diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/README.md b/README.md
index 35bae4c..06de30d 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,11 @@
# FireRedTTS-ComfyUI
-a custom node for [FireRedTTS](https://github.com/FireRedTeam/FireRedTTS)
+a custom node for [FireRedTTS](https://github.com/FireRedTeam/FireRedTTS),you can find [workflow here](./doc/base_workflow.json)
+
+## Example
+|text|prompt_wav|out_wav|
+|--|--|--|
+|`《三体》是刘慈欣创作的长篇科幻小说系列，由《三体》《三体2：黑暗森林》《三体3：死神永生》组成，第一部于2006年5月起在《科幻世界》杂志上连载，第二部于2008年5月首次出版，第三部则于2010年11月出版。作品讲述了地球人类文明和三体文明的信息交流、生死搏杀及两个文明在宇宙中的兴衰历程。其第一部经过刘宇昆翻译后获得了第73届雨果奖最佳长篇小说奖，第三部英文版获得2017年轨迹奖最佳长篇科幻小说奖。2019年，列入“新中国70年70部长篇小说典藏”。2022年9月，《三体》入选2021十大年度国家IP。`| ||
+
+## Features
+- speed control
+- auto split text
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..24d75ac
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,275 @@
+import os,sys
+import os.path as osp
+now_dir = osp.dirname(osp.abspath(__file__))
+sys.path.append(now_dir)
+tmp_dir = osp.join(now_dir, "tmp")
+import re
+import torch
+import ffmpeg
+import shutil
+import tempfile
+import torchaudio
+import folder_paths
+import numpy as np
+from tqdm import tqdm
+from comfy.utils import ProgressBar
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+from huggingface_hub import snapshot_download
+from fireredtts.fireredtts import FireRedTTS
+from zhon.hanzi import punctuation
+SPLIT_WORDS = [
+ "but", "however", "nevertheless", "yet", "still",
+ "therefore", "thus", "hence", "consequently",
+ "moreover", "furthermore", "additionally",
+ "meanwhile", "alternatively", "otherwise",
+ "namely", "specifically", "for example", "such as",
+ "in fact", "indeed", "notably",
+ "in contrast", "on the other hand", "conversely",
+ "in conclusion", "to summarize", "finally"
+]
+device = (
+ "cuda"
+ if torch.cuda.is_available()
+ else "mps" if torch.backends.mps.is_available() else "cpu"
+)
+aifsh_models = osp.join(folder_paths.models_dir,"AIFSH")
+fireredtss_dir = osp.join(aifsh_models,"FireRedTTS")
+
+def speed_change(input_audio, speed, sr):
+ # 检查输入数据类型和声道数
+ if input_audio.dtype != np.int16:
+ raise ValueError("输入音频数据类型必须为 np.int16")
+
+
+ # 转换为字节流
+ raw_audio = input_audio.astype(np.int16).tobytes()
+
+ # 设置 ffmpeg 输入流
+ input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
+
+ # 变速处理
+ output_stream = input_stream.filter('atempo', speed)
+
+ # 输出流到管道
+ out, _ = (
+ output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
+ .run(input=raw_audio, capture_stdout=True, capture_stderr=True)
+ )
+
+ # 将管道输出解码为 NumPy 数组
+ processed_audio = np.frombuffer(out, np.int16)
+
+ return processed_audio
+
+class FireRedTTSNode:
+ def __init__(self):
+ if not osp.exists(osp.join(fireredtss_dir,"fireredtts_gpt.pt")):
+ snapshot_download(repo_id="fireredteam/FireRedTTS",local_dir=fireredtss_dir)
+ @classmethod
+ def INPUT_TYPES(s):
+
+ return {
+ "required":{
+ "text":("TEXT",),
+ "prompt_wav":("AUDIO",),
+ "remove_slience":("BOOLEAN",{
+ "default": True
+ }),
+ "speed":("FLOAT",{
+ "default":1.0,
+ "min":0.5,
+ "max":2.0,
+ "step":0.05,
+ "round":0.001,
+ "display":"slider"
+ }),
+ "split_words":("STRING",{
+ "default":",".join(SPLIT_WORDS),
+ "multiline": True,
+ "dynamicPrompts": True,
+ "tooltip":"Enter custom words to split on, separated by commas. Leave blank to use default list.",
+ })
+ }
+ }
+
+ RETURN_TYPES = ("AUDIO",)
+ #RETURN_NAMES = ("image_output_name",)
+
+ FUNCTION = "gen_audio"
+
+ #OUTPUT_NODE = False
+
+ CATEGORY = "AIFSH_FireRedTSS"
+
+ def gen_audio(self,text,prompt_wav,remove_slience,speed,split_words):
+ os.makedirs(tmp_dir, exist_ok=True)
+ with tempfile.NamedTemporaryFile(delete=False, suffix=".wav",dir=tmp_dir) as f:
+ waveform = prompt_wav["waveform"].squeeze(0)
+ torchaudio.save(f.name,waveform,prompt_wav["sample_rate"])
+ if remove_slience:
+ combined = slice(f.name)
+ combined.export(f.name, format="wav")
+ prompt_wav = f.name
+ # Split the input text into batches
+ if len(text.encode('utf-8')) == len(text):
+ max_chars = 400-len(text.encode('utf-8'))
+ else:
+ max_chars = 300-len(text.encode('utf-8'))
+
+ if not split_words.strip():
+ custom_words = [word.strip() for word in split_words.split(',')]
+ global SPLIT_WORDS
+ SPLIT_WORDS = custom_words
+ gen_text_batches = split_text_into_batches(text, max_chars=max_chars)
+ comfy_par = ProgressBar(len(gen_text_batches))
+ tts = FireRedTTS(config_path=osp.join(now_dir,"config_24k.json"),
+ pretrained_path=fireredtss_dir,device=device)
+ rec_wavs_list = []
+ for i,i_text in enumerate(tqdm(gen_text_batches,total=len(gen_text_batches),desc="TTS ...")):
+ for dot in punctuation:
+ i_text = i_text.replace(dot,"")
+ print(f"sentence {i+1}, synthesize text:{i_text}")
+ rec_wavs = tts.synthesize(prompt_wav=prompt_wav,text=i_text)
+ rec_wavs = rec_wavs.detach().cpu().numpy()
+ rec_wavs_list.append(rec_wavs)
+ comfy_par.update(1)
+ res_np = np.concatenate(rec_wavs_list,axis=1)
+ if speed > 1.0 or speed < 1.0:
+ res_np = res_np * 32768
+ res_np = res_np.astype(np.int16)
+ res_np = speed_change(res_np,speed,sr=24000)
+ waveform = torch.from_numpy(res_np/32768).unsqueeze(0).unsqueeze(0)
+ else:
+ waveform = torch.from_numpy(res_np).unsqueeze(0)
+ print(waveform.shape)
+ res_audio = {
+ "waveform": waveform,
+ "sample_rate": 24000
+ }
+ shutil.rmtree(tmp_dir)
+ return (res_audio, )
+
+def split_text_into_batches(text, max_chars=200, split_words=SPLIT_WORDS):
+ if len(text.encode('utf-8')) <= max_chars:
+ return [text]
+ if text[-1] not in ['。', '.', '!', '！', '?', '？']:
+ text += '.'
+
+ sentences = re.split('([。.!?！？])', text)
+ sentences = [''.join(i) for i in zip(sentences[0::2], sentences[1::2])]
+
+ batches = []
+ current_batch = ""
+
+ def split_by_words(text):
+ words = text.split()
+ current_word_part = ""
+ word_batches = []
+ for word in words:
+ if len(current_word_part.encode('utf-8')) + len(word.encode('utf-8')) + 1 <= max_chars:
+ current_word_part += word + ' '
+ else:
+ if current_word_part:
+ # Try to find a suitable split word
+ for split_word in split_words:
+ split_index = current_word_part.rfind(' ' + split_word + ' ')
+ if split_index != -1:
+ word_batches.append(current_word_part[:split_index].strip())
+ current_word_part = current_word_part[split_index:].strip() + ' '
+ break
+ else:
+ # If no suitable split word found, just append the current part
+ word_batches.append(current_word_part.strip())
+ current_word_part = ""
+ current_word_part += word + ' '
+ if current_word_part:
+ word_batches.append(current_word_part.strip())
+ return word_batches
+
+ for sentence in sentences:
+ if len(current_batch.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars:
+ current_batch += sentence
+ else:
+ # If adding this sentence would exceed the limit
+ if current_batch:
+ batches.append(current_batch)
+ current_batch = ""
+
+ # If the sentence itself is longer than max_chars, split it
+ if len(sentence.encode('utf-8')) > max_chars:
+ # First, try to split by colon
+ colon_parts = sentence.split(':')
+ if len(colon_parts) > 1:
+ for part in colon_parts:
+ if len(part.encode('utf-8')) <= max_chars:
+ batches.append(part)
+ else:
+ # If colon part is still too long, split by comma
+ comma_parts = re.split('[,，]', part)
+ if len(comma_parts) > 1:
+ current_comma_part = ""
+ for comma_part in comma_parts:
+ if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars:
+ current_comma_part += comma_part + ','
+ else:
+ if current_comma_part:
+ batches.append(current_comma_part.rstrip(','))
+ current_comma_part = comma_part + ','
+ if current_comma_part:
+ batches.append(current_comma_part.rstrip(','))
+ else:
+ # If no comma, split by words
+ batches.extend(split_by_words(part))
+ else:
+ # If no colon, split by comma
+ comma_parts = re.split('[,，]', sentence)
+ if len(comma_parts) > 1:
+ current_comma_part = ""
+ for comma_part in comma_parts:
+ if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars:
+ current_comma_part += comma_part + ','
+ else:
+ if current_comma_part:
+ batches.append(current_comma_part.rstrip(','))
+ current_comma_part = comma_part + ','
+ if current_comma_part:
+ batches.append(current_comma_part.rstrip(','))
+ else:
+ # If no comma, split by words
+ batches.extend(split_by_words(sentence))
+ else:
+ current_batch = sentence
+
+ if current_batch:
+ batches.append(current_batch)
+
+ return batches
+
+
+def slice(audio_path):
+ """_summary_
+
+ Args:
+ audio_path (_type_): audio path
+ """
+ try:
+ audio = AudioSegment.from_file(audio_path)
+ except:
+ print(audio_path)
+ return 0
+
+ segments = split_on_silence(
+ audio, min_silence_len=200, silence_thresh=-50, seek_step=100, keep_silence=100
+ )
+
+ print("---segments:\n", segments)
+
+ combined = segments[0]
+ for i in range(1, len(segments)):
+ combined += segments[i]
+ return combined
+
+NODE_CLASS_MAPPINGS = {
+ "FireRedTTSNode": FireRedTTSNode
+}
\ No newline at end of file
diff --git a/config_24k.json b/config_24k.json
new file mode 100644
index 0000000..1b81b5f
--- /dev/null
+++ b/config_24k.json
@@ -0,0 +1,123 @@
+{
+ "gpt": {
+ "gpt_max_audio_tokens": 630,
+ "gpt_max_text_tokens": 402,
+ "gpt_max_prompt_tokens": 70,
+ "gpt_layers": 30,
+ "gpt_n_model_channels": 1024,
+ "gpt_n_heads": 16,
+ "gpt_number_text_tokens": 57341,
+ "gpt_start_text_token": 57187,
+ "gpt_stop_text_token": 57184,
+ "gpt_num_audio_tokens": 16386,
+ "gpt_start_audio_token": 16384,
+ "gpt_stop_audio_token": 16385,
+ "gpt_code_stride_len": 640,
+ "duration_const": 102400,
+ "min_conditioning_length": 48000,
+ "max_conditioning_length": 128000,
+ "max_wav_length": 320000,
+ "max_text_length": 200
+ },
+ "flow": {
+ "output_size": 100,
+ "input_embedding": {
+ "out_channels": 512,
+ "codebook_path": "fireredtts/modules/flow/codebook.npy",
+ "freeze": true
+ },
+ "encoder": {
+ "input_size": 512,
+ "output_size": 512,
+ "attention_heads": 8,
+ "linear_units": 2048,
+ "num_blocks": 6,
+ "dropout_rate": 0.01,
+ "srcattention_start_index": 0,
+ "srcattention_end_index": 2,
+ "attention_dropout_rate": 0.01,
+ "positional_dropout_rate": 0.01,
+ "key_bias": true,
+ "normalize_before": true
+ },
+ "length_regulator": {
+ "channels": 512,
+ "num_blocks": 4
+ },
+ "mel_encoder": {
+ "in_channels": 100,
+ "out_channels": 512,
+ "hidden_channels": 384,
+ "reduction_rate": 4,
+ "n_layers": 2,
+ "n_blocks": 5,
+ "kernel_size": 3
+ },
+ "decoder": {
+ "t_scheduler": "cosine",
+ "inference_cfg_rate": 0.7,
+ "estimator": {
+ "in_channels": 200,
+ "out_channels": 100,
+ "channels": [
+ 256,
+ 256
+ ],
+ "dropout": 0,
+ "attention_head_dim": 64,
+ "n_blocks": 4,
+ "num_mid_blocks": 12,
+ "num_heads": 8,
+ "act_fn": "gelu"
+ }
+ }
+ },
+ "bigvgan": {
+ "num_mels": 100,
+ "upsample_initial_channel": 1536,
+ "upsample_rates": [
+ 5,
+ 3,
+ 2,
+ 2,
+ 2,
+ 2
+ ],
+ "upsample_kernel_sizes": [
+ 11,
+ 7,
+ 4,
+ 4,
+ 4,
+ 4
+ ],
+ "resblock_kernel_sizes": [
+ 3,
+ 7,
+ 11
+ ],
+ "resblock_dilation_sizes": [
+ [
+ 1,
+ 3,
+ 5
+ ],
+ [
+ 1,
+ 3,
+ 5
+ ],
+ [
+ 1,
+ 3,
+ 5
+ ]
+ ],
+ "resblock_type": "1",
+ "snake_logscale": true,
+ "activation": "snakebeta",
+ "use_tanh_at_final": false,
