverised": "pulverized", - "pulverises": "pulverizes", - "pulverising": "pulverizing", - "pummelled": "pummel", - "pummelling": "pummeled", - "pyjama": "pajama", - "pyjamas": "pajamas", - "pzazz": "pizzazz", - "quarrelled": "quarreled", - "quarrelling": "quarreling", - "radicalise": "radicalize", - "radicalised": "radicalized", - "radicalises": "radicalizes", - "radicalising": "radicalizing", - "rancour": "rancor", - "randomise": "randomize", - "randomised": "randomized", - "randomises": "randomizes", - "randomising": "randomizing", - "rationalisation": "rationalization", - "rationalisations": "rationalizations", - "rationalise": "rationalize", - "rationalised": "rationalized", - "rationalises": "rationalizes", - "rationalising": "rationalizing", - "ravelled": "raveled", - "ravelling": "raveling", - "realisable": "realizable", - "realisation": "realization", - "realisations": "realizations", - "realise": "realize", - "realised": "realized", - "realises": "realizes", - "realising": "realizing", - "recognisable": "recognizable", - "recognisably": "recognizably", - "recognisance": "recognizance", - "recognise": "recognize", - "recognised": "recognized", - "recognises": "recognizes", - "recognising": "recognizing", - "reconnoitre": "reconnoiter", - "reconnoitred": "reconnoitered", - "reconnoitres": "reconnoiters", - "reconnoitring": "reconnoitering", - "refuelled": "refueled", - "refuelling": "refueling", - "regularisation": "regularization", - "regularise": "regularize", - "regularised": "regularized", - "regularises": "regularizes", - "regularising": "regularizing", - "remodelled": "remodeled", - "remodelling": "remodeling", - "remould": "remold", - "remoulded": "remolded", - "remoulding": "remolding", - "remoulds": "remolds", - "reorganisation": "reorganization", - "reorganisations": "reorganizations", - "reorganise": "reorganize", - "reorganised": "reorganized", - "reorganises": "reorganizes", - "reorganising": "reorganizing", - "revelled": "reveled", - "reveller": "reveler", - "revellers": "revelers", - "revelling": "reveling", - "revitalise": "revitalize", - "revitalised": "revitalized", - "revitalises": "revitalizes", - "revitalising": "revitalizing", - "revolutionise": "revolutionize", - "revolutionised": "revolutionized", - "revolutionises": "revolutionizes", - "revolutionising": "revolutionizing", - "rhapsodise": "rhapsodize", - "rhapsodised": "rhapsodized", - "rhapsodises": "rhapsodizes", - "rhapsodising": "rhapsodizing", - "rigour": "rigor", - "rigours": "rigors", - "ritualised": "ritualized", - "rivalled": "rivaled", - "rivalling": "rivaling", - "romanticise": "romanticize", - "romanticised": "romanticized", - "romanticises": "romanticizes", - "romanticising": "romanticizing", - "rumour": "rumor", - "rumoured": "rumored", - "rumours": "rumors", - "sabre": "saber", - "sabres": "sabers", - "saltpetre": "saltpeter", - "sanitise": "sanitize", - "sanitised": "sanitized", - "sanitises": "sanitizes", - "sanitising": "sanitizing", - "satirise": "satirize", - "satirised": "satirized", - "satirises": "satirizes", - "satirising": "satirizing", - "saviour": "savior", - "saviours": "saviors", - "savour": "savor", - "savoured": "savored", - "savouries": "savories", - "savouring": "savoring", - "savours": "savors", - "savoury": "savory", - "scandalise": "scandalize", - "scandalised": "scandalized", - "scandalises": "scandalizes", - "scandalising": "scandalizing", - "sceptic": "skeptic", - "sceptical": "skeptical", - "sceptically": "skeptically", - "scepticism": "skepticism", - "sceptics": "skeptics", - "sceptre": "scepter", - "sceptres": "scepters", - "scrutinise": "scrutinize", - "scrutinised": "scrutinized", - "scrutinises": "scrutinizes", - "scrutinising": "scrutinizing", - "secularisation": "secularization", - "secularise": "secularize", - "secularised": "secularized", - "secularises": "secularizes", - "secularising": "secularizing", - "sensationalise": "sensationalize", - "sensationalised": "sensationalized", - "sensationalises": "sensationalizes", - "sensationalising": "sensationalizing", - "sensitise": "sensitize", - "sensitised": "sensitized", - "sensitises": "sensitizes", - "sensitising": "sensitizing", - "sentimentalise": "sentimentalize", - "sentimentalised": "sentimentalized", - "sentimentalises": "sentimentalizes", - "sentimentalising": "sentimentalizing", - "sepulchre": "sepulcher", - "sepulchres": "sepulchers", - "serialisation": "serialization", - "serialisations": "serializations", - "serialise": "serialize", - "serialised": "serialized", - "serialises": "serializes", - "serialising": "serializing", - "sermonise": "sermonize", - "sermonised": "sermonized", - "sermonises": "sermonizes", - "sermonising": "sermonizing", - "sheikh": "sheik", - "shovelled": "shoveled", - "shovelling": "shoveling", - "shrivelled": "shriveled", - "shrivelling": "shriveling", - "signalise": "signalize", - "signalised": "signalized", - "signalises": "signalizes", - "signalising": "signalizing", - "signalled": "signaled", - "signalling": "signaling", - "smoulder": "smolder", - "smouldered": "smoldered", - "smouldering": "smoldering", - "smoulders": "smolders", - "snivelled": "sniveled", - "snivelling": "sniveling", - "snorkelled": "snorkeled", - "snorkelling": "snorkeling", - "snowplough": "snowplow", - "snowploughs": "snowplow", - "socialisation": "socialization", - "socialise": "socialize", - "socialised": "socialized", - "socialises": "socializes", - "socialising": "socializing", - "sodomise": "sodomize", - "sodomised": "sodomized", - "sodomises": "sodomizes", - "sodomising": "sodomizing", - "solemnise": "solemnize", - "solemnised": "solemnized", - "solemnises": "solemnizes", - "solemnising": "solemnizing", - "sombre": "somber", - "specialisation": "specialization", - "specialisations": "specializations", - "specialise": "specialize", - "specialised": "specialized", - "specialises": "specializes", - "specialising": "specializing", - "spectre": "specter", - "spectres": "specters", - "spiralled": "spiraled", - "spiralling": "spiraling", - "splendour": "splendor", - "splendours": "splendors", - "squirrelled": "squirreled", - "squirrelling": "squirreling", - "stabilisation": "stabilization", - "stabilise": "stabilize", - "stabilised": "stabilized", - "stabiliser": "stabilizer", - "stabilisers": "stabilizers", - "stabilises": "stabilizes", - "stabilising": "stabilizing", - "standardisation": "standardization", - "standardise": "standardize", - "standardised": "standardized", - "standardises": "standardizes", - "standardising": "standardizing", - "stencilled": "stenciled", - "stencilling": "stenciling", - "sterilisation": "sterilization", - "sterilisations": "sterilizations", - "sterilise": "sterilize", - "sterilised": "sterilized", - "steriliser": "sterilizer", - "sterilisers": "sterilizers", - "sterilises": "sterilizes", - "sterilising": "sterilizing", - "stigmatisation": "stigmatization", - "stigmatise": "stigmatize", - "stigmatised": "stigmatized", - "stigmatises": "stigmatizes", - "stigmatising": "stigmatizing", - "storey": "story", - "storeys": "stories", - "subsidisation": "subsidization", - "subsidise": "subsidize", - "subsidised": "subsidized", - "subsidiser": "subsidizer", - "subsidisers": "subsidizers", - "subsidises": "subsidizes", - "subsidising": "subsidizing", - "succour": "succor", - "succoured": "succored", - "succouring": "succoring", - "succours": "succors", - "sulphate": "sulfate", - "sulphates": "sulfates", - "sulphide": "sulfide", - "sulphides": "sulfides", - "sulphur": "sulfur", - "sulphurous": "sulfurous", - "summarise": "summarize", - "summarised": "summarized", - "summarises": "summarizes", - "summarising": "summarizing", - "swivelled": "swiveled", - "swivelling": "swiveling", - "symbolise": "symbolize", - "symbolised": "symbolized", - "symbolises": "symbolizes", - "symbolising": "symbolizing", - "sympathise": "sympathize", - "sympathised": "sympathized", - "sympathiser": "sympathizer", - "sympathisers": "sympathizers", - "sympathises": "sympathizes", - "sympathising": "sympathizing", - "synchronisation": "synchronization", - "synchronise": "synchronize", - "synchronised": "synchronized", - "synchronises": "synchronizes", - "synchronising": "synchronizing", - "synthesise": "synthesize", - "synthesised": "synthesized", - "synthesiser": "synthesizer", - "synthesisers": "synthesizers", - "synthesises": "synthesizes", - "synthesising": "synthesizing", - "syphon": "siphon", - "syphoned": "siphoned", - "syphoning": "siphoning", - "syphons": "siphons", - "systematisation": "systematization", - "systematise": "systematize", - "systematised": "systematized", - "systematises": "systematizes", - "systematising": "systematizing", - "tantalise": "tantalize", - "tantalised": "tantalized", - "tantalises": "tantalizes", - "tantalising": "tantalizing", - "tantalisingly": "tantalizingly", - "tasselled": "tasseled", - "technicolour": "technicolor", - "temporise": "temporize", - "temporised": "temporized", - "temporises": "temporizes", - "temporising": "temporizing", - "tenderise": "tenderize", - "tenderised": "tenderized", - "tenderises": "tenderizes", - "tenderising": "tenderizing", - "terrorise": "terrorize", - "terrorised": "terrorized", - "terrorises": "terrorizes", - "terrorising": "terrorizing", - "theatre": "theater", - "theatregoer": "theatergoer", - "theatregoers": "theatergoers", - "theatres": "theaters", - "theorise": "theorize", - "theorised": "theorized", - "theorises": "theorizes", - "theorising": "theorizing", - "tonne": "ton", - "tonnes": "tons", - "towelled": "toweled", - "towelling": "toweling", - "toxaemia": "toxemia", - "tranquillise": "tranquilize", - "tranquillised": "tranquilized", - "tranquilliser": "tranquilizer", - "tranquillisers": "tranquilizers", - "tranquillises": "tranquilizes", - "tranquillising": "tranquilizing", - "tranquillity": "tranquility", - "tranquillize": "tranquilize", - "tranquillized": "tranquilized", - "tranquillizer": "tranquilizer", - "tranquillizers": "tranquilizers", - "tranquillizes": "tranquilizes", - "tranquillizing": "tranquilizing", - "tranquilly": "tranquility", - "transistorised": "transistorized", - "traumatise": "traumatize", - "traumatised": "traumatized", - "traumatises": "traumatizes", - "traumatising": "traumatizing", - "travelled": "traveled", - "traveller": "traveler", - "travellers": "travelers", - "travelling": "traveling", - "travelog": "travelogue", - "travelogs": "travelogues", - "trialled": "trialed", - "trialling": "trialing", - "tricolour": "tricolor", - "tricolours": "tricolors", - "trivialise": "trivialize", - "trivialised": "trivialized", - "trivialises": "trivializes", - "trivialising": "trivializing", - "tumour": "tumor", - "tumours": "tumors", - "tunnelled": "tunneled", - "tunnelling": "tunneling", - "tyrannise": "tyrannize", - "tyrannised": "tyrannized", - "tyrannises": "tyrannizes", - "tyrannising": "tyrannizing", - "tyre": "tire", - "tyres": "tires", - "unauthorised": "unauthorized", - "uncivilised": "uncivilized", - "underutilised": "underutilized", - "unequalled": "unequaled", - "unfavourable": "unfavorable", - "unfavourably": "unfavorably", - "unionisation": "unionization", - "unionise": "unionize", - "unionised": "unionized", - "unionises": "unionizes", - "unionising": "unionizing", - "unorganised": "unorganized", - "unravelled": "unraveled", - "unravelling": "unraveling", - "unrecognisable": "unrecognizable", - "unrecognised": "unrecognized", - "unrivalled": "unrivaled", - "unsavoury": "unsavory", - "untrammelled": "untrammeled", - "urbanisation": "urbanization", - "urbanise": "urbanize", - "urbanised": "urbanized", - "urbanises": "urbanizes", - "urbanising": "urbanizing", - "utilisable": "utilizable", - "utilisation": "utilization", - "utilise": "utilize", - "utilised": "utilized", - "utilises": "utilizes", - "utilising": "utilizing", - "valour": "valor", - "vandalise": "vandalize", - "vandalised": "vandalized", - "vandalises": "vandalizes", - "vandalising": "vandalizing", - "vaporisation": "vaporization", - "vaporise": "vaporize", - "vaporised": "vaporized", - "vaporises": "vaporizes", - "vaporising": "vaporizing", - "vapour": "vapor", - "vapours": "vapors", - "verbalise": "verbalize", - "verbalised": "verbalized", - "verbalises": "verbalizes", - "verbalising": "verbalizing", - "victimisation": "victimization", - "victimise": "victimize", - "victimised": "victimized", - "victimises": "victimizes", - "victimising": "victimizing", - "videodisc": "videodisk", - "videodiscs": "videodisks", - "vigour": "vigor", - "visualisation": "visualization", - "visualisations": "visualizations", - "visualise": "visualize", - "visualised": "visualized", - "visualises": "visualizes", - "visualising": "visualizing", - "vocalisation": "vocalization", - "vocalisations": "vocalizations", - "vocalise": "vocalize", - "vocalised": "vocalized", - "vocalises": "vocalizes", - "vocalising": "vocalizing", - "vulcanised": "vulcanized", - "vulgarisation": "vulgarization", - "vulgarise": "vulgarize", - "vulgarised": "vulgarized", - "vulgarises": "vulgarizes", - "vulgarising": "vulgarizing", - "waggon": "wagon", - "waggons": "wagons", - "watercolour": "watercolor", - "watercolours": "watercolors", - "weaselled": "weaseled", - "weaselling": "weaseling", - "westernisation": "westernization", - "westernise": "westernize", - "westernised": "westernized", - "westernises": "westernizes", - "westernising": "westernizing", - "womanise": "womanize", - "womanised": "womanized", - "womaniser": "womanizer", - "womanisers": "womanizers", - "womanises": "womanizes", - "womanising": "womanizing", - "woollen": "woolen", - "woollens": "woolens", - "woollies": "woolies", - "woolly": "wooly", - "worshipped": "worshiped", - "worshipping": "worshiping", - "worshipper": "worshiper", - "yodelled": "yodeled", - "yodelling": "yodeling", - "yoghourt": "yogurt", - "yoghourts": "yogurts", - "yoghurt": "yogurt", - "yoghurts": "yogurts", - "mhm": "hmm", - "mm": "hmm", - "mmm": "hmm" +{ + "accessorise": "accessorize", + "accessorised": "accessorized", + "accessorises": "accessorizes", + "accessorising": "accessorizing", + "acclimatisation": "acclimatization", + "acclimatise": "acclimatize", + "acclimatised": "acclimatized", + "acclimatises": "acclimatizes", + "acclimatising": "acclimatizing", + "accoutrements": "accouterments", + "aeon": "eon", + "aeons": "eons", + "aerogramme": "aerogram", + "aerogrammes": "aerograms", + "aeroplane": "airplane", + "aeroplanes": "airplanes", + "aesthete": "esthete", + "aesthetes": "esthetes", + "aesthetic": "esthetic", + "aesthetically": "esthetically", + "aesthetics": "esthetics", + "aetiology": "etiology", + "ageing": "aging", + "aggrandisement": "aggrandizement", + "agonise": "agonize", + "agonised": "agonized", + "agonises": "agonizes", + "agonising": "agonizing", + "agonisingly": "agonizingly", + "almanack": "almanac", + "almanacks": "almanacs", + "aluminium": "aluminum", + "amortisable": "amortizable", + "amortisation": "amortization", + "amortisations": "amortizations", + "amortise": "amortize", + "amortised": "amortized", + "amortises": "amortizes", + "amortising": "amortizing", + "amphitheatre": "amphitheater", + "amphitheatres": "amphitheaters", + "anaemia": "anemia", + "anaemic": "anemic", + "anaesthesia": "anesthesia", + "anaesthetic": "anesthetic", + "anaesthetics": "anesthetics", + "anaesthetise": "anesthetize", + "anaesthetised": "anesthetized", + "anaesthetises": "anesthetizes", + "anaesthetising": "anesthetizing", + "anaesthetist": "anesthetist", + "anaesthetists": "anesthetists", + "anaesthetize": "anesthetize", + "anaesthetized": "anesthetized", + "anaesthetizes": "anesthetizes", + "anaesthetizing": "anesthetizing", + "analogue": "analog", + "analogues": "analogs", + "analyse": "analyze", + "analysed": "analyzed", + "analyses": "analyzes", + "analysing": "analyzing", + "anglicise": "anglicize", + "anglicised": "anglicized", + "anglicises": "anglicizes", + "anglicising": "anglicizing", + "annualised": "annualized", + "antagonise": "antagonize", + "antagonised": "antagonized", + "antagonises": "antagonizes", + "antagonising": "antagonizing", + "apologise": "apologize", + "apologised": "apologized", + "apologises": "apologizes", + "apologising": "apologizing", + "appal": "appall", + "appals": "appalls", + "appetiser": "appetizer", + "appetisers": "appetizers", + "appetising": "appetizing", + "appetisingly": "appetizingly", + "arbour": "arbor", + "arbours": "arbors", + "archeological": "archaeological", + "archaeologically": "archeologically", + "archaeologist": "archeologist", + "archaeologists": "archeologists", + "archaeology": "archeology", + "ardour": "ardor", + "armour": "armor", + "armoured": "armored", + "armourer": "armorer", + "armourers": "armorers", + "armouries": "armories", + "armoury": "armory", + "artefact": "artifact", + "artefacts": "artifacts", + "authorise": "authorize", + "authorised": "authorized", + "authorises": "authorizes", + "authorising": "authorizing", + "axe": "ax", + "backpedalled": "backpedaled", + "backpedalling": "backpedaling", + "bannister": "banister", + "bannisters": "banisters", + "baptise": "baptize", + "baptised": "baptized", + "baptises": "baptizes", + "baptising": "baptizing", + "bastardise": "bastardize", + "bastardised": "bastardized", + "bastardises": "bastardizes", + "bastardising": "bastardizing", + "battleax": "battleaxe", + "baulk": "balk", + "baulked": "balked", + "baulking": "balking", + "baulks": "balks", + "bedevilled": "bedeviled", + "bedevilling": "bedeviling", + "behaviour": "behavior", + "behavioural": "behavioral", + "behaviourism": "behaviorism", + "behaviourist": "behaviorist", + "behaviourists": "behaviorists", + "behaviours": "behaviors", + "behove": "behoove", + "behoved": "behooved", + "behoves": "behooves", + "bejewelled": "bejeweled", + "belabour": "belabor", + "belaboured": "belabored", + "belabouring": "belaboring", + "belabours": "belabors", + "bevelled": "beveled", + "bevvies": "bevies", + "bevvy": "bevy", + "biassed": "biased", + "biassing": "biasing", + "bingeing": "binging", + "bougainvillaea": "bougainvillea", + "bougainvillaeas": "bougainvilleas", + "bowdlerise": "bowdlerize", + "bowdlerised": "bowdlerized", + "bowdlerises": "bowdlerizes", + "bowdlerising": "bowdlerizing", + "breathalyse": "breathalyze", + "breathalysed": "breathalyzed", + "breathalyser": "breathalyzer", + "breathalysers": "breathalyzers", + "breathalyses": "breathalyzes", + "breathalysing": "breathalyzing", + "brutalise": "brutalize", + "brutalised": "brutalized", + "brutalises": "brutalizes", + "brutalising": "brutalizing", + "busses": "buses", + "bussing": "busing", + "caesarean": "cesarean", + "caesareans": "cesareans", + "calibre": "caliber", + "calibres": "calibers", + "calliper": "caliper", + "callipers": "calipers", + "callisthenics": "calisthenics", + "canalise": "canalize", + "canalised": "canalized", + "canalises": "canalizes", + "canalising": "canalizing", + "cancelation": "cancellation", + "cancelations": "cancellations", + "cancelled": "canceled", + "cancelling": "canceling", + "candour": "candor", + "cannibalise": "cannibalize", + "cannibalised": "cannibalized", + "cannibalises": "cannibalizes", + "cannibalising": "cannibalizing", + "canonise": "canonize", + "canonised": "canonized", + "canonises": "canonizes", + "canonising": "canonizing", + "capitalise": "capitalize", + "capitalised": "capitalized", + "capitalises": "capitalizes", + "capitalising": "capitalizing", + "caramelise": "caramelize", + "caramelised": "caramelized", + "caramelises": "caramelizes", + "caramelising": "caramelizing", + "carbonise": "carbonize", + "carbonised": "carbonized", + "carbonises": "carbonizes", + "carbonising": "carbonizing", + "carolled": "caroled", + "carolling": "caroling", + "catalogue": "catalog", + "catalogued": "cataloged", + "catalogues": "catalogs", + "cataloguing": "cataloging", + "catalyse": "catalyze", + "catalysed": "catalyzed", + "catalyses": "catalyzes", + "catalysing": "catalyzing", + "categorise": "categorize", + "categorised": "categorized", + "categorises": "categorizes", + "categorising": "categorizing", + "cauterise": "cauterize", + "cauterised": "cauterized", + "cauterises": "cauterizes", + "cauterising": "cauterizing", + "cavilled": "caviled", + "cavilling": "caviling", + "centigramme": "centigram", + "centigrammes": "centigrams", + "centilitre": "centiliter", + "centilitres": "centiliters", + "centimetre": "centimeter", + "centimetres": "centimeters", + "centralise": "centralize", + "centralised": "centralized", + "centralises": "centralizes", + "centralising": "centralizing", + "centre": "center", + "centred": "centered", + "centrefold": "centerfold", + "centrefolds": "centerfolds", + "centrepiece": "centerpiece", + "centrepieces": "centerpieces", + "centres": "centers", + "channelled": "channeled", + "channelling": "channeling", + "characterise": "characterize", + "characterised": "characterized", + "characterises": "characterizes", + "characterising": "characterizing", + "cheque": "check", + "chequebook": "checkbook", + "chequebooks": "checkbooks", + "chequered": "checkered", + "cheques": "checks", + "chilli": "chili", + "chimaera": "chimera", + "chimaeras": "chimeras", + "chiselled": "chiseled", + "chiselling": "chiseling", + "circularise": "circularize", + "circularised": "circularized", + "circularises": "circularizes", + "circularising": "circularizing", + "civilise": "civilize", + "civilised": "civilized", + "civilises": "civilizes", + "civilising": "civilizing", + "clamour": "clamor", + "clamoured": "clamored", + "clamouring": "clamoring", + "clamours": "clamors", + "clangour": "clangor", + "clarinettist": "clarinetist", + "clarinettists": "clarinetists", + "collectivise": "collectivize", + "collectivised": "collectivized", + "collectivises": "collectivizes", + "collectivising": "collectivizing", + "colonisation": "colonization", + "colonise": "colonize", + "colonised": "colonized", + "coloniser": "colonizer", + "colonisers": "colonizers", + "colonises": "colonizes", + "colonising": "colonizing", + "colour": "color", + "colourant": "colorant", + "colourants": "colorants", + "coloured": "colored", + "coloureds": "coloreds", + "colourful": "colorful", + "colourfully": "colorfully", + "colouring": "coloring", + "colourize": "colorize", + "colourized": "colorized", + "colourizes": "colorizes", + "colourizing": "colorizing", + "colourless": "colorless", + "colours": "colors", + "commercialise": "commercialize", + "commercialised": "commercialized", + "commercialises": "commercializes", + "commercialising": "commercializing", + "compartmentalise": "compartmentalize", + "compartmentalised": "compartmentalized", + "compartmentalises": "compartmentalizes", + "compartmentalising": "compartmentalizing", + "computerise": "computerize", + "computerised": "computerized", + "computerises": "computerizes", + "computerising": "computerizing", + "conceptualise": "conceptualize", + "conceptualised": "conceptualized", + "conceptualises": "conceptualizes", + "conceptualising": "conceptualizing", + "connexion": "connection", + "connexions": "connections", + "contextualise": "contextualize", + "contextualised": "contextualized", + "contextualises": "contextualizes", + "contextualising": "contextualizing", + "cosier": "cozier", + "cosies": "cozies", + "cosiest": "coziest", + "cosily": "cozily", + "cosiness": "coziness", + "cosy": "cozy", + "councillor": "councilor", + "councillors": "councilors", + "counselled": "counseled", + "counselling": "counseling", + "counsellor": "counselor", + "counsellors": "counselors", + "crenelated": "crenellated", + "criminalise": "criminalize", + "criminalised": "criminalized", + "criminalises": "criminalizes", + "criminalising": "criminalizing", + "criticise": "criticize", + "criticised": "criticized", + "criticises": "criticizes", + "criticising": "criticizing", + "crueller": "crueler", + "cruellest": "cruelest", + "crystallisation": "crystallization", + "crystallise": "crystallize", + "crystallised": "crystallized", + "crystallises": "crystallizes", + "crystallising": "crystallizing", + "cudgelled": "cudgeled", + "cudgelling": "cudgeling", + "customise": "customize", + "customised": "customized", + "customises": "customizes", + "customising": "customizing", + "cypher": "cipher", + "cyphers": "ciphers", + "decentralisation": "decentralization", + "decentralise": "decentralize", + "decentralised": "decentralized", + "decentralises": "decentralizes", + "decentralising": "decentralizing", + "decriminalisation": "decriminalization", + "decriminalise": "decriminalize", + "decriminalised": "decriminalized", + "decriminalises": "decriminalizes", + "decriminalising": "decriminalizing", + "defence": "defense", + "defenceless": "defenseless", + "defences": "defenses", + "dehumanisation": "dehumanization", + "dehumanise": "dehumanize", + "dehumanised": "dehumanized", + "dehumanises": "dehumanizes", + "dehumanising": "dehumanizing", + "demeanour": "demeanor", + "demilitarisation": "demilitarization", + "demilitarise": "demilitarize", + "demilitarised": "demilitarized", + "demilitarises": "demilitarizes", + "demilitarising": "demilitarizing", + "demobilisation": "demobilization", + "demobilise": "demobilize", + "demobilised": "demobilized", + "demobilises": "demobilizes", + "demobilising": "demobilizing", + "democratisation": "democratization", + "democratise": "democratize", + "democratised": "democratized", + "democratises": "democratizes", + "democratising": "democratizing", + "demonise": "demonize", + "demonised": "demonized", + "demonises": "demonizes", + "demonising": "demonizing", + "demoralisation": "demoralization", + "demoralise": "demoralize", + "demoralised": "demoralized", + "demoralises": "demoralizes", + "demoralising": "demoralizing", + "denationalisation": "denationalization", + "denationalise": "denationalize", + "denationalised": "denationalized", + "denationalises": "denationalizes", + "denationalising": "denationalizing", + "deodorise": "deodorize", + "deodorised": "deodorized", + "deodorises": "deodorizes", + "deodorising": "deodorizing", + "depersonalise": "depersonalize", + "depersonalised": "depersonalized", + "depersonalises": "depersonalizes", + "depersonalising": "depersonalizing", + "deputise": "deputize", + "deputised": "deputized", + "deputises": "deputizes", + "deputising": "deputizing", + "desensitisation": "desensitization", + "desensitise": "desensitize", + "desensitised": "desensitized", + "desensitises": "desensitizes", + "desensitising": "desensitizing", + "destabilisation": "destabilization", + "destabilise": "destabilize", + "destabilised": "destabilized", + "destabilises": "destabilizes", + "destabilising": "destabilizing", + "dialled": "dialed", + "dialling": "dialing", + "dialogue": "dialog", + "dialogues": "dialogs", + "diarrhoea": "diarrhea", + "digitise": "digitize", + "digitised": "digitized", + "digitises": "digitizes", + "digitising": "digitizing", + "disc": "disk", + "discolour": "discolor", + "discoloured": "discolored", + "discolouring": "discoloring", + "discolours": "discolors", + "discs": "disks", + "disembowelled": "disemboweled", + "disembowelling": "disemboweling", + "disfavour": "disfavor", + "dishevelled": "disheveled", + "dishonour": "dishonor", + "dishonourable": "dishonorable", + "dishonourably": "dishonorably", + "dishonoured": "dishonored", + "dishonouring": "dishonoring", + "dishonours": "dishonors", + "disorganisation": "disorganization", + "disorganised": "disorganized", + "distil": "distill", + "distils": "distills", + "dramatisation": "dramatization", + "dramatisations": "dramatizations", + "dramatise": "dramatize", + "dramatised": "dramatized", + "dramatises": "dramatizes", + "dramatising": "dramatizing", + "draught": "draft", + "draughtboard": "draftboard", + "draughtboards": "draftboards", + "draughtier": "draftier", + "draughtiest": "draftiest", + "draughts": "drafts", + "draughtsman": "draftsman", + "draughtsmanship": "draftsmanship", + "draughtsmen": "draftsmen", + "draughtswoman": "draftswoman", + "draughtswomen": "draftswomen", + "draughty": "drafty", + "drivelled": "driveled", + "drivelling": "driveling", + "duelled": "dueled", + "duelling": "dueling", + "economise": "economize", + "economised": "economized", + "economises": "economizes", + "economising": "economizing", + "edoema": "edema", + "editorialise": "editorialize", + "editorialised": "editorialized", + "editorialises": "editorializes", + "editorialising": "editorializing", + "empathise": "empathize", + "empathised": "empathized", + "empathises": "empathizes", + "empathising": "empathizing", + "emphasise": "emphasize", + "emphasised": "emphasized", + "emphasises": "emphasizes", + "emphasising": "emphasizing", + "enamelled": "enameled", + "enamelling": "enameling", + "enamoured": "enamored", + "encyclopaedia": "encyclopedia", + "encyclopaedias": "encyclopedias", + "encyclopaedic": "encyclopedic", + "endeavour": "endeavor", + "endeavoured": "endeavored", + "endeavouring": "endeavoring", + "endeavours": "endeavors", + "energise": "energize", + "energised": "energized", + "energises": "energizes", + "energising": "energizing", + "enrol": "enroll", + "enrols": "enrolls", + "enthral": "enthrall", + "enthrals": "enthralls", + "epaulette": "epaulet", + "epaulettes": "epaulets", + "epicentre": "epicenter", + "epicentres": "epicenters", + "epilogue": "epilog", + "epilogues": "epilogs", + "epitomise": "epitomize", + "epitomised": "epitomized", + "epitomises": "epitomizes", + "epitomising": "epitomizing", + "equalisation": "equalization", + "equalise": "equalize", + "equalised": "equalized", + "equaliser": "equalizer", + "equalisers": "equalizers", + "equalises": "equalizes", + "equalising": "equalizing", + "eulogise": "eulogize", + "eulogised": "eulogized", + "eulogises": "eulogizes", + "eulogising": "eulogizing", + "evangelise": "evangelize", + "evangelised": "evangelized", + "evangelises": "evangelizes", + "evangelising": "evangelizing", + "exorcise": "exorcize", + "exorcised": "exorcized", + "exorcises": "exorcizes", + "exorcising": "exorcizing", + "extemporisation": "extemporization", + "extemporise": "extemporize", + "extemporised": "extemporized", + "extemporises": "extemporizes", + "extemporising": "extemporizing", + "externalisation": "externalization", + "externalisations": "externalizations", + "externalise": "externalize", + "externalised": "externalized", + "externalises": "externalizes", + "externalising": "externalizing", + "factorise": "factorize", + "factorised": "factorized", + "factorises": "factorizes", + "factorising": "factorizing", + "faecal": "fecal", + "faeces": "feces", + "familiarisation": "familiarization", + "familiarise": "familiarize", + "familiarised": "familiarized", + "familiarises": "familiarizes", + "familiarising": "familiarizing", + "fantasise": "fantasize", + "fantasised": "fantasized", + "fantasises": "fantasizes", + "fantasising": "fantasizing", + "favour": "favor", + "favourable": "favorable", + "favourably": "favorably", + "favoured": "favored", + "favouring": "favoring", + "favourite": "favorite", + "favourites": "favorites", + "favouritism": "favoritism", + "favours": "favors", + "feminise": "feminize", + "feminised": "feminized", + "feminises": "feminizes", + "feminising": "feminizing", + "fertilisation": "fertilization", + "fertilise": "fertilize", + "fertilised": "fertilized", + "fertiliser": "fertilizer", + "fertilisers": "fertilizers", + "fertilises": "fertilizes", + "fertilising": "fertilizing", + "fervour": "fervor", + "fibre": "fiber", + "fibreglass": "fiberglass", + "fibres": "fibers", + "fictionalisation": "fictionalization", + "fictionalisations": "fictionalizations", + "fictionalise": "fictionalize", + "fictionalised": "fictionalized", + "fictionalises": "fictionalizes", + "fictionalising": "fictionalizing", + "fillet": "filet", + "filleted": "fileted", + "filleting": "fileting", + "fillets": "filets", + "finalisation": "finalization", + "finalise": "finalize", + "finalised": "finalized", + "finalises": "finalizes", + "finalising": "finalizing", + "flautist": "flutist", + "flautists": "flutists", + "flavour": "flavor", + "flavoured": "flavored", + "flavouring": "flavoring", + "flavourings": "flavorings", + "flavourless": "flavorless", + "flavours": "flavors", + "flavoursome": "flavorsome", + "flyer / flier": "flier / flyer", + "foetal": "fetal", + "foetid": "fetid", + "foetus": "fetus", + "foetuses": "fetuses", + "formalisation": "formalization", + "formalise": "formalize", + "formalised": "formalized", + "formalises": "formalizes", + "formalising": "formalizing", + "fossilisation": "fossilization", + "fossilise": "fossilize", + "fossilised": "fossilized", + "fossilises": "fossilizes", + "fossilising": "fossilizing", + "fraternisation": "fraternization", + "fraternise": "fraternize", + "fraternised": "fraternized", + "fraternises": "fraternizes", + "fraternising": "fraternizing", + "fulfil": "fulfill", + "fulfilment": "fulfillment", + "fulfils": "fulfills", + "funnelled": "funneled", + "funnelling": "funneling", + "galvanise": "galvanize", + "galvanised": "galvanized", + "galvanises": "galvanizes", + "galvanising": "galvanizing", + "gambolled": "gamboled", + "gambolling": "gamboling", + "gaol": "jail", + "gaolbird": "jailbird", + "gaolbirds": "jailbirds", + "gaolbreak": "jailbreak", + "gaolbreaks": "jailbreaks", + "gaoled": "jailed", + "gaoler": "jailer", + "gaolers": "jailers", + "gaoling": "jailing", + "gaols": "jails", + "gasses": "gases", + "gage": "gauge", + "gaged": "gauged", + "gages": "gauges", + "gaging": "gauging", + "generalisation": "generalization", + "generalisations": "generalizations", + "generalise": "generalize", + "generalised": "generalized", + "generalises": "generalizes", + "generalising": "generalizing", + "ghettoise": "ghettoize", + "ghettoised": "ghettoized", + "ghettoises": "ghettoizes", + "ghettoising": "ghettoizing", + "gipsies": "gypsies", + "glamorise": "glamorize", + "glamorised": "glamorized", + "glamorises": "glamorizes", + "glamorising": "glamorizing", + "glamor": "glamour", + "globalisation": "globalization", + "globalise": "globalize", + "globalised": "globalized", + "globalises": "globalizes", + "globalising": "globalizing", + "glueing": "gluing", + "goitre": "goiter", + "goitres": "goiters", + "gonorrhoea": "gonorrhea", + "gramme": "gram", + "grammes": "grams", + "gravelled": "graveled", + "grey": "gray", + "greyed": "grayed", + "greying": "graying", + "greyish": "grayish", + "greyness": "grayness", + "greys": "grays", + "grovelled": "groveled", + "grovelling": "groveling", + "groyne": "groin", + "groynes": "groins", + "gruelling": "grueling", + "gruellingly": "gruelingly", + "gryphon": "griffin", + "gryphons": "griffins", + "gynaecological": "gynecological", + "gynaecologist": "gynecologist", + "gynaecologists": "gynecologists", + "gynaecology": "gynecology", + "haematological": "hematological", + "haematologist": "hematologist", + "haematologists": "hematologists", + "haematology": "hematology", + "haemoglobin": "hemoglobin", + "haemophilia": "hemophilia", + "haemophiliac": "hemophiliac", + "haemophiliacs": "hemophiliacs", + "haemorrhage": "hemorrhage", + "haemorrhaged": "hemorrhaged", + "haemorrhages": "hemorrhages", + "haemorrhaging": "hemorrhaging", + "haemorrhoids": "hemorrhoids", + "harbour": "harbor", + "harboured": "harbored", + "harbouring": "harboring", + "harbours": "harbors", + "harmonisation": "harmonization", + "harmonise": "harmonize", + "harmonised": "harmonized", + "harmonises": "harmonizes", + "harmonising": "harmonizing", + "homoeopath": "homeopath", + "homoeopathic": "homeopathic", + "homoeopaths": "homeopaths", + "homoeopathy": "homeopathy", + "homogenise": "homogenize", + "homogenised": "homogenized", + "homogenises": "homogenizes", + "homogenising": "homogenizing", + "honour": "honor", + "honourable": "honorable", + "honourably": "honorably", + "honoured": "honored", + "honouring": "honoring", + "honours": "honors", + "hospitalisation": "hospitalization", + "hospitalise": "hospitalize", + "hospitalised": "hospitalized", + "hospitalises": "hospitalizes", + "hospitalising": "hospitalizing", + "humanise": "humanize", + "humanised": "humanized", + "humanises": "humanizes", + "humanising": "humanizing", + "humour": "humor", + "humoured": "humored", + "humouring": "humoring", + "humourless": "humorless", + "humours": "humors", + "hybridise": "hybridize", + "hybridised": "hybridized", + "hybridises": "hybridizes", + "hybridising": "hybridizing", + "hypnotise": "hypnotize", + "hypnotised": "hypnotized", + "hypnotises": "hypnotizes", + "hypnotising": "hypnotizing", + "hypothesise": "hypothesize", + "hypothesised": "hypothesized", + "hypothesises": "hypothesizes", + "hypothesising": "hypothesizing", + "idealisation": "idealization", + "idealise": "idealize", + "idealised": "idealized", + "idealises": "idealizes", + "idealising": "idealizing", + "idolise": "idolize", + "idolised": "idolized", + "idolises": "idolizes", + "idolising": "idolizing", + "immobilisation": "immobilization", + "immobilise": "immobilize", + "immobilised": "immobilized", + "immobiliser": "immobilizer", + "immobilisers": "immobilizers", + "immobilises": "immobilizes", + "immobilising": "immobilizing", + "immortalise": "immortalize", + "immortalised": "immortalized", + "immortalises": "immortalizes", + "immortalising": "immortalizing", + "immunisation": "immunization", + "immunise": "immunize", + "immunised": "immunized", + "immunises": "immunizes", + "immunising": "immunizing", + "impanelled": "impaneled", + "impanelling": "impaneling", + "imperilled": "imperiled", + "imperilling": "imperiling", + "individualise": "individualize", + "individualised": "individualized", + "individualises": "individualizes", + "individualising": "individualizing", + "industrialise": "industrialize", + "industrialised": "industrialized", + "industrialises": "industrializes", + "industrialising": "industrializing", + "inflexion": "inflection", + "inflexions": "inflections", + "initialise": "initialize", + "initialised": "initialized", + "initialises": "initializes", + "initialising": "initializing", + "initialled": "initialed", + "initialling": "initialing", + "instal": "install", + "instalment": "installment", + "instalments": "installments", + "instals": "installs", + "instil": "instill", + "instils": "instills", + "institutionalisation": "institutionalization", + "institutionalise": "institutionalize", + "institutionalised": "institutionalized", + "institutionalises": "institutionalizes", + "institutionalising": "institutionalizing", + "intellectualise": "intellectualize", + "intellectualised": "intellectualized", + "intellectualises": "intellectualizes", + "intellectualising": "intellectualizing", + "internalisation": "internalization", + "internalise": "internalize", + "internalised": "internalized", + "internalises": "internalizes", + "internalising": "internalizing", + "internationalisation": "internationalization", + "internationalise": "internationalize", + "internationalised": "internationalized", + "internationalises": "internationalizes", + "internationalising": "internationalizing", + "ionisation": "ionization", + "ionise": "ionize", + "ionised": "ionized", + "ioniser": "ionizer", + "ionisers": "ionizers", + "ionises": "ionizes", + "ionising": "ionizing", + "italicise": "italicize", + "italicised": "italicized", + "italicises": "italicizes", + "italicising": "italicizing", + "itemise": "itemize", + "itemised": "itemized", + "itemises": "itemizes", + "itemising": "itemizing", + "jeopardise": "jeopardize", + "jeopardised": "jeopardized", + "jeopardises": "jeopardizes", + "jeopardising": "jeopardizing", + "jewelled": "jeweled", + "jeweller": "jeweler", + "jewellers": "jewelers", + "jewellery": "jewelry", + "judgement": "judgment", + "kilogramme": "kilogram", + "kilogrammes": "kilograms", + "kilometre": "kilometer", + "kilometres": "kilometers", + "labelled": "labeled", + "labelling": "labeling", + "labour": "labor", + "laboured": "labored", + "labourer": "laborer", + "labourers": "laborers", + "labouring": "laboring", + "labours": "labors", + "lacklustre": "lackluster", + "legalisation": "legalization", + "legalise": "legalize", + "legalised": "legalized", + "legalises": "legalizes", + "legalising": "legalizing", + "legitimise": "legitimize", + "legitimised": "legitimized", + "legitimises": "legitimizes", + "legitimising": "legitimizing", + "leukaemia": "leukemia", + "levelled": "leveled", + "leveller": "leveler", + "levellers": "levelers", + "levelling": "leveling", + "libelled": "libeled", + "libelling": "libeling", + "libellous": "libelous", + "liberalisation": "liberalization", + "liberalise": "liberalize", + "liberalised": "liberalized", + "liberalises": "liberalizes", + "liberalising": "liberalizing", + "licence": "license", + "licenced": "licensed", + "licences": "licenses", + "licencing": "licensing", + "likeable": "likable", + "lionisation": "lionization", + "lionise": "lionize", + "lionised": "lionized", + "lionises": "lionizes", + "lionising": "lionizing", + "liquidise": "liquidize", + "liquidised": "liquidized", + "liquidiser": "liquidizer", + "liquidisers": "liquidizers", + "liquidises": "liquidizes", + "liquidising": "liquidizing", + "litre": "liter", + "litres": "liters", + "localise": "localize", + "localised": "localized", + "localises": "localizes", + "localising": "localizing", + "louvre": "louver", + "louvred": "louvered", + "louvres": "louvers", + "lustre": "luster", + "magnetise": "magnetize", + "magnetised": "magnetized", + "magnetises": "magnetizes", + "magnetising": "magnetizing", + "manoeuvrability": "maneuverability", + "manoeuvrable": "maneuverable", + "manoeuvre": "maneuver", + "manoeuvred": "maneuvered", + "manoeuvres": "maneuvers", + "manoeuvring": "maneuvering", + "manoeuvrings": "maneuverings", + "marginalisation": "marginalization", + "marginalise": "marginalize", + "marginalised": "marginalized", + "marginalises": "marginalizes", + "marginalising": "marginalizing", + "marshalled": "marshaled", + "marshalling": "marshaling", + "marvelled": "marveled", + "marvelling": "marveling", + "marvellous": "marvelous", + "marvellously": "marvelously", + "materialisation": "materialization", + "materialise": "materialize", + "materialised": "materialized", + "materialises": "materializes", + "materialising": "materializing", + "maximisation": "maximization", + "maximise": "maximize", + "maximised": "maximized", + "maximises": "maximizes", + "maximising": "maximizing", + "meagre": "meager", + "mechanisation": "mechanization", + "mechanise": "mechanize", + "mechanised": "mechanized", + "mechanises": "mechanizes", + "mechanising": "mechanizing", + "mediaeval": "medieval", + "memorialise": "memorialize", + "memorialised": "memorialized", + "memorialises": "memorializes", + "memorialising": "memorializing", + "memorise": "memorize", + "memorised": "memorized", + "memorises": "memorizes", + "memorising": "memorizing", + "mesmerise": "mesmerize", + "mesmerised": "mesmerized", + "mesmerises": "mesmerizes", + "mesmerising": "mesmerizing", + "metabolise": "metabolize", + "metabolised": "metabolized", + "metabolises": "metabolizes", + "metabolising": "metabolizing", + "metre": "meter", + "metres": "meters", + "micrometre": "micrometer", + "micrometres": "micrometers", + "militarise": "militarize", + "militarised": "militarized", + "militarises": "militarizes", + "militarising": "militarizing", + "milligramme": "milligram", + "milligrammes": "milligrams", + "millilitre": "milliliter", + "millilitres": "milliliters", + "millimetre": "millimeter", + "millimetres": "millimeters", + "miniaturisation": "miniaturization", + "miniaturise": "miniaturize", + "miniaturised": "miniaturized", + "miniaturises": "miniaturizes", + "miniaturising": "miniaturizing", + "minibusses": "minibuses", + "minimise": "minimize", + "minimised": "minimized", + "minimises": "minimizes", + "minimising": "minimizing", + "misbehaviour": "misbehavior", + "misdemeanour": "misdemeanor", + "misdemeanours": "misdemeanors", + "misspelt": "misspelled", + "mitre": "miter", + "mitres": "miters", + "mobilisation": "mobilization", + "mobilise": "mobilize", + "mobilised": "mobilized", + "mobilises": "mobilizes", + "mobilising": "mobilizing", + "modelled": "modeled", + "modeller": "modeler", + "modellers": "modelers", + "modelling": "modeling", + "modernise": "modernize", + "modernised": "modernized", + "modernises": "modernizes", + "modernising": "modernizing", + "moisturise": "moisturize", + "moisturised": "moisturized", + "moisturiser": "moisturizer", + "moisturisers": "moisturizers", + "moisturises": "moisturizes", + "moisturising": "moisturizing", + "monologue": "monolog", + "monologues": "monologs", + "monopolisation": "monopolization", + "monopolise": "monopolize", + "monopolised": "monopolized", + "monopolises": "monopolizes", + "monopolising": "monopolizing", + "moralise": "moralize", + "moralised": "moralized", + "moralises": "moralizes", + "moralising": "moralizing", + "motorised": "motorized", + "mould": "mold", + "moulded": "molded", + "moulder": "molder", + "mouldered": "moldered", + "mouldering": "moldering", + "moulders": "molders", + "mouldier": "moldier", + "mouldiest": "moldiest", + "moulding": "molding", + "mouldings": "moldings", + "moulds": "molds", + "mouldy": "moldy", + "moult": "molt", + "moulted": "molted", + "moulting": "molting", + "moults": "molts", + "moustache": "mustache", + "moustached": "mustached", + "moustaches": "mustaches", + "moustachioed": "mustachioed", + "multicoloured": "multicolored", + "nationalisation": "nationalization", + "nationalisations": "nationalizations", + "nationalise": "nationalize", + "nationalised": "nationalized", + "nationalises": "nationalizes", + "nationalising": "nationalizing", + "naturalisation": "naturalization", + "naturalise": "naturalize", + "naturalised": "naturalized", + "naturalises": "naturalizes", + "naturalising": "naturalizing", + "neighbour": "neighbor", + "neighbourhood": "neighborhood", + "neighbourhoods": "neighborhoods", + "neighbouring": "neighboring", + "neighbourliness": "neighborliness", + "neighbourly": "neighborly", + "neighbours": "neighbors", + "neutralisation": "neutralization", + "neutralise": "neutralize", + "neutralised": "neutralized", + "neutralises": "neutralizes", + "neutralising": "neutralizing", + "normalisation": "normalization", + "normalise": "normalize", + "normalised": "normalized", + "normalises": "normalizes", + "normalising": "normalizing", + "odour": "odor", + "odourless": "odorless", + "odours": "odors", + "oesophagus": "esophagus", + "oesophaguses": "esophaguses", + "oestrogen": "estrogen", + "offence": "offense", + "offences": "offenses", + "omelette": "omelet", + "omelettes": "omelets", + "optimise": "optimize", + "optimised": "optimized", + "optimises": "optimizes", + "optimising": "optimizing", + "organisation": "organization", + "organisational": "organizational", + "organisations": "organizations", + "organise": "organize", + "organised": "organized", + "organiser": "organizer", + "organisers": "organizers", + "organises": "organizes", + "organising": "organizing", + "orthopaedic": "orthopedic", + "orthopaedics": "orthopedics", + "ostracise": "ostracize", + "ostracised": "ostracized", + "ostracises": "ostracizes", + "ostracising": "ostracizing", + "outmanoeuvre": "outmaneuver", + "outmanoeuvred": "outmaneuvered", + "outmanoeuvres": "outmaneuvers", + "outmanoeuvring": "outmaneuvering", + "overemphasise": "overemphasize", + "overemphasised": "overemphasized", + "overemphasises": "overemphasizes", + "overemphasising": "overemphasizing", + "oxidisation": "oxidization", + "oxidise": "oxidize", + "oxidised": "oxidized", + "oxidises": "oxidizes", + "oxidising": "oxidizing", + "paederast": "pederast", + "paederasts": "pederasts", + "paediatric": "pediatric", + "paediatrician": "pediatrician", + "paediatricians": "pediatricians", + "paediatrics": "pediatrics", + "paedophile": "pedophile", + "paedophiles": "pedophiles", + "paedophilia": "pedophilia", + "palaeolithic": "paleolithic", + "palaeontologist": "paleontologist", + "palaeontologists": "paleontologists", + "palaeontology": "paleontology", + "panelled": "paneled", + "panelling": "paneling", + "panellist": "panelist", + "panellists": "panelists", + "paralyse": "paralyze", + "paralysed": "paralyzed", + "paralyses": "paralyzes", + "paralysing": "paralyzing", + "parcelled": "parceled", + "parcelling": "parceling", + "parlour": "parlor", + "parlours": "parlors", + "particularise": "particularize", + "particularised": "particularized", + "particularises": "particularizes", + "particularising": "particularizing", + "passivisation": "passivization", + "passivise": "passivize", + "passivised": "passivized", + "passivises": "passivizes", + "passivising": "passivizing", + "pasteurisation": "pasteurization", + "pasteurise": "pasteurize", + "pasteurised": "pasteurized", + "pasteurises": "pasteurizes", + "pasteurising": "pasteurizing", + "patronise": "patronize", + "patronised": "patronized", + "patronises": "patronizes", + "patronising": "patronizing", + "patronisingly": "patronizingly", + "pedalled": "pedaled", + "pedalling": "pedaling", + "pedestrianisation": "pedestrianization", + "pedestrianise": "pedestrianize", + "pedestrianised": "pedestrianized", + "pedestrianises": "pedestrianizes", + "pedestrianising": "pedestrianizing", + "penalise": "penalize", + "penalised": "penalized", + "penalises": "penalizes", + "penalising": "penalizing", + "pencilled": "penciled", + "pencilling": "penciling", + "personalise": "personalize", + "personalised": "personalized", + "personalises": "personalizes", + "personalising": "personalizing", + "pharmacopoeia": "pharmacopeia", + "pharmacopoeias": "pharmacopeias", + "philosophise": "philosophize", + "philosophised": "philosophized", + "philosophises": "philosophizes", + "philosophising": "philosophizing", + "philtre": "filter", + "philtres": "filters", + "phoney": "phony", + "plagiarise": "plagiarize", + "plagiarised": "plagiarized", + "plagiarises": "plagiarizes", + "plagiarising": "plagiarizing", + "plough": "plow", + "ploughed": "plowed", + "ploughing": "plowing", + "ploughman": "plowman", + "ploughmen": "plowmen", + "ploughs": "plows", + "ploughshare": "plowshare", + "ploughshares": "plowshares", + "polarisation": "polarization", + "polarise": "polarize", + "polarised": "polarized", + "polarises": "polarizes", + "polarising": "polarizing", + "politicisation": "politicization", + "politicise": "politicize", + "politicised": "politicized", + "politicises": "politicizes", + "politicising": "politicizing", + "popularisation": "popularization", + "popularise": "popularize", + "popularised": "popularized", + "popularises": "popularizes", + "popularising": "popularizing", + "pouffe": "pouf", + "pouffes": "poufs", + "practise": "practice", + "practised": "practiced", + "practises": "practices", + "practising": "practicing", + "praesidium": "presidium", + "praesidiums": "presidiums", + "pressurisation": "pressurization", + "pressurise": "pressurize", + "pressurised": "pressurized", + "pressurises": "pressurizes", + "pressurising": "pressurizing", + "pretence": "pretense", + "pretences": "pretenses", + "primaeval": "primeval", + "prioritisation": "prioritization", + "prioritise": "prioritize", + "prioritised": "prioritized", + "prioritises": "prioritizes", + "prioritising": "prioritizing", + "privatisation": "privatization", + "privatisations": "privatizations", + "privatise": "privatize", + "privatised": "privatized", + "privatises": "privatizes", + "privatising": "privatizing", + "professionalisation": "professionalization", + "professionalise": "professionalize", + "professionalised": "professionalized", + "professionalises": "professionalizes", + "professionalising": "professionalizing", + "programme": "program", + "programmes": "programs", + "prologue": "prolog", + "prologues": "prologs", + "propagandise": "propagandize", + "propagandised": "propagandized", + "propagandises": "propagandizes", + "propagandising": "propagandizing", + "proselytise": "proselytize", + "proselytised": "proselytized", + "proselytiser": "proselytizer", + "proselytisers": "proselytizers", + "proselytises": "proselytizes", + "proselytising": "proselytizing", + "psychoanalyse": "psychoanalyze", + "psychoanalysed": "psychoanalyzed", + "psychoanalyses": "psychoanalyzes", + "psychoanalysing": "psychoanalyzing", + "publicise": "publicize", + "publicised": "publicized", + "publicises": "publicizes", + "publicising": "publicizing", + "pulverisation": "pulverization", + "pulverise": "pulverize", + "pulverised": "pulverized", + "pulverises": "pulverizes", + "pulverising": "pulverizing", + "pummelled": "pummel", + "pummelling": "pummeled", + "pyjama": "pajama", + "pyjamas": "pajamas", + "pzazz": "pizzazz", + "quarrelled": "quarreled", + "quarrelling": "quarreling", + "radicalise": "radicalize", + "radicalised": "radicalized", + "radicalises": "radicalizes", + "radicalising": "radicalizing", + "rancour": "rancor", + "randomise": "randomize", + "randomised": "randomized", + "randomises": "randomizes", + "randomising": "randomizing", + "rationalisation": "rationalization", + "rationalisations": "rationalizations", + "rationalise": "rationalize", + "rationalised": "rationalized", + "rationalises": "rationalizes", + "rationalising": "rationalizing", + "ravelled": "raveled", + "ravelling": "raveling", + "realisable": "realizable", + "realisation": "realization", + "realisations": "realizations", + "realise": "realize", + "realised": "realized", + "realises": "realizes", + "realising": "realizing", + "recognisable": "recognizable", + "recognisably": "recognizably", + "recognisance": "recognizance", + "recognise": "recognize", + "recognised": "recognized", + "recognises": "recognizes", + "recognising": "recognizing", + "reconnoitre": "reconnoiter", + "reconnoitred": "reconnoitered", + "reconnoitres": "reconnoiters", + "reconnoitring": "reconnoitering", + "refuelled": "refueled", + "refuelling": "refueling", + "regularisation": "regularization", + "regularise": "regularize", + "regularised": "regularized", + "regularises": "regularizes", + "regularising": "regularizing", + "remodelled": "remodeled", + "remodelling": "remodeling", + "remould": "remold", + "remoulded": "remolded", + "remoulding": "remolding", + "remoulds": "remolds", + "reorganisation": "reorganization", + "reorganisations": "reorganizations", + "reorganise": "reorganize", + "reorganised": "reorganized", + "reorganises": "reorganizes", + "reorganising": "reorganizing", + "revelled": "reveled", + "reveller": "reveler", + "revellers": "revelers", + "revelling": "reveling", + "revitalise": "revitalize", + "revitalised": "revitalized", + "revitalises": "revitalizes", + "revitalising": "revitalizing", + "revolutionise": "revolutionize", + "revolutionised": "revolutionized", + "revolutionises": "revolutionizes", + "revolutionising": "revolutionizing", + "rhapsodise": "rhapsodize", + "rhapsodised": "rhapsodized", + "rhapsodises": "rhapsodizes", + "rhapsodising": "rhapsodizing", + "rigour": "rigor", + "rigours": "rigors", + "ritualised": "ritualized", + "rivalled": "rivaled", + "rivalling": "rivaling", + "romanticise": "romanticize", + "romanticised": "romanticized", + "romanticises": "romanticizes", + "romanticising": "romanticizing", + "rumour": "rumor", + "rumoured": "rumored", + "rumours": "rumors", + "sabre": "saber", + "sabres": "sabers", + "saltpetre": "saltpeter", + "sanitise": "sanitize", + "sanitised": "sanitized", + "sanitises": "sanitizes", + "sanitising": "sanitizing", + "satirise": "satirize", + "satirised": "satirized", + "satirises": "satirizes", + "satirising": "satirizing", + "saviour": "savior", + "saviours": "saviors", + "savour": "savor", + "savoured": "savored", + "savouries": "savories", + "savouring": "savoring", + "savours": "savors", + "savoury": "savory", + "scandalise": "scandalize", + "scandalised": "scandalized", + "scandalises": "scandalizes", + "scandalising": "scandalizing", + "sceptic": "skeptic", + "sceptical": "skeptical", + "sceptically": "skeptically", + "scepticism": "skepticism", + "sceptics": "skeptics", + "sceptre": "scepter", + "sceptres": "scepters", + "scrutinise": "scrutinize", + "scrutinised": "scrutinized", + "scrutinises": "scrutinizes", + "scrutinising": "scrutinizing", + "secularisation": "secularization", + "secularise": "secularize", + "secularised": "secularized", + "secularises": "secularizes", + "secularising": "secularizing", + "sensationalise": "sensationalize", + "sensationalised": "sensationalized", + "sensationalises": "sensationalizes", + "sensationalising": "sensationalizing", + "sensitise": "sensitize", + "sensitised": "sensitized", + "sensitises": "sensitizes", + "sensitising": "sensitizing", + "sentimentalise": "sentimentalize", + "sentimentalised": "sentimentalized", + "sentimentalises": "sentimentalizes", + "sentimentalising": "sentimentalizing", + "sepulchre": "sepulcher", + "sepulchres": "sepulchers", + "serialisation": "serialization", + "serialisations": "serializations", + "serialise": "serialize", + "serialised": "serialized", + "serialises": "serializes", + "serialising": "serializing", + "sermonise": "sermonize", + "sermonised": "sermonized", + "sermonises": "sermonizes", + "sermonising": "sermonizing", + "sheikh": "sheik", + "shovelled": "shoveled", + "shovelling": "shoveling", + "shrivelled": "shriveled", + "shrivelling": "shriveling", + "signalise": "signalize", + "signalised": "signalized", + "signalises": "signalizes", + "signalising": "signalizing", + "signalled": "signaled", + "signalling": "signaling", + "smoulder": "smolder", + "smouldered": "smoldered", + "smouldering": "smoldering", + "smoulders": "smolders", + "snivelled": "sniveled", + "snivelling": "sniveling", + "snorkelled": "snorkeled", + "snorkelling": "snorkeling", + "snowplough": "snowplow", + "snowploughs": "snowplow", + "socialisation": "socialization", + "socialise": "socialize", + "socialised": "socialized", + "socialises": "socializes", + "socialising": "socializing", + "sodomise": "sodomize", + "sodomised": "sodomized", + "sodomises": "sodomizes", + "sodomising": "sodomizing", + "solemnise": "solemnize", + "solemnised": "solemnized", + "solemnises": "solemnizes", + "solemnising": "solemnizing", + "sombre": "somber", + "specialisation": "specialization", + "specialisations": "specializations", + "specialise": "specialize", + "specialised": "specialized", + "specialises": "specializes", + "specialising": "specializing", + "spectre": "specter", + "spectres": "specters", + "spiralled": "spiraled", + "spiralling": "spiraling", + "splendour": "splendor", + "splendours": "splendors", + "squirrelled": "squirreled", + "squirrelling": "squirreling", + "stabilisation": "stabilization", + "stabilise": "stabilize", + "stabilised": "stabilized", + "stabiliser": "stabilizer", + "stabilisers": "stabilizers", + "stabilises": "stabilizes", + "stabilising": "stabilizing", + "standardisation": "standardization", + "standardise": "standardize", + "standardised": "standardized", + "standardises": "standardizes", + "standardising": "standardizing", + "stencilled": "stenciled", + "stencilling": "stenciling", + "sterilisation": "sterilization", + "sterilisations": "sterilizations", + "sterilise": "sterilize", + "sterilised": "sterilized", + "steriliser": "sterilizer", + "sterilisers": "sterilizers", + "sterilises": "sterilizes", + "sterilising": "sterilizing", + "stigmatisation": "stigmatization", + "stigmatise": "stigmatize", + "stigmatised": "stigmatized", + "stigmatises": "stigmatizes", + "stigmatising": "stigmatizing", + "storey": "story", + "storeys": "stories", + "subsidisation": "subsidization", + "subsidise": "subsidize", + "subsidised": "subsidized", + "subsidiser": "subsidizer", + "subsidisers": "subsidizers", + "subsidises": "subsidizes", + "subsidising": "subsidizing", + "succour": "succor", + "succoured": "succored", + "succouring": "succoring", + "succours": "succors", + "sulphate": "sulfate", + "sulphates": "sulfates", + "sulphide": "sulfide", + "sulphides": "sulfides", + "sulphur": "sulfur", + "sulphurous": "sulfurous", + "summarise": "summarize", + "summarised": "summarized", + "summarises": "summarizes", + "summarising": "summarizing", + "swivelled": "swiveled", + "swivelling": "swiveling", + "symbolise": "symbolize", + "symbolised": "symbolized", + "symbolises": "symbolizes", + "symbolising": "symbolizing", + "sympathise": "sympathize", + "sympathised": "sympathized", + "sympathiser": "sympathizer", + "sympathisers": "sympathizers", + "sympathises": "sympathizes", + "sympathising": "sympathizing", + "synchronisation": "synchronization", + "synchronise": "synchronize", + "synchronised": "synchronized", + "synchronises": "synchronizes", + "synchronising": "synchronizing", + "synthesise": "synthesize", + "synthesised": "synthesized", + "synthesiser": "synthesizer", + "synthesisers": "synthesizers", + "synthesises": "synthesizes", + "synthesising": "synthesizing", + "syphon": "siphon", + "syphoned": "siphoned", + "syphoning": "siphoning", + "syphons": "siphons", + "systematisation": "systematization", + "systematise": "systematize", + "systematised": "systematized", + "systematises": "systematizes", + "systematising": "systematizing", + "tantalise": "tantalize", + "tantalised": "tantalized", + "tantalises": "tantalizes", + "tantalising": "tantalizing", + "tantalisingly": "tantalizingly", + "tasselled": "tasseled", + "technicolour": "technicolor", + "temporise": "temporize", + "temporised": "temporized", + "temporises": "temporizes", + "temporising": "temporizing", + "tenderise": "tenderize", + "tenderised": "tenderized", + "tenderises": "tenderizes", + "tenderising": "tenderizing", + "terrorise": "terrorize", + "terrorised": "terrorized", + "terrorises": "terrorizes", + "terrorising": "terrorizing", + "theatre": "theater", + "theatregoer": "theatergoer", + "theatregoers": "theatergoers", + "theatres": "theaters", + "theorise": "theorize", + "theorised": "theorized", + "theorises": "theorizes", + "theorising": "theorizing", + "tonne": "ton", + "tonnes": "tons", + "towelled": "toweled", + "towelling": "toweling", + "toxaemia": "toxemia", + "tranquillise": "tranquilize", + "tranquillised": "tranquilized", + "tranquilliser": "tranquilizer", + "tranquillisers": "tranquilizers", + "tranquillises": "tranquilizes", + "tranquillising": "tranquilizing", + "tranquillity": "tranquility", + "tranquillize": "tranquilize", + "tranquillized": "tranquilized", + "tranquillizer": "tranquilizer", + "tranquillizers": "tranquilizers", + "tranquillizes": "tranquilizes", + "tranquillizing": "tranquilizing", + "tranquilly": "tranquility", + "transistorised": "transistorized", + "traumatise": "traumatize", + "traumatised": "traumatized", + "traumatises": "traumatizes", + "traumatising": "traumatizing", + "travelled": "traveled", + "traveller": "traveler", + "travellers": "travelers", + "travelling": "traveling", + "travelog": "travelogue", + "travelogs": "travelogues", + "trialled": "trialed", + "trialling": "trialing", + "tricolour": "tricolor", + "tricolours": "tricolors", + "trivialise": "trivialize", + "trivialised": "trivialized", + "trivialises": "trivializes", + "trivialising": "trivializing", + "tumour": "tumor", + "tumours": "tumors", + "tunnelled": "tunneled", + "tunnelling": "tunneling", + "tyrannise": "tyrannize", + "tyrannised": "tyrannized", + "tyrannises": "tyrannizes", + "tyrannising": "tyrannizing", + "tyre": "tire", + "tyres": "tires", + "unauthorised": "unauthorized", + "uncivilised": "uncivilized", + "underutilised": "underutilized", + "unequalled": "unequaled", + "unfavourable": "unfavorable", + "unfavourably": "unfavorably", + "unionisation": "unionization", + "unionise": "unionize", + "unionised": "unionized", + "unionises": "unionizes", + "unionising": "unionizing", + "unorganised": "unorganized", + "unravelled": "unraveled", + "unravelling": "unraveling", + "unrecognisable": "unrecognizable", + "unrecognised": "unrecognized", + "unrivalled": "unrivaled", + "unsavoury": "unsavory", + "untrammelled": "untrammeled", + "urbanisation": "urbanization", + "urbanise": "urbanize", + "urbanised": "urbanized", + "urbanises": "urbanizes", + "urbanising": "urbanizing", + "utilisable": "utilizable", + "utilisation": "utilization", + "utilise": "utilize", + "utilised": "utilized", + "utilises": "utilizes", + "utilising": "utilizing", + "valour": "valor", + "vandalise": "vandalize", + "vandalised": "vandalized", + "vandalises": "vandalizes", + "vandalising": "vandalizing", + "vaporisation": "vaporization", + "vaporise": "vaporize", + "vaporised": "vaporized", + "vaporises": "vaporizes", + "vaporising": "vaporizing", + "vapour": "vapor", + "vapours": "vapors", + "verbalise": "verbalize", + "verbalised": "verbalized", + "verbalises": "verbalizes", + "verbalising": "verbalizing", + "victimisation": "victimization", + "victimise": "victimize", + "victimised": "victimized", + "victimises": "victimizes", + "victimising": "victimizing", + "videodisc": "videodisk", + "videodiscs": "videodisks", + "vigour": "vigor", + "visualisation": "visualization", + "visualisations": "visualizations", + "visualise": "visualize", + "visualised": "visualized", + "visualises": "visualizes", + "visualising": "visualizing", + "vocalisation": "vocalization", + "vocalisations": "vocalizations", + "vocalise": "vocalize", + "vocalised": "vocalized", + "vocalises": "vocalizes", + "vocalising": "vocalizing", + "vulcanised": "vulcanized", + "vulgarisation": "vulgarization", + "vulgarise": "vulgarize", + "vulgarised": "vulgarized", + "vulgarises": "vulgarizes", + "vulgarising": "vulgarizing", + "waggon": "wagon", + "waggons": "wagons", + "watercolour": "watercolor", + "watercolours": "watercolors", + "weaselled": "weaseled", + "weaselling": "weaseling", + "westernisation": "westernization", + "westernise": "westernize", + "westernised": "westernized", + "westernises": "westernizes", + "westernising": "westernizing", + "womanise": "womanize", + "womanised": "womanized", + "womaniser": "womanizer", + "womanisers": "womanizers", + "womanises": "womanizes", + "womanising": "womanizing", + "woollen": "woolen", + "woollens": "woolens", + "woollies": "woolies", + "woolly": "wooly", + "worshipped": "worshiped", + "worshipping": "worshiping", + "worshipper": "worshiper", + "yodelled": "yodeled", + "yodelling": "yodeling", + "yoghourt": "yogurt", + "yoghourts": "yogurts", + "yoghurt": "yogurt", + "yoghurts": "yogurts", + "mhm": "hmm", + "mm": "hmm", + "mmm": "hmm" } \ No newline at end of file diff --git a/latentsync/whisper/whisper/normalizers/english.py b/latentsync/whisper/whisper/normalizers/english.py index d5c2bb4..8f6d566 100644 --- a/latentsync/whisper/whisper/normalizers/english.py +++ b/latentsync/whisper/whisper/normalizers/english.py @@ -1,543 +1,543 @@ -import json -import os -import re -from fractions import Fraction -from typing import Iterator, List, Match, Optional, Union - -from more_itertools import windowed - -from .basic import remove_symbols_and_diacritics - - -class EnglishNumberNormalizer: - """ - Convert any spelled-out numbers into arabic numbers, while handling: - - - remove any commas - - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. - - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` - - spell out `one` and `ones` - - interpret successive single-digit numbers as nominal: `one oh one` -> `101` - """ - - def __init__(self): - super().__init__() - - self.zeros = {"o", "oh", "zero"} - self.ones = { - name: i - for i, name in enumerate( - [ - "one", - "two", - "three", - "four", - "five", - "six", - "seven", - "eight", - "nine", - "ten", - "eleven", - "twelve", - "thirteen", - "fourteen", - "fifteen", - "sixteen", - "seventeen", - "eighteen", - "nineteen", - ], - start=1, - ) - } - self.ones_plural = { - "sixes" if name == "six" else name + "s": (value, "s") - for name, value in self.ones.items() - } - self.ones_ordinal = { - "zeroth": (0, "th"), - "first": (1, "st"), - "second": (2, "nd"), - "third": (3, "rd"), - "fifth": (5, "th"), - "twelfth": (12, "th"), - **{ - name + ("h" if name.endswith("t") else "th"): (value, "th") - for name, value in self.ones.items() - if value > 3 and value != 5 and value != 12 - }, - } - self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} - - self.tens = { - "twenty": 20, - "thirty": 30, - "forty": 40, - "fifty": 50, - "sixty": 60, - "seventy": 70, - "eighty": 80, - "ninety": 90, - } - self.tens_plural = { - name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() - } - self.tens_ordinal = { - name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items() - } - self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} - - self.multipliers = { - "hundred": 100, - "thousand": 1_000, - "million": 1_000_000, - "billion": 1_000_000_000, - "trillion": 1_000_000_000_000, - "quadrillion": 1_000_000_000_000_000, - "quintillion": 1_000_000_000_000_000_000, - "sextillion": 1_000_000_000_000_000_000_000, - "septillion": 1_000_000_000_000_000_000_000_000, - "octillion": 1_000_000_000_000_000_000_000_000_000, - "nonillion": 1_000_000_000_000_000_000_000_000_000_000, - "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, - } - self.multipliers_plural = { - name + "s": (value, "s") for name, value in self.multipliers.items() - } - self.multipliers_ordinal = { - name + "th": (value, "th") for name, value in self.multipliers.items() - } - self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} - self.decimals = {*self.ones, *self.tens, *self.zeros} - - self.preceding_prefixers = { - "minus": "-", - "negative": "-", - "plus": "+", - "positive": "+", - } - self.following_prefixers = { - "pound": "£", - "pounds": "£", - "euro": "€", - "euros": "€", - "dollar": "$", - "dollars": "$", - "cent": "¢", - "cents": "¢", - } - self.prefixes = set( - list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()) - ) - self.suffixers = { - "per": {"cent": "%"}, - "percent": "%", - } - self.specials = {"and", "double", "triple", "point"} - - self.words = set( - [ - key - for mapping in [ - self.zeros, - self.ones, - self.ones_suffixed, - self.tens, - self.tens_suffixed, - self.multipliers, - self.multipliers_suffixed, - self.preceding_prefixers, - self.following_prefixers, - self.suffixers, - self.specials, - ] - for key in mapping - ] - ) - self.literal_words = {"one", "ones"} - - def process_words(self, words: List[str]) -> Iterator[str]: - prefix: Optional[str] = None - value: Optional[Union[str, int]] = None - skip = False - - def to_fraction(s: str): - try: - return Fraction(s) - except ValueError: - return None - - def output(result: Union[str, int]): - nonlocal prefix, value - result = str(result) - if prefix is not None: - result = prefix + result - value = None - prefix = None - return result - - if len(words) == 0: - return - - for prev, current, next in windowed([None] + words + [None], 3): - if skip: - skip = False - continue - - next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) - has_prefix = current[0] in self.prefixes - current_without_prefix = current[1:] if has_prefix else current - if re.match(r"^\d+(\.\d+)?$", current_without_prefix): - # arabic numbers (potentially with signs and fractions) - f = to_fraction(current_without_prefix) - assert f is not None - if value is not None: - if isinstance(value, str) and value.endswith("."): - # concatenate decimals / ip address components - value = str(value) + str(current) - continue - else: - yield output(value) - - prefix = current[0] if has_prefix else prefix - if f.denominator == 1: - value = f.numerator # store integers as int - else: - value = current_without_prefix - elif current not in self.words: - # non-numeric words - if value is not None: - yield output(value) - yield output(current) - elif current in self.zeros: - value = str(value or "") + "0" - elif current in self.ones: - ones = self.ones[current] - - if value is None: - value = ones - elif isinstance(value, str) or prev in self.ones: - if prev in self.tens and ones < 10: # replace the last zero with the digit - assert value[-1] == "0" - value = value[:-1] + str(ones) - else: - value = str(value) + str(ones) - elif ones < 10: - if value % 10 == 0: - value += ones - else: - value = str(value) + str(ones) - else: # eleven to nineteen - if value % 100 == 0: - value += ones - else: - value = str(value) + str(ones) - elif current in self.ones_suffixed: - # ordinal or cardinal; yield the number right away - ones, suffix = self.ones_suffixed[current] - if value is None: - yield output(str(ones) + suffix) - elif isinstance(value, str) or prev in self.ones: - if prev in self.tens and ones < 10: - assert value[-1] == "0" - yield output(value[:-1] + str(ones) + suffix) - else: - yield output(str(value) + str(ones) + suffix) - elif ones < 10: - if value % 10 == 0: - yield output(str(value + ones) + suffix) - else: - yield output(str(value) + str(ones) + suffix) - else: # eleven to nineteen - if value % 100 == 0: - yield output(str(value + ones) + suffix) - else: - yield output(str(value) + str(ones) + suffix) - value = None - elif current in self.tens: - tens = self.tens[current] - if value is None: - value = tens - elif isinstance(value, str): - value = str(value) + str(tens) - else: - if value % 100 == 0: - value += tens - else: - value = str(value) + str(tens) - elif current in self.tens_suffixed: - # ordinal or cardinal; yield the number right away - tens, suffix = self.tens_suffixed[current] - if value is None: - yield output(str(tens) + suffix) - elif isinstance(value, str): - yield output(str(value) + str(tens) + suffix) - else: - if value % 100 == 0: - yield output(str(value + tens) + suffix) - else: - yield output(str(value) + str(tens) + suffix) - elif current in self.multipliers: - multiplier = self.multipliers[current] - if value is None: - value = multiplier - elif isinstance(value, str) or value == 0: - f = to_fraction(value) - p = f * multiplier if f is not None else None - if f is not None and p.denominator == 1: - value = p.numerator - else: - yield output(value) - value = multiplier - else: - before = value // 1000 * 1000 - residual = value % 1000 - value = before + residual * multiplier - elif current in self.multipliers_suffixed: - multiplier, suffix = self.multipliers_suffixed[current] - if value is None: - yield output(str(multiplier) + suffix) - elif isinstance(value, str): - f = to_fraction(value) - p = f * multiplier if f is not None else None - if f is not None and p.denominator == 1: - yield output(str(p.numerator) + suffix) - else: - yield output(value) - yield output(str(multiplier) + suffix) - else: # int - before = value // 1000 * 1000 - residual = value % 1000 - value = before + residual * multiplier - yield output(str(value) + suffix) - value = None - elif current in self.preceding_prefixers: - # apply prefix (positive, minus, etc.) if it precedes a number - if value is not None: - yield output(value) - - if next in self.words or next_is_numeric: - prefix = self.preceding_prefixers[current] - else: - yield output(current) - elif current in self.following_prefixers: - # apply prefix (dollars, cents, etc.) only after a number - if value is not None: - prefix = self.following_prefixers[current] - yield output(value) - else: - yield output(current) - elif current in self.suffixers: - # apply suffix symbols (percent -> '%') - if value is not None: - suffix = self.suffixers[current] - if isinstance(suffix, dict): - if next in suffix: - yield output(str(value) + suffix[next]) - skip = True - else: - yield output(value) - yield output(current) - else: - yield output(str(value) + suffix) - else: - yield output(current) - elif current in self.specials: - if next not in self.words and not next_is_numeric: - # apply special handling only if the next word can be numeric - if value is not None: - yield output(value) - yield output(current) - elif current == "and": - # ignore "and" after hundreds, thousands, etc. - if prev not in self.multipliers: - if value is not None: - yield output(value) - yield output(current) - elif current == "double" or current == "triple": - if next in self.ones or next in self.zeros: - repeats = 2 if current == "double" else 3 - ones = self.ones.get(next, 0) - value = str(value or "") + str(ones) * repeats - skip = True - else: - if value is not None: - yield output(value) - yield output(current) - elif current == "point": - if next in self.decimals or next_is_numeric: - value = str(value or "") + "." - else: - # should all have been covered at this point - raise ValueError(f"Unexpected token: {current}") - else: - # all should have been covered at this point - raise ValueError(f"Unexpected token: {current}") - - if value is not None: - yield output(value) - - def preprocess(self, s: str): - # replace " and a half" with " point five" - results = [] - - segments = re.split(r"\band\s+a\s+half\b", s) - for i, segment in enumerate(segments): - if len(segment.strip()) == 0: - continue - if i == len(segments) - 1: - results.append(segment) - else: - results.append(segment) - last_word = segment.rsplit(maxsplit=2)[-1] - if last_word in self.decimals or last_word in self.multipliers: - results.append("point five") - else: - results.append("and a half") - - s = " ".join(results) - - # put a space at number/letter boundary - s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) - s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) - - # but remove spaces which could be a suffix - s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) - - return s - - def postprocess(self, s: str): - def combine_cents(m: Match): - try: - currency = m.group(1) - integer = m.group(2) - cents = int(m.group(3)) - return f"{currency}{integer}.