+ "use_bias_at_final": false,
+ "use_cuda_kernel": false
+ }
+}
\ No newline at end of file
diff --git a/doc/base_workflow.json b/doc/base_workflow.json
new file mode 100644
index 0000000..3ee2b40
--- /dev/null
+++ b/doc/base_workflow.json
@@ -0,0 +1,181 @@
+{
+ "last_node_id": 4,
+ "last_link_id": 3,
+ "nodes": [
+ {
+ "id": 3,
+ "type": "LoadAudio",
+ "pos": {
+ "0": 94,
+ "1": 383
+ },
+ "size": {
+ "0": 315,
+ "1": 124
+ },
+ "flags": {},
+ "order": 0,
+ "mode": 0,
+ "inputs": [],
+ "outputs": [
+ {
+ "name": "AUDIO",
+ "type": "AUDIO",
+ "links": [
+ 2
+ ]
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "LoadAudio"
+ },
+ "widgets_values": [
+ "test_zh_1_ref_short.wav",
+ null,
+ ""
+ ]
+ },
+ {
+ "id": 2,
+ "type": "PromptTextNode",
+ "pos": {
+ "0": 26,
+ "1": 73
+ },
+ "size": {
+ "0": 400,
+ "1": 200
+ },
+ "flags": {},
+ "order": 1,
+ "mode": 0,
+ "inputs": [],
+ "outputs": [
+ {
+ "name": "TEXT",
+ "type": "TEXT",
+ "links": [
+ 1
+ ]
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "PromptTextNode"
+ },
+ "widgets_values": [
+ "《三体》是刘慈欣创作的长篇科幻小说系列，由《三体》《三体2：黑暗森林》《三体3：死神永生》组成，第一部于2006年5月起在《科幻世界》杂志上连载，第二部于2008年5月首次出版，第三部则于2010年11月出版。\n作品讲述了地球人类文明和三体文明的信息交流、生死搏杀及两个文明在宇宙中的兴衰历程。其第一部经过刘宇昆翻译后获得了第73届雨果奖最佳长篇小说奖，第三部英文版获得2017年轨迹奖最佳长篇科幻小说奖。2019年，列入“新中国70年70部长篇小说典藏”。2022年9月，《三体》入选2021十大年度国家IP。"
+ ]
+ },
+ {
+ "id": 1,
+ "type": "FireRedTTSNode",
+ "pos": {
+ "0": 504,
+ "1": 86
+ },
+ "size": {
+ "0": 309.60003662109375,
+ "1": 210.80001831054688
+ },
+ "flags": {},
+ "order": 2,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "text",
+ "type": "TEXT",
+ "link": 1
+ },
+ {
+ "name": "prompt_wav",
+ "type": "AUDIO",
+ "link": 2
+ }
+ ],
+ "outputs": [
+ {
+ "name": "AUDIO",
+ "type": "AUDIO",
+ "links": [
+ 3
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {
+ "Node name for S&R": "FireRedTTSNode"
+ },
+ "widgets_values": [
+ true,
+ "but,however,nevertheless,yet,still,therefore,thus,hence,consequently,moreover,furthermore,additionally,meanwhile,alternatively,otherwise,namely,specifically,for example,such as,in fact,indeed,notably,in contrast,on the other hand,conversely,in conclusion,to summarize,finally"
+ ]
+ },
+ {
+ "id": 4,
+ "type": "PreviewAudio",
+ "pos": {
+ "0": 865,
+ "1": 93
+ },
+ "size": {
+ "0": 315,
+ "1": 76
+ },
+ "flags": {},
+ "order": 3,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "audio",
+ "type": "AUDIO",
+ "link": 3
+ }
+ ],
+ "outputs": [],
+ "properties": {
+ "Node name for S&R": "PreviewAudio"
+ },
+ "widgets_values": [
+ null
+ ]
+ }
+ ],
+ "links": [
+ [
+ 1,
+ 2,
+ 0,
+ 1,
+ 0,
+ "TEXT"
+ ],
+ [
+ 2,
+ 3,
+ 0,
+ 1,
+ 1,
+ "AUDIO"
+ ],
+ [
+ 3,
+ 1,
+ 0,
+ 4,
+ 0,
+ "AUDIO"
+ ]
+ ],
+ "groups": [],
+ "config": {},
+ "extra": {
+ "ds": {
+ "scale": 1,
+ "offset": [
+ 0,
+ 0
+ ]
+ }
+ },
+ "version": 0.4
+}
\ No newline at end of file
diff --git a/fireredtts/fireredtts.py b/fireredtts/fireredtts.py
new file mode 100644
index 0000000..876ef9e
--- /dev/null
+++ b/fireredtts/fireredtts.py
@@ -0,0 +1,163 @@
+import os
+import json
+import torch
+from fireredtts.modules.gpt.gpt import GPT
+from fireredtts.modules import Token2Wav, MelSpectrogramExtractor
+from fireredtts.modules.tokenizer.tokenizer import VoiceBpeTokenizer
+from fireredtts.modules.codec.speaker import SpeakerEmbedddingExtractor
+from fireredtts.utils.utils import load_audio
+
+import time
+
+
+class FireRedTTS:
+ def __init__(self, config_path, pretrained_path, device="cuda"):
+ self.device = device
+ self.config = json.load(open(config_path))
+ self.gpt_path = os.path.join(pretrained_path, "fireredtts_gpt.pt")
+ self.token2wav_path = os.path.join(pretrained_path, "fireredtts_token2wav.pt")
+ self.speaker_extractor_path = os.path.join(
+ pretrained_path, "fireredtts_speaker.bin"
+ )
+ assert os.path.exists(self.token2wav_path)
+ assert os.path.exists(self.gpt_path)
+ assert os.path.exists(self.speaker_extractor_path)
+
+ # tokenizer;
+ self.text_tokenizer = VoiceBpeTokenizer()
+
+ # speaker ectractor
+ self.speaker_extractor = SpeakerEmbedddingExtractor(
+ ckpt_path=self.speaker_extractor_path, device=device
+ )
+
+ # load gpt model
+ self.gpt = GPT(
+ start_text_token=self.config["gpt"]["gpt_start_text_token"],
+ stop_text_token=self.config["gpt"]["gpt_stop_text_token"],
+ layers=self.config["gpt"]["gpt_layers"],
+ model_dim=self.config["gpt"]["gpt_n_model_channels"],
+ heads=self.config["gpt"]["gpt_n_heads"],
+ max_text_tokens=self.config["gpt"]["gpt_max_text_tokens"],
+ max_mel_tokens=self.config["gpt"]["gpt_max_audio_tokens"],
+ max_prompt_tokens=self.config["gpt"]["gpt_max_prompt_tokens"],
+ code_stride_len=self.config["gpt"]["gpt_code_stride_len"],
+ number_text_tokens=self.config["gpt"]["gpt_number_text_tokens"],
+ num_audio_tokens=self.config["gpt"]["gpt_num_audio_tokens"],
+ start_audio_token=self.config["gpt"]["gpt_start_audio_token"],
+ stop_audio_token=self.config["gpt"]["gpt_stop_audio_token"],
+ )
+
+ sd = torch.load(self.gpt_path, map_location=device)["model"]
+ self.gpt.load_state_dict(sd, strict=True)
+ self.gpt = self.gpt.to(device=device)
+ self.gpt.eval()
+ self.gpt.init_gpt_for_inference(kv_cache=True)
+
+ # mel-spectrogram extractor
+ self.mel_extractor = MelSpectrogramExtractor()
+
+ # load token2wav model
+ self.token2wav = Token2Wav.init_from_config(self.config)
+ sd = torch.load(self.token2wav_path, map_location="cpu")
+ self.token2wav.load_state_dict(sd, strict=True)
+ self.token2wav.generator.remove_weight_norm()
+ self.token2wav.eval()
+ self.token2wav = self.token2wav.to(device)
+
+ def extract_spk_embeddings(self, prompt_wav):
+ _, _, audio_resampled = load_audio(audiopath=prompt_wav, sampling_rate=16000)
+ audio_len = torch.tensor(
+ data=[audio_resampled.shape[1]], dtype=torch.long, requires_grad=False
+ )
+
+ # speaker embeddings [1,512]
+ spk_embeddings = self.speaker_extractor(
+ audio_resampled.to(device="cuda")
+ ).unsqueeze(0)
+
+ return spk_embeddings
+
+ def do_gpt_inference(self, spk_gpt, text_tokens):
+ """_summary_
+
+ Args:
+ spk_gpt (_type_): speaker embeddidng in gpt
+ text_tokens (_type_): text tokens
+ """
+ with torch.no_grad():
+ gpt_codes = self.gpt.generate(
+ cond_latents=spk_gpt,
+ text_inputs=text_tokens,
+ input_tokens=None,
+ do_sample=True,
+ top_p=0.85,
+ top_k=30,
+ temperature=0.75,
+ num_return_sequences=9,
+ num_beams=1,
+ length_penalty=1.0,
+ repetition_penalty=2.0,
+ output_attentions=False,
+ )
+
+ seqs = []
+ EOS_TOKEN = self.config["gpt"]["gpt_stop_audio_token"]
+ for seq in gpt_codes:
+ index = (seq == EOS_TOKEN).nonzero(as_tuple=True)[0][0]
+ seq = seq[:index]
+ seqs.append(seq)
+
+ sorted_seqs = sorted(seqs, key=lambda i: len(i), reverse=False)
+ gpt_codes = sorted_seqs[2].unsqueeze(0) # [1, len]
+ # sorted_len = [len(l) for l in sorted_seqs]
+ # print("---sorted_len:", sorted_len)
+
+ return gpt_codes
+
+ def synthesize(self, prompt_wav, text, lang="auto"):
+ """_summary_
+
+ Args:
+ prompts_wav (_type_): prompts_wav path
+ text (_type_): text
+ lang (_type_): language of text
+ """
+ # Currently only supports Chinese and English
+ assert lang in ["zh", "en", "auto"]
+ assert os.path.exists(prompt_wav)
+
+ # text to tokens
+ text_tokens = self.text_tokenizer.encode(text=text, lang=lang)
+ text_tokens = torch.IntTensor(text_tokens).unsqueeze(0).to(self.device)
+ assert text_tokens.shape[-1] < 400
+
+ # extract speaker embedding
+ spk_embeddings = self.extract_spk_embeddings(prompt_wav=prompt_wav).unsqueeze(0)
+ with torch.no_grad():
+ spk_gpt = self.gpt.reference_embedding(spk_embeddings)
+
+ # gpt inference
+ gpt_start_time = time.time()
+ gpt_codes = self.do_gpt_inference(spk_gpt=spk_gpt, text_tokens=text_tokens)
+ gpt_end_time = time.time()
+ gpt_dur = gpt_end_time - gpt_start_time
+
+ # prompt mel-spectrogram compute
+ prompt_mel = (
+ self.mel_extractor(wav_path=prompt_wav).unsqueeze(0).to(self.device)
+ )
+ # convert token to waveform (b=1, t)
+ voc_start_time = time.time()
+ rec_wavs = self.token2wav.inference(gpt_codes, prompt_mel, n_timesteps=10)
+ voc_end_time = time.time()
+ voc_dur = voc_end_time - voc_start_time
+ all_dur = voc_end_time - gpt_start_time
+
+ # rtf compute
+ # audio_dur = rec_wavs.shape[-1] / 24000
+ # rtf_gpt = gpt_dur / audio_dur
+ # rtf_voc = voc_dur / audio_dur
+ # rtf_all = all_dur / audio_dur
+
+ return rec_wavs
diff --git a/fireredtts/modules/__init__.py b/fireredtts/modules/__init__.py
new file mode 100644
index 0000000..bfbf67d
--- /dev/null
+++ b/fireredtts/modules/__init__.py
@@ -0,0 +1,42 @@
+import json
+import torch
+import torch.nn as nn
+from fireredtts.modules.bigvgan import get_bigvgan_backend
+from fireredtts.modules.flow import get_flow_frontend, MelSpectrogramExtractor
+
+
+class Token2Wav(nn.Module):
+ def __init__(
+ self,
+ flow: nn.Module,
+ generator: nn.Module,
+ ):
+ super().__init__()
+ self.flow = flow
+ self.generator = generator
+
+ @torch.no_grad()
+ def inference(
+ self, tokens: torch.Tensor, prompt_mel: torch.Tensor, n_timesteps: int = 10
+ ) -> torch.Tensor:
+ token_len = torch.tensor([tokens.shape[1]], dtype=torch.long).to(tokens.device)
+ prompt_mel_len = torch.tensor([prompt_mel.shape[1]], dtype=torch.long).to(
+ prompt_mel.device
+ )
+ # flow
+ mel = self.flow.inference(
+ token=tokens,
+ token_len=token_len,
+ prompt_mel=prompt_mel,
+ prompt_mel_len=prompt_mel_len,
+ n_timesteps=n_timesteps,
+ )
+ # bigvgan
+ audio = self.generator(mel) # (b=1, 1, t)
+ return audio.squeeze(1)
+
+ @classmethod
+ def init_from_config(cls, config) -> "Token2Wav":
+ flow = get_flow_frontend(config["flow"])
+ bigvgan = get_bigvgan_backend(config["bigvgan"])
+ return cls(flow, bigvgan)
diff --git a/fireredtts/modules/bigvgan/__init__.py b/fireredtts/modules/bigvgan/__init__.py
new file mode 100644
index 0000000..88da4b2
--- /dev/null
+++ b/fireredtts/modules/bigvgan/__init__.py
@@ -0,0 +1,6 @@
+from fireredtts.modules.bigvgan.bigvgan import BigVGAN
+
+
+def get_bigvgan_backend(bigvgan_config):
+ generator = BigVGAN(**bigvgan_config)
+ return generator
diff --git a/fireredtts/modules/bigvgan/activations.py b/fireredtts/modules/bigvgan/activations.py
new file mode 100644
index 0000000..1e8478a
--- /dev/null
+++ b/fireredtts/modules/bigvgan/activations.py
@@ -0,0 +1,126 @@
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+
+
+import torch
+from torch import nn, sin, pow
+from torch.nn import Parameter
+
+
+class Snake(nn.Module):
+ """
+ Implementation of a sine-based periodic activation function
+ Shape:
+ - Input: (B, C, T)
+ - Output: (B, C, T), same shape as the input
+ Parameters:
+ - alpha - trainable parameter
+ References:
+ - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+ https://arxiv.org/abs/2006.08195
+ Examples:
+ >>> a1 = snake(256)
+ >>> x = torch.randn(256)
+ >>> x = a1(x)
+ """
+
+ def __init__(
+ self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
+ ):
+ """
+ Initialization.
+ INPUT:
+ - in_features: shape of the input
+ - alpha: trainable parameter
+ alpha is initialized to 1 by default, higher values = higher-frequency.
+ alpha will be trained along with the rest of your model.