{cents:02d}" - except ValueError: - return m.string - - def extract_cents(m: Match): - try: - return f"¢{int(m.group(1))}" - except ValueError: - return m.string - - # apply currency postprocessing; "$2 and ¢7" -> "$2.07" - s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) - s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) - - # write "one(s)" instead of "1(s)", just for the readability - s = re.sub(r"\b1(s?)\b", r"one\1", s) - - return s - - def __call__(self, s: str): - s = self.preprocess(s) - s = " ".join(word for word in self.process_words(s.split()) if word is not None) - s = self.postprocess(s) - - return s - - -class EnglishSpellingNormalizer: - """ - Applies British-American spelling mappings as listed in [1]. - - [1] https://www.tysto.com/uk-us-spelling-list.html - """ - - def __init__(self): - mapping_path = os.path.join(os.path.dirname(__file__), "english.json") - self.mapping = json.load(open(mapping_path)) - - def __call__(self, s: str): - return " ".join(self.mapping.get(word, word) for word in s.split()) - - -class EnglishTextNormalizer: - def __init__(self): - self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" - self.replacers = { - # common contractions - r"\bwon't\b": "will not", - r"\bcan't\b": "can not", - r"\blet's\b": "let us", - r"\bain't\b": "aint", - r"\by'all\b": "you all", - r"\bwanna\b": "want to", - r"\bgotta\b": "got to", - r"\bgonna\b": "going to", - r"\bi'ma\b": "i am going to", - r"\bimma\b": "i am going to", - r"\bwoulda\b": "would have", - r"\bcoulda\b": "could have", - r"\bshoulda\b": "should have", - r"\bma'am\b": "madam", - # contractions in titles/prefixes - r"\bmr\b": "mister ", - r"\bmrs\b": "missus ", - r"\bst\b": "saint ", - r"\bdr\b": "doctor ", - r"\bprof\b": "professor ", - r"\bcapt\b": "captain ", - r"\bgov\b": "governor ", - r"\bald\b": "alderman ", - r"\bgen\b": "general ", - r"\bsen\b": "senator ", - r"\brep\b": "representative ", - r"\bpres\b": "president ", - r"\brev\b": "reverend ", - r"\bhon\b": "honorable ", - r"\basst\b": "assistant ", - r"\bassoc\b": "associate ", - r"\blt\b": "lieutenant ", - r"\bcol\b": "colonel ", - r"\bjr\b": "junior ", - r"\bsr\b": "senior ", - r"\besq\b": "esquire ", - # prefect tenses, ideally it should be any past participles, but it's harder.. - r"'d been\b": " had been", - r"'s been\b": " has been", - r"'d gone\b": " had gone", - r"'s gone\b": " has gone", - r"'d done\b": " had done", # "'s done" is ambiguous - r"'s got\b": " has got", - # general contractions - r"n't\b": " not", - r"'re\b": " are", - r"'s\b": " is", - r"'d\b": " would", - r"'ll\b": " will", - r"'t\b": " not", - r"'ve\b": " have", - r"'m\b": " am", - } - self.standardize_numbers = EnglishNumberNormalizer() - self.standardize_spellings = EnglishSpellingNormalizer() - - def __call__(self, s: str): - s = s.lower() - - s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets - s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis - s = re.sub(self.ignore_patterns, "", s) - s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe - - for pattern, replacement in self.replacers.items(): - s = re.sub(pattern, replacement, s) - - s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits - s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers - s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics - - s = self.standardize_numbers(s) - s = self.standardize_spellings(s) - - # now remove prefix/suffix symbols that are not preceded/followed by numbers - s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) - s = re.sub(r"([^0-9])%", r"\1 ", s) - - s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space - - return s +import json +import os +import re +from fractions import Fraction +from typing import Iterator, List, Match, Optional, Union + +from more_itertools import windowed + +from .basic import remove_symbols_and_diacritics + + +class EnglishNumberNormalizer: + """ + Convert any spelled-out numbers into arabic numbers, while handling: + + - remove any commas + - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. + - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` + - spell out `one` and `ones` + - interpret successive single-digit numbers as nominal: `one oh one` -> `101` + """ + + def __init__(self): + super().__init__() + + self.zeros = {"o", "oh", "zero"} + self.ones = { + name: i + for i, name in enumerate( + [ + "one", + "two", + "three", + "four", + "five", + "six", + "seven", + "eight", + "nine", + "ten", + "eleven", + "twelve", + "thirteen", + "fourteen", + "fifteen", + "sixteen", + "seventeen", + "eighteen", + "nineteen", + ], + start=1, + ) + } + self.ones_plural = { + "sixes" if name == "six" else name + "s": (value, "s") + for name, value in self.ones.items() + } + self.ones_ordinal = { + "zeroth": (0, "th"), + "first": (1, "st"), + "second": (2, "nd"), + "third": (3, "rd"), + "fifth": (5, "th"), + "twelfth": (12, "th"), + **{ + name + ("h" if name.endswith("t") else "th"): (value, "th") + for name, value in self.ones.items() + if value > 3 and value != 5 and value != 12 + }, + } + self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} + + self.tens = { + "twenty": 20, + "thirty": 30, + "forty": 40, + "fifty": 50, + "sixty": 60, + "seventy": 70, + "eighty": 80, + "ninety": 90, + } + self.tens_plural = { + name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() + } + self.tens_ordinal = { + name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items() + } + self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} + + self.multipliers = { + "hundred": 100, + "thousand": 1_000, + "million": 1_000_000, + "billion": 1_000_000_000, + "trillion": 1_000_000_000_000, + "quadrillion": 1_000_000_000_000_000, + "quintillion": 1_000_000_000_000_000_000, + "sextillion": 1_000_000_000_000_000_000_000, + "septillion": 1_000_000_000_000_000_000_000_000, + "octillion": 1_000_000_000_000_000_000_000_000_000, + "nonillion": 1_000_000_000_000_000_000_000_000_000_000, + "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, + } + self.multipliers_plural = { + name + "s": (value, "s") for name, value in self.multipliers.items() + } + self.multipliers_ordinal = { + name + "th": (value, "th") for name, value in self.multipliers.items() + } + self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} + self.decimals = {*self.ones, *self.tens, *self.zeros} + + self.preceding_prefixers = { + "minus": "-", + "negative": "-", + "plus": "+", + "positive": "+", + } + self.following_prefixers = { + "pound": "£", + "pounds": "£", + "euro": "€", + "euros": "€", + "dollar": "$", + "dollars": "$", + "cent": "¢", + "cents": "¢", + } + self.prefixes = set( + list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()) + ) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = set( + [ + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + ] + ) + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for prev, current, next in windowed([None] + words + [None], 3): + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) + has_prefix = current[0] in self.prefixes + current_without_prefix = current[1:] if has_prefix else current + if re.match(r"^\d+(\.\d+)?$", current_without_prefix): + # arabic numbers (potentially with signs and fractions) + f = to_fraction(current_without_prefix) + assert f is not None + if value is not None: + if isinstance(value, str) and value.endswith("."): + # concatenate decimals / ip address components + value = str(value) + str(current) + continue + else: + yield output(value) + + prefix = current[0] if has_prefix else prefix + if f.denominator == 1: + value = f.numerator # store integers as int + else: + value = current_without_prefix + elif current not in self.words: + # non-numeric words + if value is not None: + yield output(value) + yield output(current) + elif current in self.zeros: + value = str(value or "") + "0" + elif current in self.ones: + ones = self.ones[current] + + if value is None: + value = ones + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: # replace the last zero with the digit + assert value[-1] == "0" + value = value[:-1] + str(ones) + else: + value = str(value) + str(ones) + elif ones < 10: + if value % 10 == 0: + value += ones + else: + value = str(value) + str(ones) + else: # eleven to nineteen + if value % 100 == 0: + value += ones + else: + value = str(value) + str(ones) + elif current in self.ones_suffixed: + # ordinal or cardinal; yield the number right away + ones, suffix = self.ones_suffixed[current] + if value is None: + yield output(str(ones) + suffix) + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: + assert value[-1] == "0" + yield output(value[:-1] + str(ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + elif ones < 10: + if value % 10 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + else: # eleven to nineteen + if value % 100 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + value = None + elif current in self.tens: + tens = self.tens[current] + if value is None: + value = tens + elif isinstance(value, str): + value = str(value) + str(tens) + else: + if value % 100 == 0: + value += tens + else: + value = str(value) + str(tens) + elif current in self.tens_suffixed: + # ordinal or cardinal; yield the number right away + tens, suffix = self.tens_suffixed[current] + if value is None: + yield output(str(tens) + suffix) + elif isinstance(value, str): + yield output(str(value) + str(tens) + suffix) + else: + if value % 100 == 0: + yield output(str(value + tens) + suffix) + else: + yield output(str(value) + str(tens) + suffix) + elif current in self.multipliers: + multiplier = self.multipliers[current] + if value is None: + value = multiplier + elif isinstance(value, str) or value == 0: + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + value = p.numerator + else: + yield output(value) + value = multiplier + else: + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + elif current in self.multipliers_suffixed: + multiplier, suffix = self.multipliers_suffixed[current] + if value is None: + yield output(str(multiplier) + suffix) + elif isinstance(value, str): + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + yield output(str(p.numerator) + suffix) + else: + yield output(value) + yield output(str(multiplier) + suffix) + else: # int + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + yield output(str(value) + suffix) + value = None + elif current in self.preceding_prefixers: + # apply prefix (positive, minus, etc.) if it precedes a number + if value is not None: + yield output(value) + + if next in self.words or next_is_numeric: + prefix = self.preceding_prefixers[current] + else: + yield output(current) + elif current in self.following_prefixers: + # apply prefix (dollars, cents, etc.) only after a number + if value is not None: + prefix = self.following_prefixers[current] + yield output(value) + else: + yield output(current) + elif current in self.suffixers: + # apply suffix symbols (percent -> '%') + if value is not None: + suffix = self.suffixers[current] + if isinstance(suffix, dict): + if next in suffix: + yield output(str(value) + suffix[next]) + skip = True + else: + yield output(value) + yield output(current) + else: + yield output(str(value) + suffix) + else: + yield output(current) + elif current in self.specials: + if next not in self.words and not next_is_numeric: + # apply special handling only if the next word can be numeric + if value is not None: + yield output(value) + yield output(current) + elif current == "and": + # ignore "and" after hundreds, thousands, etc. + if prev not in self.multipliers: + if value is not None: + yield output(value) + yield output(current) + elif current == "double" or current == "triple": + if next in self.ones or next in self.zeros: + repeats = 2 if current == "double" else 3 + ones = self.ones.get(next, 0) + value = str(value or "") + str(ones) * repeats + skip = True + else: + if value is not None: + yield output(value) + yield output(current) + elif current == "point": + if next in self.decimals or next_is_numeric: + value = str(value or "") + "." + else: + # should all have been covered at this point + raise ValueError(f"Unexpected token: {current}") + else: + # all should have been covered at this point + raise ValueError(f"Unexpected token: {current}") + + if value is not None: + yield output(value) + + def preprocess(self, s: str): + # replace " and a half" with " point five" + results = [] + + segments = re.split(r"\band\s+a\s+half\b", s) + for i, segment in enumerate(segments): + if len(segment.strip()) == 0: + continue + if i == len(segments) - 1: + results.append(segment) + else: + results.append(segment) + last_word = segment.rsplit(maxsplit=2)[-1] + if last_word in self.decimals or last_word in self.multipliers: + results.append("point five") + else: + results.append("and a half") + + s = " ".join(results) + + # put a space at number/letter boundary + s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) + s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) + + # but remove spaces which could be a suffix + s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) + + return s + + def postprocess(self, s: str): + def combine_cents(m: Match): + try: + currency = m.group(1) + integer = m.group(2) + cents = int(m.group(3)) + return f"{currency}{integer}.{cents:02d}" + except ValueError: + return m.string + + def extract_cents(m: Match): + try: + return f"¢{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ¢7" -> "$2.07" + s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) + + # write "one(s)" instead of "1(s)", just for the readability + s = re.sub(r"\b1(s?)