+ """
+ super(Snake, self).__init__()
+ self.in_features = in_features
+
+ # initialize alpha
+ self.alpha_logscale = alpha_logscale
+ if self.alpha_logscale: # log scale alphas initialized to zeros
+ self.alpha = Parameter(torch.zeros(in_features) * alpha)
+ else: # linear scale alphas initialized to ones
+ self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+ self.alpha.requires_grad = alpha_trainable
+
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, x):
+ """
+ Forward pass of the function.
+ Applies the function to the input elementwise.
+ Snake ∶= x + 1/a * sin^2 (xa)
+ """
+ alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+ if self.alpha_logscale:
+ alpha = torch.exp(alpha)
+ x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+ return x
+
+
+class SnakeBeta(nn.Module):
+ """
+ A modified Snake function which uses separate parameters for the magnitude of the periodic components
+ Shape:
+ - Input: (B, C, T)
+ - Output: (B, C, T), same shape as the input
+ Parameters:
+ - alpha - trainable parameter that controls frequency
+ - beta - trainable parameter that controls magnitude
+ References:
+ - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+ https://arxiv.org/abs/2006.08195
+ Examples:
+ >>> a1 = snakebeta(256)
+ >>> x = torch.randn(256)
+ >>> x = a1(x)
+ """
+
+ def __init__(
+ self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
+ ):
+ """
+ Initialization.
+ INPUT:
+ - in_features: shape of the input
+ - alpha - trainable parameter that controls frequency
+ - beta - trainable parameter that controls magnitude
+ alpha is initialized to 1 by default, higher values = higher-frequency.
+ beta is initialized to 1 by default, higher values = higher-magnitude.
+ alpha will be trained along with the rest of your model.
+ """
+ super(SnakeBeta, self).__init__()
+ self.in_features = in_features
+
+ # initialize alpha
+ self.alpha_logscale = alpha_logscale
+ if self.alpha_logscale: # log scale alphas initialized to zeros
+ self.alpha = Parameter(torch.zeros(in_features) * alpha)
+ self.beta = Parameter(torch.zeros(in_features) * alpha)
+ else: # linear scale alphas initialized to ones
+ self.alpha = Parameter(torch.ones(in_features) * alpha)
+ self.beta = Parameter(torch.ones(in_features) * alpha)
+
+ self.alpha.requires_grad = alpha_trainable
+ self.beta.requires_grad = alpha_trainable
+
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, x):
+ """
+ Forward pass of the function.
+ Applies the function to the input elementwise.
+ SnakeBeta ∶= x + 1/b * sin^2 (xa)
+ """
+ alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+ beta = self.beta.unsqueeze(0).unsqueeze(-1)
+ if self.alpha_logscale:
+ alpha = torch.exp(alpha)
+ beta = torch.exp(beta)
+ x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+ return x
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/__init__.py b/fireredtts/modules/bigvgan/alias_free_cuda/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/activation1d.py b/fireredtts/modules/bigvgan/alias_free_cuda/activation1d.py
new file mode 100644
index 0000000..9c7c540
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/activation1d.py
@@ -0,0 +1,75 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+# Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+from token2wav.alias_free_torch.resample import UpSample1d, DownSample1d
+
+# load fused CUDA kernel: this enables importing anti_alias_activation_cuda
+from token2wav.alias_free_cuda import load
+
+load.load()
+
+
+class FusedAntiAliasActivation(torch.autograd.Function):
+ """
+ Assumes filter size 12, replication padding on upsampling, and logscale alpha/beta parameters as inputs
+ """
+
+ @staticmethod
+ def forward(ctx, inputs, ftr, alpha, beta):
+ import anti_alias_activation_cuda
+
+ activation_results = anti_alias_activation_cuda.forward(
+ inputs, ftr, alpha, beta
+ )
+ return activation_results
+
+ @staticmethod
+ def backward(ctx, output_grads):
+ # TODO: implement bwd pass
+ raise NotImplementedError
+ return output_grads, None, None
+
+
+class Activation1d(nn.Module):
+ def __init__(
+ self,
+ activation,
+ up_ratio: int = 2,
+ down_ratio: int = 2,
+ up_kernel_size: int = 12,
+ down_kernel_size: int = 12,
+ fused: bool = True,
+ ):
+ super().__init__()
+ self.up_ratio = up_ratio
+ self.down_ratio = down_ratio
+ self.act = activation
+ self.upsample = UpSample1d(up_ratio, up_kernel_size)
+ self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+ self.fused = fused # whether to use fused CUDA kernel or not
+
+ def forward(self, x):
+ if not self.fused:
+ x = self.upsample(x)
+ x = self.act(x)
+ x = self.downsample(x)
+ return x
+ else:
+ if self.act.__class__.__name__ == "Snake":
+ beta = self.act.alpha.data # snake uses same params for alpha and beta
+ else:
+ beta = (
+ self.act.beta.data
+ ) # snakebeta uses different params for alpha and beta
+ alpha = self.act.alpha.data
+ if (
+ not self.act.alpha_logscale
+ ): # exp baked into cuda kernel, cancel it out with a log
+ alpha = torch.log(alpha)
+ beta = torch.log(beta)
+ x = FusedAntiAliasActivation.apply(x, self.upsample.filter, alpha, beta)
+ x = self.downsample(x)
+ return x
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/anti_alias_activation.cpp b/fireredtts/modules/bigvgan/alias_free_cuda/anti_alias_activation.cpp
new file mode 100644
index 0000000..c68556e
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/anti_alias_activation.cpp
@@ -0,0 +1,48 @@
+/* coding=utf-8
+ * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+ *
+ * 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.
+ */
+
+#include
+#include
+#include
+
+namespace anti_alias_activation {
+
+torch::Tensor fwd_cuda(torch::Tensor const& input,
+ torch::Tensor const& filter,
+ torch::Tensor const& alpha,
+ torch::Tensor const& beta
+ );
+
+torch::Tensor fwd(torch::Tensor const& input,
+ torch::Tensor const& filter,
+ torch::Tensor const& alpha,
+ torch::Tensor const& beta
+ ) {
+ AT_ASSERTM(input.dim() == 3, "expected 3D tensor");
+ //AT_ASSERTM((input.scalar_type() == at::ScalarType::Half) ||
+ // (input.scalar_type() == at::ScalarType::BFloat16),
+ // "Only fp16 and bf16 are supported");
+
+ return fwd_cuda(input, filter, alpha, beta);
+}
+
+} // end namespace anti_alias_activation
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("forward",
+ &anti_alias_activation::fwd,
+ "Anti Alias Activation -- Forward.");
+}
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/anti_alias_activation_cuda.cu b/fireredtts/modules/bigvgan/alias_free_cuda/anti_alias_activation_cuda.cu
new file mode 100644
index 0000000..143ceea
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/anti_alias_activation_cuda.cu
@@ -0,0 +1,314 @@
+/* coding=utf-8
+ * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+ *
+ * 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.
+ */
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include "type_shim.h"
+#include
+#include
+#include
+#include
+#include
+
+namespace {
+
+ /*
+template
+__device__ __inline__ void copy_vector(Datatype *dst, const Datatype *src);
+
+template <>
+__device__ __inline__ void copy_vector(c10::BFloat16 *dst, const c10::BFloat16 *src) { *dst = *src; }
+
+template <>
+__device__ __inline__ void copy_vector(c10::BFloat16 *dst, const c10::BFloat16 *src) { *((float2*) dst) = *((float2*) src); }
+
+template <>
+__device__ __inline__ void copy_vector(c10::Half *dst, const c10::Half *src) { *dst = *src; }
+
+template <>
+__device__ __inline__ void copy_vector(c10::Half *dst, const c10::Half *src) { *((float2*) dst) = *((float2*) src); }
+
+template <>
+__device__ __inline__ void copy_vector(uint8_t *dst, const uint8_t *src) { *dst = *src; }
+
+template <>
+__device__ __inline__ void copy_vector(uint8_t *dst, const uint8_t *src) {*((half2*) dst) = *((half2*) src); }
+
+int log2_ceil(int value) {
+ int log2_value = 0;
+ while ((1 << log2_value) < value) ++log2_value;
+ return log2_value;
+}
+
+template
+struct Add {
+ __device__ __forceinline__ T operator()(T a, T b) const {
+ return a + b;
+ }
+};
+
+template
+struct Max {
+ __device__ __forceinline__ T operator()(T a, T b) const {
+ return a < b ? b : a;
+ }
+};
+
+template
+__device__ __forceinline__ T WARP_SHFL_XOR_NATIVE(T value, int laneMask, int width = warpSize, unsigned int mask = 0xffffffff)
+{
+#if CUDA_VERSION >= 9000
+ return __shfl_xor_sync(mask, value, laneMask, width);
+#else
+ return __shfl_xor(value, laneMask, width);
+#endif
+}
+
+template class ReduceOp>
+__device__ __forceinline__ void warp_reduce(acc_t* sum) {
+ ReduceOp r;
+ #pragma unroll
+ for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
+ #pragma unroll
+ for (int i = 0; i < WARP_BATCH; ++i) {
+ acc_t b = WARP_SHFL_XOR_NATIVE(sum[i], offset, WARP_SIZE);
+ sum[i] = r(sum[i], b);
+ }
+ }
+}
+*/
+
+template
+__global__ void anti_alias_activation_forward(
+ output_t *dst,
+ const input_t *src,
+ const input_t *ftr,
+ const input_t *alpha,
+ const input_t *beta,
+ int batch_size,
+ int channels,
+ int seq_len)
+{
+ // WARP_SIZE and WARP_BATCH must match the return values batches_per_warp and
+ constexpr int ELEMENTS_PER_LDG_STG = 1; //(WARP_ITERATIONS < 4) ? 1 : 4;
+ constexpr int BUFFER_SIZE = 32;
+ constexpr int FILTER_SIZE = 12;
+ constexpr int HALF_FILTER_SIZE = 6;
+ constexpr int REPLICATION_PAD = 5; // 5 on each side
+
+ // blockDim/threadIdx = (128, 1, 1)
+ // gridDim/blockIdx = (seq_blocks, channels, batches)
+ int block_offset = (blockIdx.x * 128 * BUFFER_SIZE + seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+ int local_offset = threadIdx.x * BUFFER_SIZE;
+ int seq_offset = blockIdx.x * 128 * BUFFER_SIZE + local_offset;
+
+
+ //int intermediate_seq_len = seq_len * 2 - 1 + 4 * REPLICATION_PAD;
+ //int intermediate_block_offset = (blockIdx.x * 128 * BUFFER_SIZE * 2 + intermediate_seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+ //int intermediate_local_offset = threadIdx.x * BUFFER_SIZE * 2;
+
+ int output_seq_len = seq_len * 2 ; //
+ int output_block_offset = (blockIdx.x * 128 * BUFFER_SIZE * 2 + output_seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+ int output_local_offset = threadIdx.x * BUFFER_SIZE * 2;
+ int output_seq_offset = blockIdx.x * 128 * BUFFER_SIZE *2 + output_local_offset;
+ // get values needed for replication padding before moving pointer
+ const input_t *right_most_pntr = src + (seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+ input_t seq_left_most_value = right_most_pntr[0];
+ input_t seq_right_most_value = right_most_pntr[seq_len - 1];
+
+ src += block_offset + local_offset;
+ dst += output_block_offset + output_local_offset ;
+ alpha = alpha + blockIdx.y;
+ input_t alpha_val = expf(alpha[0]);
+ beta = beta + blockIdx.y;
+ input_t beta_val = expf(beta[0]);
+ // load data from global memory
+ input_t elements[2*FILTER_SIZE+2*BUFFER_SIZE] = {0};
+ input_t intermediates[2*FILTER_SIZE+2*BUFFER_SIZE] = {0};
+ //output_t output[2*BUFFER_SIZE];
+ input_t filter[FILTER_SIZE];
+ //input_t temp_data[ELEMENTS_PER_LDG_STG];
+ //uint8_t temp_mask[ELEMENTS_PER_LDG_STG];
+
+ #pragma unroll
+ for (int it = 0; it < FILTER_SIZE; it+=1) {
+ filter[it] = ftr[it];
+ }
+
+
+ #pragma unroll
+ for (int it = -HALF_FILTER_SIZE; it < BUFFER_SIZE + HALF_FILTER_SIZE ; it+=1) {
+ int element_index = seq_offset + it;
+ if ((element_index < 0) && (element_index >= -REPLICATION_PAD)) {
+ elements[2*(HALF_FILTER_SIZE+it)] = 2*seq_left_most_value;
+ }
+ if ((element_index >= seq_len) && (element_index < seq_len + REPLICATION_PAD)) {
+ elements[2*(HALF_FILTER_SIZE+it)] = 2*seq_right_most_value;
+ }
+ if ((element_index >= 0) && (element_index < seq_len)) {
+ elements[2*(HALF_FILTER_SIZE+it)] = 2*src[it];
+ }
+ }
+
+
+
+ // apply filter
+ #pragma unroll
+ for (int it = 0; it < (2 * BUFFER_SIZE + 2*FILTER_SIZE); it+=1) {
+ input_t acc = 0.0;
+
+ int element_index = output_seq_offset + it; // index for output
+ #pragma unroll
+ for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx+=1){
+ if ((element_index + f_idx) >= 0){
+ acc += filter[f_idx] * elements[it+f_idx];
+ }
+ }
+ intermediates[it] = acc;
+ }
+
+ double no_div_by_zero = 0.000000001;
+ #pragma unroll
+ for (int it = 0; it < 12 + 2 * BUFFER_SIZE; it++) {
+ intermediates[it] += (1.0/(beta_val + no_div_by_zero)) * sinf(intermediates[it] * alpha_val) * sinf(intermediates[it] * alpha_val);
+ }
+
+
+ // now copy to output
+ #pragma unroll
+ for (int it = 0; it < 2*BUFFER_SIZE; it+=1){
+ int element_index = output_seq_offset + it;
+ if (element_index < output_seq_len) {
+ dst[it] = intermediates[it+6];
+ }
+ }
+
+
+
+ // for (int it = 0; it < BUFFER_SIZE; it+=ELEMENTS_PER_LDG_STG) {
+ // int element_index = seq_offset + it;
+ // if (element_index < seq_len) {
+ // dst[it] = output[it];
+ // }
+ // }
+
+
+ // // Upsample convolution
+ // for (int it = 0; it < 2 * BUFFER_SIZE + 12; it+=1) {
+ // input_t acc = 0.0;
+
+ // for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx+=1){
+ // acc += filter[f_idx] * elements[it+f_idx];
+ // }
+ // intermediates[it] = acc;
+ // }
+
+ // // correct the corners of intermediates
+ // if (seq_offset == 0) {
+ // for (int it = 0; it < 6; it+=1)
+ // intermediates[it] = 0;
+ // }
+
+ // if (seq_offset + 32 >= seq_len) {
+ // int offset = seq_len % 32 == 0 ? 32 : seq_len % 32;
+
+ // for (int it = 0; it < 6; it++) {
+ // intermediates[6+2*offset+it] = 0;
+ // }
+ // }
+
+
+
+
+ // for (int it = 0; it < BUFFER_SIZE; it+=ELEMENTS_PER_LDG_STG) {
+ // int element_index = seq_offset + it;
+ // if (element_index < seq_len) {
+ // dst[it] = output[it];
+ // }
+ // }
+}
+
+template
+void dispatch_anti_alias_activation_forward(
+ output_t *dst,
+ const input_t *src,
+ const input_t *ftr,
+ const input_t *alpha,
+ const input_t *beta,
+ int batch_size,
+ int channels,
+ int seq_len)
+{
+ if (seq_len == 0) {
+ return;
+ } else {
+ // use 128 threads per block to maximimize gpu utilization
+ constexpr int threads_per_block = 128;
+ constexpr int seq_len_per_block = 4096;
+ int blocks_per_seq_len = (seq_len + seq_len_per_block - 1) / seq_len_per_block;
+ dim3 blocks(blocks_per_seq_len, channels, batch_size);
+ dim3 threads(threads_per_block, 1, 1);
+
+ anti_alias_activation_forward
+ <<>>(dst, src, ftr, alpha, beta, batch_size, channels, seq_len);
+ }
+}
+}
+
+namespace anti_alias_activation {
+
+ torch::Tensor fwd_cuda(torch::Tensor const& input, torch::Tensor const& filter, torch::Tensor const& alpha, torch::Tensor const& beta)
+{
+ // input is a 4d tensor with dimensions [batches, attn_heads, seq_len, seq_len]
+ const int batches = input.size(0);
+ const int channels = input.size(1);
+ const int seq_len = input.size(2);
+
+ // Output
+ auto act_options = input.options().requires_grad(false);
+ int output_seq_len = seq_len*2; // we'll be dilating between each element by interspersing with zeros
+
+ torch::Tensor anti_alias_activation_results =
+ torch::empty({batches, channels, output_seq_len}, act_options);
+
+ // Softmax Intermediate Result Ptr
+ void* input_ptr = static_cast(input.data_ptr());
+ void* filter_ptr = static_cast(filter.data_ptr());
+ void* alpha_ptr = static_cast(alpha.data_ptr());
+ void* beta_ptr = static_cast(beta.data_ptr());
+ void* anti_alias_activation_results_ptr = static_cast(anti_alias_activation_results.data_ptr());
+
+ DISPATCH_FLOAT_HALF_AND_BFLOAT(
+ input.scalar_type(),
+ "dispatch anti alias activation_forward",
+ dispatch_anti_alias_activation_forward(
+ reinterpret_cast(anti_alias_activation_results_ptr),
+ reinterpret_cast(input_ptr),
+ reinterpret_cast(filter_ptr),
+ reinterpret_cast(alpha_ptr),
+ reinterpret_cast(beta_ptr),
+ batches,
+ channels,
+ seq_len);
+ );
+ return anti_alias_activation_results;
+}
+}
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/compat.h b/fireredtts/modules/bigvgan/alias_free_cuda/compat.h
new file mode 100644
index 0000000..92e7eb7
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/compat.h
@@ -0,0 +1,31 @@
+/* coding=utf-8
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * 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.
+ */
+
+/*This code is copied fron NVIDIA apex:
+ * https://github.com/NVIDIA/apex
+ * with minor changes. */
+
+
+
+#ifndef TORCH_CHECK
+#define TORCH_CHECK AT_CHECK
+#endif
+
+#ifdef VERSION_GE_1_3
+#define DATA_PTR data_ptr
+#else
+#define DATA_PTR data
+#endif
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/load.py b/fireredtts/modules/bigvgan/alias_free_cuda/load.py
new file mode 100644
index 0000000..63a1248
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/load.py
@@ -0,0 +1,85 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+# Licensed under the MIT license.
+
+import os
+import pathlib
+import subprocess
+
+from torch.utils import cpp_extension
+
+# Setting this param to a list has a problem of generating different
+# compilation commands (with diferent order of architectures) and
+# leading to recompilation of fused kernels. Set it to empty string
+# to avoid recompilation and assign arch flags explicity in
+# extra_cuda_cflags below
+os.environ["TORCH_CUDA_ARCH_LIST"] = ""
+
+
+def load():
+ # Check if cuda 11 is installed for compute capability 8.0
+ cc_flag = []
+ _, bare_metal_major, _ = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
+ if int(bare_metal_major) >= 11:
+ cc_flag.append("-gencode")
+ cc_flag.append("arch=compute_80,code=sm_80")
+
+ # Build path
+ srcpath = pathlib.Path(__file__).parent.absolute()
+ buildpath = srcpath / "build"
+ _create_build_dir(buildpath)
+
+ # Helper function to build the kernels.
+ def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
+ return cpp_extension.load(
+ name=name,
+ sources=sources,
+ build_directory=buildpath,
+ extra_cflags=[
+ "-O3",
+ ],
+ extra_cuda_cflags=[
+ "-O3",
+ "-gencode",
+ "arch=compute_70,code=sm_70",
+ "--use_fast_math",
+ ]
+ + extra_cuda_flags
+ + cc_flag,
+ verbose=True,
+ )
+
+ extra_cuda_flags = [
+ "-U__CUDA_NO_HALF_OPERATORS__",
+ "-U__CUDA_NO_HALF_CONVERSIONS__",
+ "--expt-relaxed-constexpr",
+ "--expt-extended-lambda",
+ ]
+
+ sources = [
+ srcpath / "anti_alias_activation.cpp",
+ srcpath / "anti_alias_activation_cuda.cu",
+ ]
+ anti_alias_activation_cuda = _cpp_extention_load_helper(
+ "anti_alias_activation_cuda", sources, extra_cuda_flags
+ )
+
+
+def _get_cuda_bare_metal_version(cuda_dir):
+ raw_output = subprocess.check_output(
+ [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
+ )
+ output = raw_output.split()
+ release_idx = output.index("release") + 1
+ release = output[release_idx].split(".")
+ bare_metal_major = release[0]
+ bare_metal_minor = release[1][0]
+
+ return raw_output, bare_metal_major, bare_metal_minor
+
+
+def _create_build_dir(buildpath):
+ try:
+ os.mkdir(buildpath)
+ except OSError:
+ if not os.path.isdir(buildpath):
+ print(f"Creation of the build directory {buildpath} failed")
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/test_activation.py b/fireredtts/modules/bigvgan/alias_free_cuda/test_activation.py
new file mode 100644
index 0000000..eef8760
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/test_activation.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+# Licensed under the MIT license.
+
+import math
+import torch
+import alias_free_cuda
+from alias_free_cuda import activation1d
+from activations import Snake, SnakeBeta
+
+
+def test_load_fused_kernels():
+ try:
+ import alias_free_cuda
+ import torch
+
+ print("[Success] load_fused_kernels")
+ except ImportError as e:
+ print("[Fail] load_fused_kernels")
+ raise e
+
+
+def test_anti_alias_activation():
+ data = torch.rand((10, 10, 50000), device="cuda")
+
+ # check activations.Snake cuda vs. torch
+ fused_anti_alias_activation = activation1d.Activation1d(
+ activation=Snake(10), fused=True
+ ).cuda()
+ fused_activation_output = fused_anti_alias_activation(data)
+
+ torch_anti_alias_activation = activation1d.Activation1d(
+ activation=Snake(10), fused=False
+ ).cuda()
+ torch_activation_output = torch_anti_alias_activation(data)
+
+ test_result = (fused_activation_output - torch_activation_output).abs()
+
+ while test_result.dim() != 1:
+ test_result = test_result.mean(dim=-1)
+
+ diff = test_result.mean(dim=-1)
+
+ if diff <= 1e-3:
+ print(
+ f"\n[Success] test_fused_anti_alias_activation"
+ f"\n > mean_difference={diff}"
+ f"\n > fused_values={fused_activation_output[-1][-1][-100:].tolist()}"
+ f"\n > torch_values={torch_activation_output[-1][-1][-100:].tolist()}"
+ )
+ else:
+ print(
+ f"\n[Fail] test_fused_anti_alias_activation"
+ f"\n > mean_difference={diff}, "
+ f"\n > fused_values={fused_activation_output[-1][-1][-30:].tolist()}, "
+ f"\n > torch_values={torch_activation_output[-1][-1][-30:].tolist()}"
+ )
+
+
+if __name__ == "__main__":
+ from alias_free_cuda import load
+
+ load.load()
+ test_load_fused_kernels()
+ test_anti_alias_activation()
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/test_activation_snake_beta.py b/fireredtts/modules/bigvgan/alias_free_cuda/test_activation_snake_beta.py
new file mode 100644
index 0000000..62153c4
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/test_activation_snake_beta.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+# Licensed under the MIT license.
+
+import math
+import torch
+import alias_free_cuda
+from alias_free_cuda import activation1d
+from activations import Snake, SnakeBeta
+
+
+def test_load_fused_kernels():
+ try:
+ import alias_free_cuda
+ import torch
+
+ print("[Success] load_fused_kernels")
+ except ImportError as e:
+ print("[Fail] load_fused_kernels")
+ raise e
+
+
+def test_anti_alias_activation():
+ data = torch.rand((10, 10, 50000), device="cuda")
+
+ # check activations.Snake cuda vs. torch
+ fused_anti_alias_activation = activation1d.Activation1d(
+ activation=SnakeBeta(10), fused=True
+ ).cuda()
+ fused_activation_output = fused_anti_alias_activation(data)
+
+ torch_anti_alias_activation = activation1d.Activation1d(
+ activation=SnakeBeta(10), fused=False
+ ).cuda()
+ torch_activation_output = torch_anti_alias_activation(data)
+
+ test_result = (fused_activation_output - torch_activation_output).abs()
+
+ while test_result.dim() != 1:
+ test_result = test_result.mean(dim=-1)
+
+ diff = test_result.mean(dim=-1)
+
+ if diff <= 1e-3:
+ print(
+ f"\n[Success] test_fused_anti_alias_activation"
+ f"\n > mean_difference={diff}"
+ f"\n > fused_values={fused_activation_output[-1][-1][-100:].tolist()}"
+ f"\n > torch_values={torch_activation_output[-1][-1][-100:].tolist()}"
+ )
+ else:
+ print(
+ f"\n[Fail] test_fused_anti_alias_activation"
+ f"\n > mean_difference={diff}, "
+ f"\n > fused_values={fused_activation_output[-1][-1][-30:].tolist()}, "
+ f"\n > torch_values={torch_activation_output[-1][-1][-30:].tolist()}"
+ )
+
+
+if __name__ == "__main__":
+ from alias_free_cuda import load
+
+ load.load()
+ test_load_fused_kernels()
+ test_anti_alias_activation()
diff --git a/fireredtts/modules/bigvgan/alias_free_cuda/type_shim.h b/fireredtts/modules/bigvgan/alias_free_cuda/type_shim.h
new file mode 100644
index 0000000..d30a7e4
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_cuda/type_shim.h
@@ -0,0 +1,97 @@
+/* coding=utf-8
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * 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.
+ */
+
+
+#include
+#include "compat.h"
+
+
+#define DISPATCH_FLOAT_HALF_AND_BFLOAT(TYPE, NAME, ...) \
+ switch(TYPE) \
+ { \
+ case at::ScalarType::Float: \
+ { \
+ using scalar_t = float; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ case at::ScalarType::Half: \
+ { \
+ using scalar_t = at::Half; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ case at::ScalarType::BFloat16: \
+ { \
+ using scalar_t = at::BFloat16; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ default: \
+ AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \
+ }
+
+
+
+#define DISPATCH_FLOAT_HALF_AND_BFLOAT_INOUT_TYPES(TYPEIN, TYPEOUT, NAME, ...) \
+ switch(TYPEIN) \
+ { \
+ case at::ScalarType::Float: \
+ { \
+ using scalar_t_in = float; \
+ switch(TYPEOUT) \
+ { \
+ case at::ScalarType::Float: \
+ { \
+ using scalar_t_out = float; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ case at::ScalarType::Half: \
+ { \
+ using scalar_t_out = at::Half; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ case at::ScalarType::BFloat16: \
+ { \
+ using scalar_t_out = at::BFloat16; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ default: \
+ AT_ERROR(#NAME, " not implemented for '", toString(TYPEOUT), "'"); \
+ } \
+ break; \
+ } \
+ case at::ScalarType::Half: \
+ { \
+ using scalar_t_in = at::Half; \
+ using scalar_t_out = at::Half; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ case at::ScalarType::BFloat16: \
+ { \
+ using scalar_t_in = at::BFloat16; \
+ using scalar_t_out = at::BFloat16; \
+ __VA_ARGS__; \
+ break; \
+ } \
+ default: \
+ AT_ERROR(#NAME, " not implemented for '", toString(TYPEIN), "'"); \
+ }
+
diff --git a/fireredtts/modules/bigvgan/alias_free_torch/__init__.py b/fireredtts/modules/bigvgan/alias_free_torch/__init__.py
new file mode 100644
index 0000000..b3bccdb
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_torch/__init__.py
@@ -0,0 +1,5 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+from .filter import *
+from .resample import *
+from .act import *
diff --git a/fireredtts/modules/bigvgan/alias_free_torch/act.py b/fireredtts/modules/bigvgan/alias_free_torch/act.py
new file mode 100644
index 0000000..779d58d
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_torch/act.py
@@ -0,0 +1,29 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+import torch.nn as nn
+from .resample import UpSample1d, DownSample1d
+
+
+class Activation1d(nn.Module):
+ def __init__(
+ self,
+ activation,
+ up_ratio: int = 2,
+ down_ratio: int = 2,
+ up_kernel_size: int = 12,
+ down_kernel_size: int = 12,
+ ):
+ super().__init__()
+ self.up_ratio = up_ratio
+ self.down_ratio = down_ratio
+ self.act = activation
+ self.upsample = UpSample1d(up_ratio, up_kernel_size)
+ self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+ # x: [B,C,T]
+ def forward(self, x):
+ x = self.upsample(x)
+ x = self.act(x)
+ x = self.downsample(x)
+
+ return x
diff --git a/fireredtts/modules/bigvgan/alias_free_torch/filter.py b/fireredtts/modules/bigvgan/alias_free_torch/filter.py
new file mode 100644
index 0000000..394850e
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_torch/filter.py
@@ -0,0 +1,98 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+if "sinc" in dir(torch):
+ sinc = torch.sinc
+else:
+ # This code is adopted from adefossez's julius.core.sinc under the MIT License
+ # https://adefossez.github.io/julius/julius/core.html
+ # LICENSE is in incl_licenses directory.
+ def sinc(x: torch.Tensor):
+ """
+ Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+ __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+ """
+ return torch.where(
+ x == 0,
+ torch.tensor(1.0, device=x.device, dtype=x.dtype),
+ torch.sin(math.pi * x) / math.pi / x,
+ )
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+# LICENSE is in incl_licenses directory.
+def kaiser_sinc_filter1d(
+ cutoff, half_width, kernel_size
+): # return filter [1,1,kernel_size]
+ even = kernel_size % 2 == 0
+ half_size = kernel_size // 2
+
+ # For kaiser window
+ delta_f = 4 * half_width
+ A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+ if A > 50.0:
+ beta = 0.1102 * (A - 8.7)
+ elif A >= 21.0:
+ beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+ else:
+ beta = 0.0
+ window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+ # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+ if even:
+ time = torch.arange(-half_size, half_size) + 0.5
+ else:
+ time = torch.arange(kernel_size) - half_size
+ if cutoff == 0:
+ filter_ = torch.zeros_like(time)
+ else:
+ filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+ # Normalize filter to have sum = 1, otherwise we will have a small leakage
+ # of the constant component in the input signal.
+ filter_ /= filter_.sum()
+ filter = filter_.view(1, 1, kernel_size)
+
+ return filter
+
+
+class LowPassFilter1d(nn.Module):
+ def __init__(
+ self,
+ cutoff=0.5,
+ half_width=0.6,
+ stride: int = 1,
+ padding: bool = True,
+ padding_mode: str = "replicate",
+ kernel_size: int = 12,
+ ):
+ # kernel_size should be even number for stylegan3 setup,
+ # in this implementation, odd number is also possible.
+ super().__init__()
+ if cutoff < -0.0:
+ raise ValueError("Minimum cutoff must be larger than zero.")
+ if cutoff > 0.5:
+ raise ValueError("A cutoff above 0.5 does not make sense.")
+ self.kernel_size = kernel_size
+ self.even = kernel_size % 2 == 0
+ self.pad_left = kernel_size // 2 - int(self.even)
+ self.pad_right = kernel_size // 2
+ self.stride = stride
+ self.padding = padding
+ self.padding_mode = padding_mode
+ filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+ self.register_buffer("filter", filter)
+
+ # input [B, C, T]
+ def forward(self, x):
+ _, C, _ = x.shape
+
+ if self.padding:
+ x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+ out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+ return out
diff --git a/fireredtts/modules/bigvgan/alias_free_torch/resample.py b/fireredtts/modules/bigvgan/alias_free_torch/resample.py
new file mode 100644
index 0000000..ee993b1
--- /dev/null
+++ b/fireredtts/modules/bigvgan/alias_free_torch/resample.py
@@ -0,0 +1,57 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+import torch.nn as nn
+from torch.nn import functional as F
+from .filter import LowPassFilter1d
+from .filter import kaiser_sinc_filter1d
+
+
+class UpSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ self.ratio = ratio
+ self.kernel_size = (
+ int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ )
+ self.stride = ratio
+ self.pad = self.kernel_size // ratio - 1
+ self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+ self.pad_right = (
+ self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+ )
+ filter = kaiser_sinc_filter1d(
+ cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
+ )
+ self.register_buffer("filter", filter)
+
+ # x: [B, C, T]
+ def forward(self, x):
+ _, C, _ = x.shape
+
+ x = F.pad(x, (self.pad, self.pad), mode="replicate")
+ x = self.ratio * F.conv_transpose1d(
+ x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
+ )
+ x = x[..., self.pad_left : -self.pad_right]
+
+ return x
+
+
+class DownSample1d(nn.Module):
+ def __init__(self, ratio=2, kernel_size=None):
+ super().__init__()
+ self.ratio = ratio
+ self.kernel_size = (
+ int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ )
+ self.lowpass = LowPassFilter1d(
+ cutoff=0.5 / ratio,
+ half_width=0.6 / ratio,
+ stride=ratio,
+ kernel_size=self.kernel_size,
+ )
+
+ def forward(self, x):
+ xx = self.lowpass(x)
+
+ return xx
diff --git a/fireredtts/modules/bigvgan/bigvgan.py b/fireredtts/modules/bigvgan/bigvgan.py
new file mode 100644
index 0000000..3fb876d
--- /dev/null
+++ b/fireredtts/modules/bigvgan/bigvgan.py
@@ -0,0 +1,399 @@
+import typing as tp
+import torch
+import torch.nn as nn
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from fireredtts.modules.bigvgan.alias_free_torch import (
+ Activation1d as TorchActivation1d,
+)
+from fireredtts.modules.bigvgan.activations import Snake, SnakeBeta
+
+
+def init_weights(m, mean=0.0, std=0.01):
+ classname = m.__class__.__name__
+ if classname.find("Conv") != -1:
+ m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+ return int((kernel_size * dilation - dilation) / 2)
+
+
+class AMPBlock1(torch.nn.Module):
+ def __init__(
+ self,
+ channels,
+ kernel_size=3,
+ dilation=(1, 3, 5),
+ activation=None,
+ snake_logscale=True,
+ use_cuda_kernel=False,
+ ):
+ super(AMPBlock1, self).__init__()
+
+ self.convs1 = nn.ModuleList(
+ [
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[0],
+ padding=get_padding(kernel_size, dilation[0]),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[1],
+ padding=get_padding(kernel_size, dilation[1]),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[2],
+ padding=get_padding(kernel_size, dilation[2]),
+ )
+ ),
+ ]
+ )
+ self.convs1.apply(init_weights)
+
+ self.convs2 = nn.ModuleList(
+ [
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1),
+ )
+ ),
+ ]
+ )
+ self.convs2.apply(init_weights)
+
+ self.num_layers = len(self.convs1) + len(
+ self.convs2
+ ) # total number of conv layers
+
+ # select which Activation1d, lazy-load cuda version to ensure backward compatibility
+ if use_cuda_kernel:
+ from modules.bigvgan.alias_free_cuda.activation1d import (
+ Activation1d as CudaActivation1d,
+ )
+
+ Activation1d = CudaActivation1d
+ else:
+ Activation1d = TorchActivation1d
+
+ if (
+ activation == "snake"
+ ): # periodic nonlinearity with snake function and anti-aliasing
+ self.activations = nn.ModuleList(
+ [
+ Activation1d(
+ activation=Snake(channels, alpha_logscale=snake_logscale)
+ )
+ for _ in range(self.num_layers)
+ ]
+ )
+ elif (
+ activation == "snakebeta"
+ ): # periodic nonlinearity with snakebeta function and anti-aliasing
+ self.activations = nn.ModuleList(
+ [
+ Activation1d(
+ activation=SnakeBeta(channels, alpha_logscale=snake_logscale)
+ )
+ for _ in range(self.num_layers)
+ ]
+ )
+ else:
+ raise NotImplementedError(
+ "activation incorrectly specified. check the config file and look for 'activation'."
+ )
+
+ def forward(self, x):
+ acts1, acts2 = self.activations[::2], self.activations[1::2]
+ for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
+ xt = a1(x)
+ xt = c1(xt)
+ xt = a2(xt)
+ xt = c2(xt)
+ x = xt + x
+
+ return x
+
+ def remove_weight_norm(self):
+ for l in self.convs1:
+ remove_weight_norm(l)
+ for l in self.convs2:
+ remove_weight_norm(l)
+
+
+class AMPBlock2(torch.nn.Module):
+ def __init__(
+ self,
+ channels,
+ kernel_size=3,
+ dilation=(1, 3),
+ activation=None,
+ snake_logscale=True,
+ use_cuda_kernel=False,
+ ):
+ super(AMPBlock2, self).__init__()
+
+ self.convs = nn.ModuleList(
+ [
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[0],
+ padding=get_padding(kernel_size, dilation[0]),
+ )
+ ),
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation[1],
+ padding=get_padding(kernel_size, dilation[1]),
+ )
+ ),
+ ]
+ )
+ self.convs.apply(init_weights)
+
+ self.num_layers = len(self.convs) # total number of conv layers
+
+ # select which Activation1d, lazy-load cuda version to ensure backward compatibility
+ if use_cuda_kernel:
+ from modules.bigvgan.alias_free_cuda.activation1d import (
+ Activation1d as CudaActivation1d,
+ )
+
+ Activation1d = CudaActivation1d
+ else:
+ Activation1d = TorchActivation1d
+
+ if (
+ activation == "snake"
+ ): # periodic nonlinearity with snake function and anti-aliasing
+ self.activations = nn.ModuleList(
+ [
+ Activation1d(
+ activation=Snake(channels, alpha_logscale=snake_logscale)
+ )
+ for _ in range(self.num_layers)
+ ]
+ )
+ elif (
+ activation == "snakebeta"
+ ): # periodic nonlinearity with snakebeta function and anti-aliasing
+ self.activations = nn.ModuleList(
+ [
+ Activation1d(
+ activation=SnakeBeta(channels, alpha_logscale=snake_logscale)
+ )
+ for _ in range(self.num_layers)
+ ]
+ )
+ else:
+ raise NotImplementedError(
+ "activation incorrectly specified. check the config file and look for 'activation'."
+ )
+
+ def forward(self, x):
+ for c, a in zip(self.convs, self.activations):
+ xt = a(x)
+ xt = c(xt)
+ x = xt + x
+
+ return x
+
+ def remove_weight_norm(self):
+ for l in self.convs:
+ remove_weight_norm(l)
+
+
+class BigVGAN(torch.nn.Module):
+ # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
+ def __init__(
+ self,
+ num_mels: int,
+ upsample_initial_channel: int,
+ resblock_kernel_sizes: tp.List[int],
+ resblock_dilation_sizes: tp.List[tp.List[int]],
+ upsample_rates: tp.List[int],
+ upsample_kernel_sizes: tp.List[int],
+ resblock_type: str = "1",
+ snake_logscale: bool = True,
+ activation: str = "snakebeta",
+ use_tanh_at_final: bool = False,
+ use_bias_at_final: bool = False,
+ use_cuda_kernel: bool = False,
+ ):
+ super(BigVGAN, self).__init__()
+
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.num_upsamples = len(upsample_rates)
+
+ # pre conv
+ self.conv_pre = weight_norm(
+ Conv1d(num_mels, upsample_initial_channel, 7, 1, padding=3)
+ )
+
+ # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
+ resblock = AMPBlock1 if resblock_type == "1" else AMPBlock2
+
+ # transposed conv-based upsamplers. does not apply anti-aliasing
+ self.ups = nn.ModuleList()
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ self.ups.append(
+ nn.ModuleList(
+ [
+ weight_norm(
+ ConvTranspose1d(
+ upsample_initial_channel // (2**i),
+ upsample_initial_channel // (2 ** (i + 1)),
+ k,
+ u,
+ padding=(k - u) // 2,
+ )
+ )
+ ]
+ )
+ )
+
+ # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
+ self.resblocks = nn.ModuleList()
+ for i in range(len(self.ups)):
+ ch = upsample_initial_channel // (2 ** (i + 1))
+ for j, (k, d) in enumerate(
+ zip(resblock_kernel_sizes, resblock_dilation_sizes)
+ ):
+ self.resblocks.append(
+ resblock(
+ ch,
+ k,
+ d,
+ activation=activation,
+ snake_logscale=snake_logscale,
+ use_cuda_kernel=use_cuda_kernel,
+ )
+ )
+
+ # select which Activation1d, lazy-load cuda version to ensure backward compatibility
+ if use_cuda_kernel:
+ from modules.bigvgan.alias_free_cuda.activation1d import (
+ Activation1d as CudaActivation1d,
+ )
+
+ Activation1d = CudaActivation1d
+ else:
+ Activation1d = TorchActivation1d
+
+ # post conv
+ if (
+ activation == "snake"
+ ): # periodic nonlinearity with snake function and anti-aliasing
+ activation_post = Snake(ch, alpha_logscale=snake_logscale)
+ self.activation_post = Activation1d(activation=activation_post)
+ elif (
+ activation == "snakebeta"
+ ): # periodic nonlinearity with snakebeta function and anti-aliasing
+ activation_post = SnakeBeta(ch, alpha_logscale=snake_logscale)
+ self.activation_post = Activation1d(activation=activation_post)
+ else:
+ raise NotImplementedError(
+ "activation incorrectly specified. check the config file and look for 'activation'."
+ )
+
+ # whether to use bias for the final conv_post. Defaults to True for backward compatibility
+ self.use_bias_at_final = use_bias_at_final
+ self.conv_post = weight_norm(
+ Conv1d(ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final)
+ )
+
+ # weight initialization
+ for i in range(len(self.ups)):
+ self.ups[i].apply(init_weights)
+ self.conv_post.apply(init_weights)
+
+ # final tanh activation. Defaults to True for backward compatibility
+ self.use_tanh_at_final = use_tanh_at_final
+
+ def forward(self, x):
+ # pre conv
+ x = self.conv_pre(x)
+
+ for i in range(self.num_upsamples):
+ # upsampling
+ for i_up in range(len(self.ups[i])):
+ x = self.ups[i][i_up](x)
+ # AMP blocks
+ xs = None
+ for j in range(self.num_kernels):
+ if xs is None:
+ xs = self.resblocks[i * self.num_kernels + j](x)
+ else:
+ xs += self.resblocks[i * self.num_kernels + j](x)
+ x = xs / self.num_kernels
+
+ # post conv
+ x = self.activation_post(x)
+ x = self.conv_post(x)
+ # final tanh activation
+ if self.use_tanh_at_final:
+ x = torch.tanh(x)
+ else:
+ x = torch.clamp(x, min=-1.0, max=1.0) # bound the output to [-1, 1]
+
+ return x
+
+ def remove_weight_norm(self):
+ print("Removing weight norm...")
+ for l in self.ups:
+ for l_i in l:
+ remove_weight_norm(l_i)
+ for l in self.resblocks:
+ l.remove_weight_norm()
+ remove_weight_norm(self.conv_pre)
+ remove_weight_norm(self.conv_post)
diff --git a/fireredtts/modules/codec/speaker.py b/fireredtts/modules/codec/speaker.py
new file mode 100644
index 0000000..d4614db
--- /dev/null
+++ b/fireredtts/modules/codec/speaker.py
@@ -0,0 +1,1052 @@
+from librosa.filters import mel as librosa_mel_fn
+from torch import nn
+from torch.nn import functional as F
+
+import math
+import numpy as np
+import torch
+import torchaudio
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+ return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def spectral_normalize_torch(magnitudes):
+ output = dynamic_range_compression_torch(magnitudes)
+ return output
+
+
+class TorchMelSpectrogram(nn.Module):
+
+ def __init__(
+ self,
+ filter_length=1024,
+ hop_length=160,
+ win_length=640,
+ n_mel_channels=80,
+ mel_fmin=0,
+ mel_fmax=8000,
+ sampling_rate=16000,
+ ):
+
+ super().__init__()
+ self.filter_length = filter_length
+ self.hop_length = hop_length
+ self.win_length = win_length
+ self.n_mel_channels = n_mel_channels
+ self.mel_fmin = mel_fmin
+ self.mel_fmax = mel_fmax
+ self.sampling_rate = sampling_rate
+
+ self.mel_basis = {}
+ self.hann_window = {}
+
+ def forward(self, inp, length=None):
+ if len(inp.shape) == 3:
+ inp = inp.squeeze(1) if inp.shape[1] == 1 else inp.squeeze(2)
+ assert len(inp.shape) == 2
+
+ y = inp
+ if len(list(self.mel_basis.keys())) == 0:
+ mel = librosa_mel_fn(
+ sr=self.sampling_rate,
+ n_fft=self.filter_length,
+ n_mels=self.n_mel_channels,
+ fmin=self.mel_fmin,
+ fmax=self.mel_fmax,
+ )
+ self.mel_basis[str(self.mel_fmax) + "_" + str(y.device)] = (
+ torch.from_numpy(mel).float().to(y.device)
+ )
+ self.hann_window[str(y.device)] = torch.hann_window(self.win_length).to(
+ y.device
+ )
+
+ y = torch.nn.functional.pad(
+ y.unsqueeze(1),
+ (
+ int((self.filter_length - self.hop_length) / 2),
+ int((self.filter_length - self.hop_length) / 2),
+ ),
+ mode="reflect",
+ )
+ y = y.squeeze(1)
+
+ # complex tensor as default, then use view_as_real for future pytorch compatibility
+ spec = torch.stft(
+ y,
+ self.filter_length,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ window=self.hann_window[str(y.device)],
+ center=False,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=True,
+ )
+ spec = torch.view_as_real(spec)
+ spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+
+ spec = torch.matmul(
+ self.mel_basis[str(self.mel_fmax) + "_" + str(y.device)], spec
+ )
+ spec = spectral_normalize_torch(spec)
+
+ max_mel_length = math.ceil(y.shape[-1] / self.hop_length)
+ spec = spec[..., :max_mel_length].transpose(1, 2)
+
+ if length is None:
+ return spec
+ else:
+ spec_len = torch.ceil(length / self.hop_length).clamp(max=spec.shape[1])
+ return spec, spec_len
+
+
+def length_to_mask(length, max_len=None, dtype=None, device=None):
+ """Creates a binary mask for each sequence.
+
+ Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3
+
+ Arguments
+ ---------
+ length : torch.LongTensor
+ Containing the length of each sequence in the batch. Must be 1D.
+ max_len : int
+ Max length for the mask, also the size of the second dimension.
+ dtype : torch.dtype, default: None
+ The dtype of the generated mask.
+ device: torch.device, default: None
+ The device to put the mask variable.
+
+ Returns
+ -------
+ mask : tensor
+ The binary mask.
+
+ Example
+ -------
+ >>> length=torch.Tensor([1,2,3])
+ >>> mask=length_to_mask(length)
+ >>> mask
+ tensor([[1., 0., 0.],
+ [1., 1., 0.],
+ [1., 1., 1.]])
+ """
+ assert len(length.shape) == 1
+
+ if max_len is None:
+ max_len = length.max().long().item() # using arange to generate mask
+ mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand(
+ len(length), max_len
+ ) < length.unsqueeze(1)
+
+ if dtype is None:
+ dtype = length.dtype
+
+ if device is None:
+ device = length.device
+
+ mask = torch.as_tensor(mask, dtype=dtype, device=device)
+ return mask
+
+
+def get_padding_elem(L_in: int, stride: int, kernel_size: int, dilation: int):
+ """This function computes the number of elements to add for zero-padding.
+
+ Arguments
+ ---------
+ L_in : int
+ stride: int
+ kernel_size : int
+ dilation : int
+ """
+ if stride > 1:
+ n_steps = math.ceil(((L_in - kernel_size * dilation) / stride) + 1)
+ L_out = stride * (n_steps - 1) + kernel_size * dilation
+ padding = [kernel_size // 2, kernel_size // 2]
+
+ else:
+ L_out = (L_in - dilation * (kernel_size - 1) - 1) // stride + 1
+
+ padding = [(L_in - L_out) // 2, (L_in - L_out) // 2]
+ return padding
+
+
+class Conv1d(nn.Module):
+ """This function implements 1d convolution.
+
+ Arguments
+ ---------
+ out_channels : int
+ It is the number of output channels.
+ kernel_size : int
+ Kernel size of the convolutional filters.
+ input_shape : tuple
+ The shape of the input. Alternatively use ``in_channels``.
+ in_channels : int
+ The number of input channels. Alternatively use ``input_shape``.
+ stride : int
+ Stride factor of the convolutional filters. When the stride factor > 1,
+ a decimation in time is performed.
+ dilation : int
+ Dilation factor of the convolutional filters.
+ padding : str
+ (same, valid, causal). If "valid", no padding is performed.
+ If "same" and stride is 1, output shape is the same as the input shape.
+ "causal" results in causal (dilated) convolutions.
+ padding_mode : str
+ This flag specifies the type of padding. See torch.nn documentation
+ for more information.
+ skip_transpose : bool
+ If False, uses batch x time x channel convention of speechbrain.
+ If True, uses batch x channel x time convention.
+
+ Example
+ -------
+ >>> inp_tensor = torch.rand([10, 40, 16])
+ >>> cnn_1d = Conv1d(
+ ... input_shape=inp_tensor.shape, out_channels=8, kernel_size=5
+ ... )
+ >>> out_tensor = cnn_1d(inp_tensor)
+ >>> out_tensor.shape
+ torch.Size([10, 40, 8])
+ """
+
+ def __init__(
+ self,
+ out_channels,
+ kernel_size,
+ input_shape=None,
+ in_channels=None,
+ stride=1,
+ dilation=1,
+ padding="same",
+ groups=1,
+ bias=True,
+ padding_mode="reflect",
+ skip_transpose=True,
+ ):
+ super().__init__()
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.dilation = dilation
+ self.padding = padding
+ self.padding_mode = padding_mode
+ self.unsqueeze = False
+ self.skip_transpose = skip_transpose
+
+ if input_shape is None and in_channels is None:
+ raise ValueError("Must provide one of input_shape or in_channels")
+
+ if in_channels is None:
+ in_channels = self._check_input_shape(input_shape)
+
+ self.conv = nn.Conv1d(
+ in_channels,
+ out_channels,
+ self.kernel_size,
+ stride=self.stride,
+ dilation=self.dilation,
+ padding=0,
+ groups=groups,
+ bias=bias,
+ )
+
+ def forward(self, x):
+ """Returns the output of the convolution.
+
+ Arguments
+ ---------
+ x : torch.Tensor (batch, time, channel)
+ input to convolve. 2d or 4d tensors are expected.
+ """
+
+ if not self.skip_transpose:
+ x = x.transpose(1, -1)
+
+ if self.unsqueeze:
+ x = x.unsqueeze(1)
+
+ if self.padding == "same":
+ x = self._manage_padding(x, self.kernel_size, self.dilation, self.stride)
+
+ elif self.padding == "causal":
+ num_pad = (self.kernel_size - 1) * self.dilation
+ x = F.pad(x, (num_pad, 0))
+
+ elif self.padding == "valid":
+ pass
+
+ else:
+ raise ValueError(
+ "Padding must be 'same', 'valid' or 'causal'. Got " + self.padding
+ )
+
+ wx = self.conv(x)
+
+ if self.unsqueeze:
+ wx = wx.squeeze(1)
+
+ if not self.skip_transpose:
+ wx = wx.transpose(1, -1)
+
+ return wx
+
+ def _manage_padding(
+ self,
+ x,
+ kernel_size: int,
+ dilation: int,
+ stride: int,
+ ):
+ """This function performs zero-padding on the time axis
+ such that their lengths is unchanged after the convolution.
+
+ Arguments
+ ---------
+ x : torch.Tensor
+ Input tensor.
+ kernel_size : int
+ Size of kernel.
+ dilation : int
+ Dilation used.
+ stride : int
+ Stride.
+ """
+
+ # Detecting input shape
+ L_in = x.shape[-1]
+
+ # Time padding
+ padding = get_padding_elem(L_in, stride, kernel_size, dilation)
+
+ # Applying padding
+ x = F.pad(x, padding, mode=self.padding_mode)
+
+ return x
+
+ def _check_input_shape(self, shape):
+ """Checks the input shape and returns the number of input channels."""
+
+ if len(shape) == 2:
+ self.unsqueeze = True
+ in_channels = 1
+ elif self.skip_transpose:
+ in_channels = shape[1]
+ elif len(shape) == 3:
+ in_channels = shape[2]
+ else:
+ raise ValueError("conv1d expects 2d, 3d inputs. Got " + str(len(shape)))
+
+ # Kernel size must be odd
+ if self.kernel_size % 2 == 0:
+ raise ValueError(
+ "The field kernel size must be an odd number. Got %s."
+ % (self.kernel_size)
+ )
+ return in_channels
+
+
+class Fp32BatchNorm(nn.Module):
+ def __init__(self, sync=True, *args, **kwargs):
+ super().__init__()
+
+ if (
+ not torch.distributed.is_initialized()
+ or torch.distributed.get_world_size() == 1
+ ):
+ sync = False
+
+ if sync:
+ self.bn = nn.SyncBatchNorm(*args, **kwargs)
+ else:
+ self.bn = nn.BatchNorm1d(*args, **kwargs)
+
+ self.sync = sync
+
+ def forward(self, input):
+ if self.bn.running_mean.dtype != torch.float:
+ if self.sync:
+ self.bn.running_mean = self.bn.running_mean.float()
+ self.bn.running_var = self.bn.running_var.float()
+ if self.bn.affine:
+ try:
+ self.bn.weight = self.bn.weight.float()
+ self.bn.bias = self.bn.bias.float()
+ except:
+ self.bn.float()
+ else:
+ self.bn.float()
+
+ output = self.bn(input.float())
+ return output.type_as(input)
+
+
+class BatchNorm1d(nn.Module):
+ """Applies 1d batch normalization to the input tensor.
+
+ Arguments
+ ---------
+ input_shape : tuple
+ The expected shape of the input. Alternatively, use ``input_size``.
+ input_size : int
+ The expected size of the input. Alternatively, use ``input_shape``.
+ eps : float
+ This value is added to std deviation estimation to improve the numerical
+ stability.
+ momentum : float
+ It is a value used for the running_mean and running_var computation.
+ affine : bool
+ When set to True, the affine parameters are learned.
+ track_running_stats : bool
+ When set to True, this module tracks the running mean and variance,
+ and when set to False, this module does not track such statistics.
+ combine_batch_time : bool
+ When true, it combines batch an time axis.
+
+
+ Example
+ -------
+ >>> input = torch.randn(100, 10)
+ >>> norm = BatchNorm1d(input_shape=input.shape)
+ >>> output = norm(input)
+ >>> output.shape
+ torch.Size([100, 10])
+ """
+
+ def __init__(
+ self,
+ input_shape=None,
+ input_size=None,
+ eps=1e-05,
+ momentum=0.1,
+ affine=True,
+ track_running_stats=True,
+ combine_batch_time=False,
+ skip_transpose=True,
+ enabled=True,
+ ):
+ super().__init__()
+ self.combine_batch_time = combine_batch_time
+ self.skip_transpose = skip_transpose
+
+ if input_size is None and skip_transpose:
+ input_size = input_shape[1]
+ elif input_size is None:
+ input_size = input_shape[-1]
+
+ if enabled:
+ self.norm = Fp32BatchNorm(
+ num_features=input_size,
+ eps=eps,
+ momentum=momentum,
+ affine=affine,
+ track_running_stats=track_running_stats,
+ )
+ else:
+ self.norm = nn.Identity()
+
+ def forward(self, x):
+ """Returns the normalized input tensor.
+
+ Arguments
+ ---------
+ x : torch.Tensor (batch, time, [channels])
+ input to normalize. 2d or 3d tensors are expected in input
+ 4d tensors can be used when combine_dims=True.
+ """
+ shape_or = x.shape
+ if self.combine_batch_time:
+ if x.ndim == 3:
+ x = x.reshape(shape_or[0] * shape_or[1], shape_or[2])
+ else:
+ x = x.reshape(shape_or[0] * shape_or[1], shape_or[3], shape_or[2])
+
+ elif not self.skip_transpose:
+ x = x.transpose(-1, 1)
+
+ x_n = self.norm(x)
+
+ if self.combine_batch_time:
+ x_n = x_n.reshape(shape_or)
+ elif not self.skip_transpose:
+ x_n = x_n.transpose(1, -1)
+
+ return x_n
+
+
+class Linear(torch.nn.Module):
+ """Computes a linear transformation y = wx + b.
+
+ Arguments
+ ---------
+ n_neurons : int
+ It is the number of output neurons (i.e, the dimensionality of the
+ output).
+ bias : bool
+ If True, the additive bias b is adopted.
+ combine_dims : bool
+ If True and the input is 4D, combine 3rd and 4th dimensions of input.
+
+ Example
+ -------
+ >>> inputs = torch.rand(10, 50, 40)
+ >>> lin_t = Linear(input_shape=(10, 50, 40), n_neurons=100)
+ >>> output = lin_t(inputs)
+ >>> output.shape
+ torch.Size([10, 50, 100])
+ """
+
+ def __init__(
+ self,
+ n_neurons,
+ input_shape=None,
+ input_size=None,
+ bias=True,
+ combine_dims=False,
+ ):
+ super().__init__()
+ self.combine_dims = combine_dims
+
+ if input_shape is None and input_size is None:
+ raise ValueError("Expected one of input_shape or input_size")
+
+ if input_size is None:
+ input_size = input_shape[-1]
+ if len(input_shape) == 4 and self.combine_dims:
+ input_size = input_shape[2] * input_shape[3]
+
+ # Weights are initialized following pytorch approach
+ self.w = nn.Linear(input_size, n_neurons, bias=bias)
+
+ def forward(self, x):
+ """Returns the linear transformation of input tensor.