\b", r"one\1", s) + + return s + + def __call__(self, s: str): + s = self.preprocess(s) + s = " ".join(word for word in self.process_words(s.split()) if word is not None) + s = self.postprocess(s) + + return s + + +class EnglishSpellingNormalizer: + """ + Applies British-American spelling mappings as listed in [1]. + + [1] https://www.tysto.com/uk-us-spelling-list.html + """ + + def __init__(self): + mapping_path = os.path.join(os.path.dirname(__file__), "english.json") + self.mapping = json.load(open(mapping_path)) + + def __call__(self, s: str): + return " ".join(self.mapping.get(word, word) for word in s.split()) + + +class EnglishTextNormalizer: + def __init__(self): + self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" + self.replacers = { + # common contractions + r"\bwon't\b": "will not", + r"\bcan't\b": "can not", + r"\blet's\b": "let us", + r"\bain't\b": "aint", + r"\by'all\b": "you all", + r"\bwanna\b": "want to", + r"\bgotta\b": "got to", + r"\bgonna\b": "going to", + r"\bi'ma\b": "i am going to", + r"\bimma\b": "i am going to", + r"\bwoulda\b": "would have", + r"\bcoulda\b": "could have", + r"\bshoulda\b": "should have", + r"\bma'am\b": "madam", + # contractions in titles/prefixes + r"\bmr\b": "mister ", + r"\bmrs\b": "missus ", + r"\bst\b": "saint ", + r"\bdr\b": "doctor ", + r"\bprof\b": "professor ", + r"\bcapt\b": "captain ", + r"\bgov\b": "governor ", + r"\bald\b": "alderman ", + r"\bgen\b": "general ", + r"\bsen\b": "senator ", + r"\brep\b": "representative ", + r"\bpres\b": "president ", + r"\brev\b": "reverend ", + r"\bhon\b": "honorable ", + r"\basst\b": "assistant ", + r"\bassoc\b": "associate ", + r"\blt\b": "lieutenant ", + r"\bcol\b": "colonel ", + r"\bjr\b": "junior ", + r"\bsr\b": "senior ", + r"\besq\b": "esquire ", + # prefect tenses, ideally it should be any past participles, but it's harder.. + r"'d been\b": " had been", + r"'s been\b": " has been", + r"'d gone\b": " had gone", + r"'s gone\b": " has gone", + r"'d done\b": " had done", # "'s done" is ambiguous + r"'s got\b": " has got", + # general contractions + r"n't\b": " not", + r"'re\b": " are", + r"'s\b": " is", + r"'d\b": " would", + r"'ll\b": " will", + r"'t\b": " not", + r"'ve\b": " have", + r"'m\b": " am", + } + self.standardize_numbers = EnglishNumberNormalizer() + self.standardize_spellings = EnglishSpellingNormalizer() + + def __call__(self, s: str): + s = s.lower() + + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = re.sub(self.ignore_patterns, "", s) + s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe + + for pattern, replacement in self.replacers.items(): + s = re.sub(pattern, replacement, s) + + s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits + s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics + + s = self.standardize_numbers(s) + s = self.standardize_spellings(s) + + # now remove prefix/suffix symbols that are not preceded/followed by numbers + s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) + s = re.sub(r"([^0-9])%", r"\1 ", s) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + + return s diff --git a/latentsync/whisper/whisper/tokenizer.py b/latentsync/whisper/whisper/tokenizer.py index 88142c4..3e5cd42 100644 --- a/latentsync/whisper/whisper/tokenizer.py +++ b/latentsync/whisper/whisper/tokenizer.py @@ -1,331 +1,331 @@ -import os -from dataclasses import dataclass -from functools import lru_cache -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch -from transformers import GPT2TokenizerFast - -LANGUAGES = { - "en": "english", - "zh": "chinese", - "de": "german", - "es": "spanish", - "ru": "russian", - "ko": "korean", - "fr": "french", - "ja": "japanese", - "pt": "portuguese", - "tr": "turkish", - "pl": "polish", - "ca": "catalan", - "nl": "dutch", - "ar": "arabic", - "sv": "swedish", - "it": "italian", - "id": "indonesian", - "hi": "hindi", - "fi": "finnish", - "vi": "vietnamese", - "iw": "hebrew", - "uk": "ukrainian", - "el": "greek", - "ms": "malay", - "cs": "czech", - "ro": "romanian", - "da": "danish", - "hu": "hungarian", - "ta": "tamil", - "no": "norwegian", - "th": "thai", - "ur": "urdu", - "hr": "croatian", - "bg": "bulgarian", - "lt": "lithuanian", - "la": "latin", - "mi": "maori", - "ml": "malayalam", - "cy": "welsh", - "sk": "slovak", - "te": "telugu", - "fa": "persian", - "lv": "latvian", - "bn": "bengali", - "sr": "serbian", - "az": "azerbaijani", - "sl": "slovenian", - "kn": "kannada", - "et": "estonian", - "mk": "macedonian", - "br": "breton", - "eu": "basque", - "is": "icelandic", - "hy": "armenian", - "ne": "nepali", - "mn": "mongolian", - "bs": "bosnian", - "kk": "kazakh", - "sq": "albanian", - "sw": "swahili", - "gl": "galician", - "mr": "marathi", - "pa": "punjabi", - "si": "sinhala", - "km": "khmer", - "sn": "shona", - "yo": "yoruba", - "so": "somali", - "af": "afrikaans", - "oc": "occitan", - "ka": "georgian", - "be": "belarusian", - "tg": "tajik", - "sd": "sindhi", - "gu": "gujarati", - "am": "amharic", - "yi": "yiddish", - "lo": "lao", - "uz": "uzbek", - "fo": "faroese", - "ht": "haitian creole", - "ps": "pashto", - "tk": "turkmen", - "nn": "nynorsk", - "mt": "maltese", - "sa": "sanskrit", - "lb": "luxembourgish", - "my": "myanmar", - "bo": "tibetan", - "tl": "tagalog", - "mg": "malagasy", - "as": "assamese", - "tt": "tatar", - "haw": "hawaiian", - "ln": "lingala", - "ha": "hausa", - "ba": "bashkir", - "jw": "javanese", - "su": "sundanese", -} - -# language code lookup by name, with a few language aliases -TO_LANGUAGE_CODE = { - **{language: code for code, language in LANGUAGES.items()}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", -} - - -@dataclass(frozen=True) -class Tokenizer: - """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" - - tokenizer: "GPT2TokenizerFast" - language: Optional[str] - sot_sequence: Tuple[int] - - def encode(self, text, **kwargs): - return self.tokenizer.encode(text, **kwargs) - - def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): - return self.tokenizer.decode(token_ids, **kwargs) - - def decode_with_timestamps(self, tokens) -> str: - """ - Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. - This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". - """ - outputs = [[]] - for token in tokens: - if token >= self.timestamp_begin: - timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" - outputs.append(timestamp) - outputs.append([]) - else: - outputs[-1].append(token) - outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] - return "".join(outputs) - - @property - @lru_cache() - def eot(self) -> int: - return self.tokenizer.eos_token_id - - @property - @lru_cache() - def sot(self) -> int: - return self._get_single_token_id("<|startoftranscript|>") - - @property - @lru_cache() - def sot_lm(self) -> int: - return self._get_single_token_id("<|startoflm|>") - - @property - @lru_cache() - def sot_prev(self) -> int: - return self._get_single_token_id("<|startofprev|>") - - @property - @lru_cache() - def no_speech(self) -> int: - return self._get_single_token_id("<|nospeech|>") - - @property - @lru_cache() - def no_timestamps(self) -> int: - return self._get_single_token_id("<|notimestamps|>") - - @property - @lru_cache() - def timestamp_begin(self) -> int: - return self.tokenizer.all_special_ids[-1] + 1 - - @property - @lru_cache() - def language_token(self) -> int: - """Returns the token id corresponding to the value of the `language` field""" - if self.language is None: - raise ValueError(f"This tokenizer does not have language token configured") - - additional_tokens = dict( - zip( - self.tokenizer.additional_special_tokens, - self.tokenizer.additional_special_tokens_ids, - ) - ) - candidate = f"<|{self.language}|>" - if candidate in additional_tokens: - return additional_tokens[candidate] - - raise KeyError(f"Language {self.language} not found in tokenizer.") - - @property - @lru_cache() - def all_language_tokens(self) -> Tuple[int]: - result = [] - for token, token_id in zip( - self.tokenizer.additional_special_tokens, - self.tokenizer.additional_special_tokens_ids, - ): - if token.strip("<|>") in LANGUAGES: - result.append(token_id) - return tuple(result) - - @property - @lru_cache() - def all_language_codes(self) -> Tuple[str]: - return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) - - @property - @lru_cache() - def sot_sequence_including_notimestamps(self) -> Tuple[int]: - return tuple(list(self.sot_sequence) + [self.no_timestamps]) - - @property - @lru_cache() - def non_speech_tokens(self) -> Tuple[int]: - """ - Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech - annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. - - - ♪♪♪ - - ( SPEAKING FOREIGN LANGUAGE ) - - [DAVID] Hey there, - - keeping basic punctuations like commas, periods, question marks, exclamation points, etc. - """ - symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") - symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() - - # symbols that may be a single token or multiple tokens depending on the tokenizer. - # In case they're multiple tokens, suppress the first token, which is safe because: - # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress - # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. - miscellaneous = set("♩♪♫♬♭♮♯") - assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) - - # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word - result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} - for symbol in symbols + list(miscellaneous): - for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: - if len(tokens) == 1 or symbol in miscellaneous: - result.add(tokens[0]) - - return tuple(sorted(result)) - - def _get_single_token_id(self, text) -> int: - tokens = self.tokenizer.encode(text) - assert len(tokens) == 1, f"{text} is not encoded as a single token" - return tokens[0] - - -@lru_cache(maxsize=None) -def build_tokenizer(name: str = "gpt2"): - os.environ["TOKENIZERS_PARALLELISM"] = "false" - path = os.path.join(os.path.dirname(__file__), "assets", name) - tokenizer = GPT2TokenizerFast.from_pretrained(path) - - specials = [ - "<|startoftranscript|>", - *[f"<|{lang}|>" for lang in LANGUAGES.keys()], - "<|translate|>", - "<|transcribe|>", - "<|startoflm|>", - "<|startofprev|>", - "<|nospeech|>", - "<|notimestamps|>", - ] - - tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) - return tokenizer - - -@lru_cache(maxsize=None) -def get_tokenizer( - multilingual: bool, - *, - task: Optional[str] = None, # Literal["transcribe", "translate", None] - language: Optional[str] = None, -) -> Tokenizer: - if language is not None: - language = language.lower() - if language not in LANGUAGES: - if language in TO_LANGUAGE_CODE: - language = TO_LANGUAGE_CODE[language] - else: - raise ValueError(f"Unsupported language: {language}") - - if multilingual: - tokenizer_name = "multilingual" - task = task or "transcribe" - language = language or "en" - else: - tokenizer_name = "gpt2" - task = None - language = None - - tokenizer = build_tokenizer(name=tokenizer_name) - all_special_ids: List[int] = tokenizer.all_special_ids - sot: int = all_special_ids[1] - translate: int = all_special_ids[-6] - transcribe: int = all_special_ids[-5] - - langs = tuple(LANGUAGES.keys()) - sot_sequence = [sot] - if language is not None: - sot_sequence.append(sot + 1 + langs.index(language)) - if task is not None: - sot_sequence.append(transcribe if task == "transcribe" else translate) - - return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import GPT2TokenizerFast + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "iw": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", +} + + +@dataclass(frozen=True) +class Tokenizer: + """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" + + tokenizer: "GPT2TokenizerFast" + language: Optional[str] + sot_sequence: Tuple[int] + + def encode(self, text, **kwargs): + return self.tokenizer.encode(text, **kwargs) + + def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): + return self.tokenizer.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, tokens) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + outputs = [[]] + for token in tokens: + if token >= self.timestamp_begin: + timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" + outputs.append(timestamp) + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] + return "".join(outputs) + + @property + @lru_cache() + def eot(self) -> int: + return self.tokenizer.eos_token_id + + @property + @lru_cache() + def sot(self) -> int: + return self._get_single_token_id("<|startoftranscript|>") + + @property + @lru_cache() + def sot_lm(self) -> int: + return self._get_single_token_id("<|startoflm|>") + + @property + @lru_cache() + def sot_prev(self) -> int: + return self._get_single_token_id("<|startofprev|>") + + @property + @lru_cache() + def no_speech(self) -> int: + return self._get_single_token_id("<|nospeech|>") + + @property + @lru_cache() + def no_timestamps(self) -> int: + return self._get_single_token_id("<|notimestamps|>") + + @property + @lru_cache() + def timestamp_begin(self) -> int: + return self.tokenizer.all_special_ids[-1] + 1 + + @property + @lru_cache() + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError(f"This tokenizer does not have language token configured") + + additional_tokens = dict( + zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ) + ) + candidate = f"<|{self.language}|>" + if candidate in additional_tokens: + return additional_tokens[candidate] + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @property + @lru_cache() + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @property + @lru_cache() + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @property + @lru_cache() + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @property + @lru_cache() + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") + symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def _get_single_token_id(self, text) -> int: + tokens = self.tokenizer.encode(text) + assert len(tokens) == 1, f"{text} is not encoded as a single token" + return tokens[0] + + +@lru_cache(maxsize=None) +def build_tokenizer(name: str = "gpt2"): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + path = os.path.join(os.path.dirname(__file__), "assets", name) + tokenizer = GPT2TokenizerFast.from_pretrained(path) + + specials = [ + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + ] + + tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) + return tokenizer + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + task: Optional[str] = None, # Literal["transcribe", "translate", None] + language: Optional[str] = None, +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + tokenizer_name = "multilingual" + task = task or "transcribe" + language = language or "en" + else: + tokenizer_name = "gpt2" + task = None + language = None + + tokenizer = build_tokenizer(name=tokenizer_name) + all_special_ids: List[int] = tokenizer.all_special_ids + sot: int = all_special_ids[1] + translate: int = all_special_ids[-6] + transcribe: int = all_special_ids[-5] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if language is not None: + sot_sequence.append(sot + 1 + langs.index(language)) + if task is not None: + sot_sequence.append(transcribe if task == "transcribe" else translate) + + return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) diff --git a/latentsync/whisper/whisper/transcribe.py b/latentsync/whisper/whisper/transcribe.py index 