+
+ Arguments
+ ---------
+ x : torch.Tensor
+ Input to transform linearly.
+ """
+ if x.ndim == 4 and self.combine_dims:
+ x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3])
+
+ wx = self.w(x)
+
+ return wx
+
+
+class TDNNBlock(nn.Module):
+ """An implementation of TDNN.
+
+ Arguments
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ The number of output channels.
+ kernel_size : int
+ The kernel size of the TDNN blocks.
+ dilation : int
+ The dilation of the Res2Net block.
+ activation : torch class
+ A class for constructing the activation layers.
+
+ Example
+ -------
+ >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2)
+ >>> layer = TDNNBlock(64, 64, kernel_size=3, dilation=1)
+ >>> out_tensor = layer(inp_tensor).transpose(1, 2)
+ >>> out_tensor.shape
+ torch.Size([8, 120, 64])
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ dilation,
+ activation=nn.ReLU,
+ batch_norm=True,
+ ):
+ super(TDNNBlock, self).__init__()
+ self.conv = Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ )
+ self.activation = activation()
+ self.norm = BatchNorm1d(input_size=out_channels, enabled=batch_norm)
+
+ def forward(self, x):
+ return self.norm(self.activation(self.conv(x)))
+
+
+class Res2NetBlock(torch.nn.Module):
+ """An implementation of Res2NetBlock w/ dilation.
+
+ Arguments
+ ---------
+ in_channels : int
+ The number of channels expected in the input.
+ out_channels : int
+ The number of output channels.
+ scale : int
+ The scale of the Res2Net block.
+ kernel_size: int
+ The kernel size of the Res2Net block.
+ dilation : int
+ The dilation of the Res2Net block.
+
+ Example
+ -------
+ >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2)
+ >>> layer = Res2NetBlock(64, 64, scale=4, dilation=3)
+ >>> out_tensor = layer(inp_tensor).transpose(1, 2)
+ >>> out_tensor.shape
+ torch.Size([8, 120, 64])
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ scale=8,
+ kernel_size=3,
+ dilation=1,
+ batch_norm=True,
+ ):
+ super(Res2NetBlock, self).__init__()
+ assert in_channels % scale == 0
+ assert out_channels % scale == 0
+
+ in_channel = in_channels // scale
+ hidden_channel = out_channels // scale
+
+ self.blocks = nn.ModuleList(
+ [
+ TDNNBlock(
+ in_channel,
+ hidden_channel,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ batch_norm=batch_norm,
+ )
+ for i in range(scale - 1)
+ ]
+ )
+ self.scale = scale
+
+ def forward(self, x):
+ y = []
+ for i, x_i in enumerate(torch.chunk(x, self.scale, dim=1)):
+ if i == 0:
+ y_i = x_i
+ elif i == 1:
+ y_i = self.blocks[i - 1](x_i)
+ else:
+ y_i = self.blocks[i - 1](x_i + y_i)
+ y.append(y_i)
+ y = torch.cat(y, dim=1)
+ return y
+
+
+class SEBlock(nn.Module):
+ """An implementation of squeeze-and-excitation block.
+
+ Arguments
+ ---------
+ in_channels : int
+ The number of input channels.
+ se_channels : int
+ The number of output channels after squeeze.
+ out_channels : int
+ The number of output channels.
+
+ Example
+ -------
+ >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2)
+ >>> se_layer = SEBlock(64, 16, 64)
+ >>> lengths = torch.rand((8,))
+ >>> out_tensor = se_layer(inp_tensor, lengths).transpose(1, 2)
+ >>> out_tensor.shape
+ torch.Size([8, 120, 64])
+ """
+
+ def __init__(self, in_channels, se_channels, out_channels):
+ super(SEBlock, self).__init__()
+
+ self.conv1 = Conv1d(
+ in_channels=in_channels, out_channels=se_channels, kernel_size=1
+ )
+ self.relu = torch.nn.ReLU(inplace=True)
+ self.conv2 = Conv1d(
+ in_channels=se_channels, out_channels=out_channels, kernel_size=1
+ )
+ self.sigmoid = torch.nn.Sigmoid()
+
+ def forward(self, x, lengths=None):
+ L = x.shape[-1]
+ if lengths is not None:
+ mask = length_to_mask(lengths * L, max_len=L, device=x.device)
+ mask = mask.unsqueeze(1)
+ total = mask.sum(dim=2, keepdim=True)
+ s = (x * mask).sum(dim=2, keepdim=True) / total
+ else:
+ s = x.mean(dim=2, keepdim=True)
+
+ s = self.relu(self.conv1(s))
+ s = self.sigmoid(self.conv2(s))
+
+ return s * x
+
+
+class AttentiveStatisticsPooling(nn.Module):
+ """This class implements an attentive statistic pooling layer for each channel.
+ It returns the concatenated mean and std of the input tensor.
+
+ Arguments
+ ---------
+ channels: int
+ The number of input channels.
+ attention_channels: int
+ The number of attention channels.
+
+ Example
+ -------
+ >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2)
+ >>> asp_layer = AttentiveStatisticsPooling(64)
+ >>> lengths = torch.rand((8,))
+ >>> out_tensor = asp_layer(inp_tensor, lengths).transpose(1, 2)
+ >>> out_tensor.shape
+ torch.Size([8, 1, 128])
+ """
+
+ def __init__(
+ self, channels, attention_channels=128, global_context=True, batch_norm=True
+ ):
+ super().__init__()
+
+ self.eps = 1e-12
+ self.global_context = global_context
+ if global_context:
+ self.tdnn = TDNNBlock(
+ channels * 3, attention_channels, 1, 1, batch_norm=batch_norm
+ )
+ else:
+ self.tdnn = TDNNBlock(
+ channels, attention_channels, 1, 1, batch_norm, batch_norm
+ )
+ self.tanh = nn.Tanh()
+ self.conv = Conv1d(
+ in_channels=attention_channels, out_channels=channels, kernel_size=1
+ )
+
+ def forward(self, x, lengths=None):
+ """Calculates mean and std for a batch (input tensor).
+
+ Arguments
+ ---------
+ x : torch.Tensor
+ Tensor of shape [N, C, L].
+ """
+ L = x.shape[-1]
+
+ def _compute_statistics(x, m, dim=2, eps=self.eps):
+ mean = (m * x).sum(dim)
+ std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps))
+ return mean, std
+
+ if lengths is None:
+ lengths = torch.ones(x.shape[0], device=x.device)
+
+ # Make binary mask of shape [N, 1, L]
+ mask = length_to_mask(lengths * L, max_len=L, device=x.device)
+ mask = mask.unsqueeze(1)
+
+ # Expand the temporal context of the pooling layer by allowing the
+ # self-attention to look at global properties of the utterance.
+ if self.global_context:
+ # torch.std is unstable for backward computation
+ # https://github.com/pytorch/pytorch/issues/4320
+ total = mask.sum(dim=2, keepdim=True).float()
+ mean, std = _compute_statistics(x, mask / total)
+ mean = mean.unsqueeze(2).repeat(1, 1, L)
+ std = std.unsqueeze(2).repeat(1, 1, L)
+ attn = torch.cat([x, mean, std], dim=1)
+ else:
+ attn = x
+
+ # Apply layers
+ attn = self.conv(self.tanh(self.tdnn(attn)))
+
+ # Filter out zero-paddings
+ attn = attn.masked_fill(mask == 0, float("-inf"))
+
+ attn = F.softmax(attn, dim=2)
+ mean, std = _compute_statistics(x, attn)
+ # Append mean and std of the batch
+ pooled_stats = torch.cat((mean, std), dim=1)
+ pooled_stats = pooled_stats.unsqueeze(2)
+
+ return pooled_stats
+
+
+class SERes2NetBlock(nn.Module):
+ """An implementation of building block in ECAPA-TDNN, i.e.,
+ TDNN-Res2Net-TDNN-SEBlock.
+
+ Arguments
+ ----------
+ out_channels: int
+ The number of output channels.
+ res2net_scale: int
+ The scale of the Res2Net block.
+ kernel_size: int
+ The kernel size of the TDNN blocks.
+ dilation: int
+ The dilation of the Res2Net block.
+ activation : torch class
+ A class for constructing the activation layers.
+
+ Example
+ -------
+ >>> x = torch.rand(8, 120, 64).transpose(1, 2)
+ >>> conv = SERes2NetBlock(64, 64, res2net_scale=4)
+ >>> out = conv(x).transpose(1, 2)
+ >>> out.shape
+ torch.Size([8, 120, 64])
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ res2net_scale=8,
+ se_channels=128,
+ kernel_size=1,
+ dilation=1,
+ activation=torch.nn.ReLU,
+ batch_norm=True,
+ ):
+ super().__init__()
+ self.out_channels = out_channels
+ self.tdnn1 = TDNNBlock(
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ activation=activation,
+ batch_norm=batch_norm,
+ )
+ self.res2net_block = Res2NetBlock(
+ out_channels,
+ out_channels,
+ res2net_scale,
+ kernel_size,
+ dilation,
+ batch_norm=batch_norm,
+ )
+ self.tdnn2 = TDNNBlock(
+ out_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ activation=activation,
+ batch_norm=batch_norm,
+ )
+ self.se_block = SEBlock(out_channels, se_channels, out_channels)
+
+ self.shortcut = None
+ if in_channels != out_channels:
+ self.shortcut = Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ )
+
+ def forward(self, x, lengths=None):
+ residual = x
+ if self.shortcut:
+ residual = self.shortcut(x)
+
+ x = self.tdnn1(x)
+ x = self.res2net_block(x)
+ x = self.tdnn2(x)
+ x = self.se_block(x, lengths)
+
+ return x + residual
+
+
+class ECAPA_TDNN(torch.nn.Module):
+ """An implementation of the speaker embedding model in a paper.
+ "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in
+ TDNN Based Speaker Verification" (https://arxiv.org/abs/2005.07143).
+
+ Arguments
+ ---------
+ device : str
+ Device used, e.g., "cpu" or "cuda".
+ activation : torch class
+ A class for constructing the activation layers.
+ channels : list of ints
+ Output channels for TDNN/SERes2Net layer.
+ kernel_sizes : list of ints
+ List of kernel sizes for each layer.
+ dilations : list of ints
+ List of dilations for kernels in each layer.
+ lin_neurons : int
+ Number of neurons in linear layers.
+
+ Example
+ -------
+ >>> input_feats = torch.rand([5, 120, 80])
+ >>> compute_embedding = ECAPA_TDNN(80, lin_neurons=192)
+ >>> outputs = compute_embedding(input_feats)
+ >>> outputs.shape
+ torch.Size([5, 1, 192])
+ """
+
+ def __init__(
+ self,
+ input_size,
+ lin_neurons=192,
+ activation=torch.nn.ReLU,
+ channels=[512, 512, 512, 512, 1536],
+ kernel_sizes=[5, 3, 3, 3, 1],
+ dilations=[1, 2, 3, 4, 1],
+ attention_channels=128,
+ res2net_scale=8,
+ se_channels=128,
+ global_context=True,
+ batch_norm=True,
+ ):
+
+ super().__init__()
+ assert len(channels) == len(kernel_sizes)
+ assert len(channels) == len(dilations)
+ self.channels = channels
+ self.blocks = nn.ModuleList()
+
+ # The initial TDNN layer
+ self.blocks.append(
+ TDNNBlock(
+ input_size,
+ channels[0],
+ kernel_sizes[0],
+ dilations[0],
+ activation,
+ batch_norm=batch_norm,
+ )
+ )
+
+ # SE-Res2Net layers
+ for i in range(1, len(channels) - 1):
+ self.blocks.append(
+ SERes2NetBlock(
+ channels[i - 1],
+ channels[i],
+ res2net_scale=res2net_scale,
+ se_channels=se_channels,
+ kernel_size=kernel_sizes[i],
+ dilation=dilations[i],
+ activation=activation,
+ batch_norm=batch_norm,
+ )
+ )
+
+ # Multi-layer feature aggregation
+ self.mfa = TDNNBlock(
+ channels[-1],
+ channels[-1],
+ kernel_sizes[-1],
+ dilations[-1],
+ activation,
+ batch_norm=batch_norm,
+ )
+
+ # Attentive Statistical Pooling
+ self.asp = AttentiveStatisticsPooling(
+ channels[-1],
+ attention_channels=attention_channels,
+ global_context=global_context,
+ batch_norm=batch_norm,
+ )
+ self.asp_bn = BatchNorm1d(input_size=channels[-1] * 2, enabled=batch_norm)
+
+ # Final linear transformation
+ self.fc = Conv1d(
+ in_channels=channels[-1] * 2,
+ out_channels=lin_neurons,
+ kernel_size=1,
+ )
+
+ # @torch.cuda.amp.autocast(enabled=True, dtype=torch.float32)
+ def forward(self, x, lengths=None):
+ """Returns the embedding vector.
+
+ Arguments
+ ---------
+ x : torch.Tensor
+ Tensor of shape (batch, time, channel).
+ """
+ # Minimize transpose for efficiency
+ x = x.transpose(1, 2)
+
+ xl = []
+ for layer in self.blocks:
+ try:
+ x = layer(x, lengths=lengths)
+ except TypeError:
+ x = layer(x)
+ xl.append(x)
+
+ # Multi-layer feature aggregation
+ x = torch.cat(xl[1:], dim=1)
+ x = self.mfa(x)
+
+ # Attentive Statistical Pooling
+ x = self.asp(x, lengths=lengths)
+ x = self.asp_bn(x)
+
+ # Final linear transformation
+ x = self.fc(x)
+
+ x = x.squeeze(-1)
+ return x
+
+
+class SpeakerEmbedddingExtractor(object):
+
+ def __init__(self, ckpt_path, device="cuda"):
+ # NOTE: The sampling rate is 16000
+ self.mel_extractor = TorchMelSpectrogram()
+ self.mel_extractor.to(device)
+ model = ECAPA_TDNN(
+ 80,
+ 512,
+ channels=[512, 512, 512, 512, 1536],
+ kernel_sizes=[5, 3, 3, 3, 1],
+ dilations=[1, 2, 3, 4, 1],
+ attention_channels=128,
+ res2net_scale=4,
+ se_channels=128,
+ global_context=True,
+ batch_norm=True,
+ )
+ model.load_state_dict(torch.load(ckpt_path), strict=True)
+ model.eval()
+ self.model = model
+ self.model.to(device)
+
+ def __call__(self, wav):
+ # wav, sr = torchaudio.load(audio_path)
+ # assert sr == 16000, f"The sampling rate is not 16000"
+ # print(wav.shape)
+ mel = self.mel_extractor(wav.unsqueeze(0))
+ spk = self.model(mel)
+ spk = spk[0]
+ return spk
diff --git a/fireredtts/modules/flow/__init__.py b/fireredtts/modules/flow/__init__.py
new file mode 100644
index 0000000..221b727
--- /dev/null
+++ b/fireredtts/modules/flow/__init__.py
@@ -0,0 +1,24 @@
+from fireredtts.modules.flow.codec_embedding import HHGCodecEmbedding
+from fireredtts.modules.flow.conformer import ConformerDecoderV2
+from fireredtts.modules.flow.mel_encoder import MelReduceEncoder
+from fireredtts.modules.flow.decoder import ConditionalCFM, ConditionalDecoder
+from fireredtts.modules.flow.flow_model import InterpolateRegulator, CrossAttnFlowMatching
+from fireredtts.modules.flow.mel_spectrogram import MelSpectrogramExtractor
+
+
+def get_flow_frontend(flow_config):
+ flow = CrossAttnFlowMatching(
+ output_size=flow_config["output_size"],
+ input_embedding=HHGCodecEmbedding(**flow_config["input_embedding"]),
+ encoder=ConformerDecoderV2(**flow_config["encoder"]),
+ length_regulator=InterpolateRegulator(**flow_config["length_regulator"]),
+ mel_encoder=MelReduceEncoder(**flow_config["mel_encoder"]),
+ decoder=ConditionalCFM(
+ estimator=ConditionalDecoder(**flow_config["decoder"]["estimator"]),
+ t_scheduler=flow_config["decoder"]["t_scheduler"],
+ inference_cfg_rate=flow_config["decoder"]["inference_cfg_rate"]
+ )
+ )
+ return flow
+
+
diff --git a/fireredtts/modules/flow/codebook.npy b/fireredtts/modules/flow/codebook.npy
new file mode 100644
index 0000000..072ae3f
Binary files /dev/null and b/fireredtts/modules/flow/codebook.npy differ
diff --git a/fireredtts/modules/flow/codec_embedding.py b/fireredtts/modules/flow/codec_embedding.py
new file mode 100644
index 0000000..b0a874f
--- /dev/null
+++ b/fireredtts/modules/flow/codec_embedding.py
@@ -0,0 +1,31 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import os.path as osp
+now_dir = osp.dirname(osp.abspath(__file__))
+class HHGCodecEmbedding(nn.Module):
+ def __init__(self, out_channels, codebook_path:str, freeze=True):
+ super().__init__()
+ # (2, 128, 128)
+ codebook_path = osp.join(now_dir,"codebook.npy")
+ codebook = torch.from_numpy(np.load(codebook_path).copy())
+ assert codebook.shape[0] == 2 and codebook.shape[1] == 128
+ self.codebook_dim = codebook.shape[2]
+
+ self.codebook = torch.nn.ModuleList([
+ torch.nn.Embedding.from_pretrained(codebook[i], freeze=freeze)
+ for i in range(codebook.shape[0])]
+ )
+ if self.codebook_dim * 2 != out_channels:
+ self.proj = nn.Linear(self.codebook_dim * 2, out_channels)
+ else:
+ self.proj = nn.Identity()
+
+ def forward(self, tokens):
+ token_embs = torch.cat([
+ self.codebook[0](tokens % 128),
+ self.codebook[1](tokens // 128)
+ ], dim=-1)
+ token_embs = self.proj(token_embs)
+ return token_embs
+
diff --git a/fireredtts/modules/flow/conformer.py b/fireredtts/modules/flow/conformer.py
new file mode 100644
index 0000000..296ba77
--- /dev/null
+++ b/fireredtts/modules/flow/conformer.py
@@ -0,0 +1,730 @@
+import typing as tp
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from fireredtts.modules.flow.utils import make_pad_mask
+
+
+class MultiHeadedAttention(nn.Module):
+ """Multi-Head Attention layer.
+
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self,
+ n_head: int,
+ n_feat: int,
+ dropout_rate: float,
+ key_bias: bool = True):
+ """Construct an MultiHeadedAttention object."""
+ super().__init__()
+ assert n_feat % n_head == 0
+ # We assume d_v always equals d_k
+ self.d_k = n_feat // n_head
+ self.h = n_head
+ self.linear_q = nn.Linear(n_feat, n_feat)
+ self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias)
+ self.linear_v = nn.Linear(n_feat, n_feat)
+ self.linear_out = nn.Linear(n_feat, n_feat)
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ def forward_qkv(
+ self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+ ) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Transform query, key and value.
+
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+
+ Returns:
+ torch.Tensor: Transformed query tensor, size
+ (#batch, n_head, time1, d_k).
+ torch.Tensor: Transformed key tensor, size
+ (#batch, n_head, time2, d_k).
+ torch.Tensor: Transformed value tensor, size
+ (#batch, n_head, time2, d_k).
+
+ """
+ n_batch = query.size(0)
+ q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
+ k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
+ v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
+ q = q.transpose(1, 2) # (batch, head, time1, d_k)
+ k = k.transpose(1, 2) # (batch, head, time2, d_k)
+ v = v.transpose(1, 2) # (batch, head, time2, d_k)
+
+ return q, k, v
+
+ def forward_attention(
+ self,
+ value: torch.Tensor,
+ scores: torch.Tensor,
+ mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
+ ) -> torch.Tensor:
+ """Compute attention context vector.
+
+ Args:
+ value (torch.Tensor): Transformed value, size
+ (#batch, n_head, time2, d_k).
+ scores (torch.Tensor): Attention score, size
+ (#batch, n_head, time1, time2).
+ mask (torch.Tensor): Mask, size (#batch, 1, time2) or
+ (#batch, time1, time2), (0, 0, 0) means fake mask.
+
+ Returns:
+ torch.Tensor: Transformed value (#batch, time1, d_model)
+ weighted by the attention score (#batch, time1, time2).
+
+ """
+ n_batch = value.size(0)
+ # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
+ # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
+ # 1st chunk to ease the onnx export.]
+ # 2. pytorch training
+ if mask.size(2) > 0: # time2 > 0
+ mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
+ # For last chunk, time2 might be larger than scores.size(-1)
+ mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2)
+ scores = scores.masked_fill(mask, -float('inf'))
+ attn = torch.softmax(scores, dim=-1).masked_fill(
+ mask, 0.0) # (batch, head, time1, time2)
+ # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
+ # 1. onnx(16/-1, -1/-1, 16/0)
+ # 2. jit (16/-1, -1/-1, 16/0, 16/4)
+ else:
+ attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
+
+ p_attn = self.dropout(attn)
+ x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
+ x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
+ self.h * self.d_k)
+ ) # (batch, time1, d_model)
+
+ return self.linear_out(x) # (batch, time1, d_model)
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ pos_emb: torch.Tensor = torch.empty(0),
+ cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
+ ) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+ """Compute scaled dot product attention.
+
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+ mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+ (#batch, time1, time2).
+ 1.When applying cross attention between decoder and encoder,
+ the batch padding mask for input is in (#batch, 1, T) shape.
+ 2.When applying self attention of encoder,
+ the mask is in (#batch, T, T) shape.
+ cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time1, d_model).
+ torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+
+ """
+ q, k, v = self.forward_qkv(query, key, value)
+
+ # NOTE(xcsong):
+ # when export onnx model, for 1st chunk, we feed
+ # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+ # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+ # In all modes, `if cache.size(0) > 0` will alwayse be `True`
+ # and we will always do splitting and
+ # concatnation(this will simplify onnx export). Note that
+ # it's OK to concat & split zero-shaped tensors(see code below).
+ # when export jit model, for 1st chunk, we always feed
+ # cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+ # >>> a = torch.ones((1, 2, 0, 4))
+ # >>> b = torch.ones((1, 2, 3, 4))
+ # >>> c = torch.cat((a, b), dim=2)
+ # >>> torch.equal(b, c) # True
+ # >>> d = torch.split(a, 2, dim=-1)
+ # >>> torch.equal(d[0], d[1]) # True
+ if cache.size(0) > 0:
+ key_cache, value_cache = torch.split(cache,
+ cache.size(-1) // 2,
+ dim=-1)
+ k = torch.cat([key_cache, k], dim=2)
+ v = torch.cat([value_cache, v], dim=2)
+ # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+ # non-trivial to calculate `next_cache_start` here.
+ new_cache = torch.cat((k, v), dim=-1)
+
+ scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+ return self.forward_attention(v, scores, mask), new_cache
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+ """Multi-Head Attention layer with relative position encoding.
+ Paper: https://arxiv.org/abs/1901.02860
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+ """
+
+ def __init__(self,
+ n_head: int,
+ n_feat: int,
+ dropout_rate: float,
+ key_bias: bool = True):
+ """Construct an RelPositionMultiHeadedAttention object."""
+ super().__init__(n_head, n_feat, dropout_rate, key_bias)
+ # linear transformation for positional encoding
+ self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+ # these two learnable bias are used in matrix c and matrix d
+ # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+ self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+ self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+ torch.nn.init.xavier_uniform_(self.pos_bias_u)
+ torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+ def rel_shift(self, x):
+ """Compute relative positional encoding.
+
+ Args:
+ x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
+ time1 means the length of query vector.
+
+ Returns:
+ torch.Tensor: Output tensor.
+
+ """
+ zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
+ x_padded = torch.cat([zero_pad, x], dim=-1)
+
+ x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
+ x = x_padded[:, :, 1:].view_as(x)[
+ :, :, :, : x.size(-1) // 2 + 1
+ ] # only keep the positions from 0 to time2
+ return x
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ pos_emb: torch.Tensor = torch.empty(0),
+ cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
+ ) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+ """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+ mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+ (#batch, time1, time2), (0, 0, 0) means fake mask.
+ pos_emb (torch.Tensor): Positional embedding tensor
+ (#batch, time2, size).
+ cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+ Returns:
+ torch.Tensor: Output tensor (#batch, time1, d_model).
+ torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+ """
+ q, k, v = self.forward_qkv(query, key, value)
+ q = q.transpose(1, 2) # (batch, time1, head, d_k)
+
+ # NOTE(xcsong):
+ # when export onnx model, for 1st chunk, we feed
+ # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+ # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+ # In all modes, `if cache.size(0) > 0` will alwayse be `True`
+ # and we will always do splitting and
+ # concatnation(this will simplify onnx export). Note that
+ # it's OK to concat & split zero-shaped tensors(see code below).
+ # when export jit model, for 1st chunk, we always feed
+ # cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+ # >>> a = torch.ones((1, 2, 0, 4))
+ # >>> b = torch.ones((1, 2, 3, 4))
+ # >>> c = torch.cat((a, b), dim=2)
+ # >>> torch.equal(b, c) # True
+ # >>> d = torch.split(a, 2, dim=-1)
+ # >>> torch.equal(d[0], d[1]) # True
+ if cache.size(0) > 0:
+ key_cache, value_cache = torch.split(cache,
+ cache.size(-1) // 2,
+ dim=-1)
+ k = torch.cat([key_cache, k], dim=2)
+ v = torch.cat([value_cache, v], dim=2)
+ # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+ # non-trivial to calculate `next_cache_start` here.
+ new_cache = torch.cat((k, v), dim=-1)
+
+ n_batch_pos = pos_emb.size(0)
+ p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+ p = p.transpose(1, 2) # (batch, head, time1, d_k)
+
+ # (batch, head, time1, d_k)
+ q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+ # (batch, head, time1, d_k)
+ q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+ # compute attention score
+ # first compute matrix a and matrix c
+ # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+ # (batch, head, time1, time2)
+ matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+ # compute matrix b and matrix d
+ # (batch, head, time1, time2)
+ matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+ # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
+ if matrix_ac.shape != matrix_bd.shape:
+ matrix_bd = self.rel_shift(matrix_bd)
+
+ scores = (matrix_ac + matrix_bd) / math.sqrt(
+ self.d_k) # (batch, head, time1, time2)
+
+ return self.forward_attention(v, scores, mask), new_cache
+
+
+class PositionwiseFeedForward(torch.nn.Module):
+ """Positionwise feed forward layer.
+
+ FeedForward are appied on each position of the sequence.
+ The output dim is same with the input dim.
+
+ Args:
+ idim (int): Input dimenstion.
+ hidden_units (int): The number of hidden units.
+ dropout_rate (float): Dropout rate.
+ activation (torch.nn.Module): Activation function
+ """
+
+ def __init__(
+ self,
+ idim: int,
+ hidden_units: int,
+ dropout_rate: float,
+ activation: torch.nn.Module = torch.nn.ReLU(),
+ ):
+ """Construct a PositionwiseFeedForward object."""
+ super(PositionwiseFeedForward, self).__init__()
+ self.w_1 = torch.nn.Linear(idim, hidden_units)
+ self.activation = activation
+ self.dropout = torch.nn.Dropout(dropout_rate)
+ self.w_2 = torch.nn.Linear(hidden_units, idim)
+
+ def forward(self, xs: torch.Tensor) -> torch.Tensor:
+ """Forward function.
+
+ Args:
+ xs: input tensor (B, L, D)
+ Returns:
+ output tensor, (B, L, D)
+ """
+ return self.w_2(self.dropout(self.activation(self.w_1(xs))))
+
+
+class ConformerDecoderLayer(nn.Module):
+ """Encoder layer module.
+ Args:
+ size (int): Input dimension.
+ self_attn (torch.nn.Module): Self-attention module instance.
+ `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+ instance can be used as the argument.
+ src_attn (torch.nn.Module): Cross-attention module instance.
+ `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+ instance can be used as the argument.
+ feed_forward (torch.nn.Module): Feed-forward module instance.
+ `PositionwiseFeedForward` instance can be used as the argument.
+ feed_forward_macaron (torch.nn.Module): Additional feed-forward module
+ instance.
+ `PositionwiseFeedForward` instance can be used as the argument.
+ conv_module (torch.nn.Module): Convolution module instance.
+ `ConvlutionModule` instance can be used as the argument.
+ dropout_rate (float): Dropout rate.
+ normalize_before (bool):
+ True: use layer_norm before each sub-block.
+ False: use layer_norm after each sub-block.
+ """
+
+ def __init__(
+ self,
+ size: int,
+ self_attn: torch.nn.Module,
+ src_attn: tp.Optional[torch.nn.Module] = None,
+ feed_forward: tp.Optional[nn.