745e775..629142e 100644 --- a/latentsync/whisper/whisper/transcribe.py +++ b/latentsync/whisper/whisper/transcribe.py @@ -1,207 +1,207 @@ -import argparse -import os -import warnings -from typing import List, Optional, Tuple, Union, TYPE_CHECKING - -import numpy as np -import torch -import tqdm - -from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram -from .decoding import DecodingOptions, DecodingResult -from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer -from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt - -if TYPE_CHECKING: - from .model import Whisper - - -def transcribe( - model: "Whisper", - audio: Union[str, np.ndarray, torch.Tensor], - *, - verbose: Optional[bool] = None, - temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), - compression_ratio_threshold: Optional[float] = 2.4, - logprob_threshold: Optional[float] = -1.0, - no_speech_threshold: Optional[float] = 0.6, - condition_on_previous_text: bool = True, - force_extraction: bool = False, - **decode_options, -): - """ - Transcribe an audio file using Whisper - - Parameters - ---------- - model: Whisper - The Whisper model instance - - audio: Union[str, np.ndarray, torch.Tensor] - The path to the audio file to open, or the audio waveform - - verbose: bool - Whether to display the text being decoded to the console. If True, displays all the details, - If False, displays minimal details. If None, does not display anything - - temperature: Union[float, Tuple[float, ...]] - Temperature for sampling. It can be a tuple of temperatures, which will be successfully used - upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. - - compression_ratio_threshold: float - If the gzip compression ratio is above this value, treat as failed - - logprob_threshold: float - If the average log probability over sampled tokens is below this value, treat as failed - - no_speech_threshold: float - If the no_speech probability is higher than this value AND the average log probability - over sampled tokens is below `logprob_threshold`, consider the segment as silent - - condition_on_previous_text: bool - if True, the previous output of the model is provided as a prompt for the next window; - disabling may make the text inconsistent across windows, but the model becomes less prone to - getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. - - decode_options: dict - Keyword arguments to construct `DecodingOptions` instances - - Returns - ------- - A dictionary containing the resulting text ("text") and segment-level details ("segments"), and - the spoken language ("language"), which is detected when `decode_options["language"]` is None. - """ - dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 - if model.device == torch.device("cpu"): - if torch.cuda.is_available(): - warnings.warn("Performing inference on CPU when CUDA is available") - if dtype == torch.float16: - warnings.warn("FP16 is not supported on CPU; using FP32 instead") - dtype = torch.float32 - - if dtype == torch.float32: - decode_options["fp16"] = False - - mel = log_mel_spectrogram(audio) - - all_segments = [] - def add_segment( - *, start: float, end: float, encoder_embeddings - ): - - all_segments.append( - { - "start": start, - "end": end, - "encoder_embeddings":encoder_embeddings, - } - ) - # show the progress bar when verbose is False (otherwise the transcribed text will be printed) - num_frames = mel.shape[-1] - seek = 0 - previous_seek_value = seek - sample_skip = 3000 # - with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: - while seek < num_frames: - # seek是开始的帧数 - end_seek = min(seek + sample_skip, num_frames) - segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype) - - single = segment.ndim == 2 - if single: - segment = segment.unsqueeze(0) - if dtype == torch.float16: - segment = segment.half() - audio_features, embeddings = model.encoder(segment, include_embeddings = True) - - encoder_embeddings = embeddings - #print(f"encoder_embeddings shape {encoder_embeddings.shape}") - add_segment( - start=seek, - end=end_seek, - #text_tokens=tokens, - #result=result, - encoder_embeddings=encoder_embeddings, - ) - seek+=sample_skip - - return dict(segments=all_segments) - - -def cli(): - from . import available_models - - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") - parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") - parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") - parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") - parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") - parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") - - parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") - parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") - - parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") - parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") - parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") - parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") - parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") - - parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") - parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") - parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") - parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") - - parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") - parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") - parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") - parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") - parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") - - args = parser.parse_args().__dict__ - model_name: str = args.pop("model") - model_dir: str = args.pop("model_dir") - output_dir: str = args.pop("output_dir") - device: str = args.pop("device") - os.makedirs(output_dir, exist_ok=True) - - if model_name.endswith(".en") and args["language"] not in {"en", "English"}: - if args["language"] is not None: - warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") - args["language"] = "en" - - temperature = args.pop("temperature") - temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") - if temperature_increment_on_fallback is not None: - temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) - else: - temperature = [temperature] - - threads = args.pop("threads") - if threads > 0: - torch.set_num_threads(threads) - - from . import load_model - model = load_model(model_name, device=device, download_root=model_dir) - - for audio_path in args.pop("audio"): - result = transcribe(model, audio_path, temperature=temperature, **args) - - audio_basename = os.path.basename(audio_path) - - # save TXT - with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: - write_txt(result["segments"], file=txt) - - # save VTT - with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: - write_vtt(result["segments"], file=vtt) - - # save SRT - with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: - write_srt(result["segments"], file=srt) - - -if __name__ == '__main__': - cli() +import argparse +import os +import warnings +from typing import List, Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np +import torch +import tqdm + +from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram +from .decoding import DecodingOptions, DecodingResult +from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer +from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + force_extraction: bool = False, + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successfully used + upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. + + compression_ratio_threshold: float + If the gzip compression ratio is above this value, treat as failed + + logprob_threshold: float + If the average log probability over sampled tokens is below this value, treat as failed + + no_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + decode_options: dict + Keyword arguments to construct `DecodingOptions` instances + + Returns + ------- + A dictionary containing the resulting text ("text") and segment-level details ("segments"), and + the spoken language ("language"), which is detected when `decode_options["language"]` is None. + """ + dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 + if model.device == torch.device("cpu"): + if torch.cuda.is_available(): + warnings.warn("Performing inference on CPU when CUDA is available") + if dtype == torch.float16: + warnings.warn("FP16 is not supported on CPU; using FP32 instead") + dtype = torch.float32 + + if dtype == torch.float32: + decode_options["fp16"] = False + + mel = log_mel_spectrogram(audio) + + all_segments = [] + def add_segment( + *, start: float, end: float, encoder_embeddings + ): + + all_segments.append( + { + "start": start, + "end": end, + "encoder_embeddings":encoder_embeddings, + } + ) + # show the progress bar when verbose is False (otherwise the transcribed text will be printed) + num_frames = mel.shape[-1] + seek = 0 + previous_seek_value = seek + sample_skip = 3000 # + with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: + while seek < num_frames: + # seek是开始的帧数 + end_seek = min(seek + sample_skip, num_frames) + segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype) + + single = segment.ndim == 2 + if single: + segment = segment.unsqueeze(0) + if dtype == torch.float16: + segment = segment.half() + audio_features, embeddings = model.encoder(segment, include_embeddings = True) + + encoder_embeddings = embeddings + #print(f"encoder_embeddings shape {encoder_embeddings.shape}") + add_segment( + start=seek, + end=end_seek, + #text_tokens=tokens, + #result=result, + encoder_embeddings=encoder_embeddings, + ) + seek+=sample_skip + + return dict(segments=all_segments) + + +def cli(): + from . import available_models + + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") + parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") + parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") + parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") + parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") + + parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") + parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") + + parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") + parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") + parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") + parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") + parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") + + parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") + parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") + parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") + parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") + + parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") + parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") + parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") + parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") + args["language"] = "en" + + temperature = args.pop("temperature") + temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") + if temperature_increment_on_fallback is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) + else: + temperature = [temperature] + + threads = args.pop("threads") + if threads > 0: + torch.set_num_threads(threads) + + from . import load_model + model = load_model(model_name, device=device, download_root=model_dir) + + for audio_path in args.pop("audio"): + result = transcribe(model, audio_path, temperature=temperature, **args) + + audio_basename = os.path.basename(audio_path) + + # save TXT + with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: + write_txt(result["segments"], file=txt) + + # save VTT + with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: + write_vtt(result["segments"], file=vtt) + + # save SRT + with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + + +if __name__ == '__main__': + cli() diff --git a/latentsync/whisper/whisper/utils.py b/latentsync/whisper/whisper/utils.py index 87c91f2..5c38747 100644 --- a/latentsync/whisper/whisper/utils.py +++ b/latentsync/whisper/whisper/utils.py @@ -1,87 +1,87 @@ -import zlib -from typing import Iterator, TextIO - - -def exact_div(x, y): - assert x % y == 0 - return x // y - - -def str2bool(string): - str2val = {"True": True, "False": False} - if string in str2val: - return str2val[string] - else: - raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") - - -def optional_int(string): - return None if string == "None" else int(string) - - -def optional_float(string): - return None if string == "None" else float(string) - - -def compression_ratio(text) -> float: - return len(text) / len(zlib.compress(text.encode("utf-8"))) - - -def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'): - assert seconds >= 0, "non-negative timestamp expected" - milliseconds = round(seconds * 1000.0) - - hours = milliseconds // 3_600_000 - milliseconds -= hours * 3_600_000 - - minutes = milliseconds // 60_000 - milliseconds -= minutes * 60_000 - - seconds = milliseconds // 1_000 - milliseconds -= seconds * 1_000 - - hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" - return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" - - -def write_txt(transcript: Iterator[dict], file: TextIO): - for segment in transcript: - print(segment['text'].strip(), file=file, flush=True) - - -def write_vtt(transcript: Iterator[dict], file: TextIO): - print("WEBVTT\n", file=file) - for segment in transcript: - print( - f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) - - -def write_srt(transcript: Iterator[dict], file: TextIO): - """ - Write a transcript to a file in SRT format. - - Example usage: - from pathlib import Path - from whisper.utils import write_srt - - result = transcribe(model, audio_path, temperature=temperature, **args) - - # save SRT - audio_basename = Path(audio_path).stem - with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: - write_srt(result["segments"], file=srt) - """ - for i, segment in enumerate(transcript, start=1): - # write srt lines - print( - f"{i}\n" - f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " - f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) +import zlib +from typing import Iterator, TextIO + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + return len(text) / len(zlib.compress(text.encode("utf-8"))) + + +def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + + +def write_txt(transcript: Iterator[dict], file: TextIO): + for segment in transcript: + print(segment['text'].strip(), file=file, flush=True) + + +def write_vtt(transcript: Iterator[dict], file: TextIO): + print("WEBVTT\n", file=file) + for segment in transcript: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +def write_srt(transcript: Iterator[dict], file: TextIO): + """ + Write a transcript to a file in SRT format. + + Example usage: + from pathlib import Path + from whisper.utils import write_srt + + result = transcribe(model, audio_path, temperature=temperature, **args) + + # save SRT + audio_basename = Path(audio_path).stem + with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + """ + for i, segment in enumerate(transcript, start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + )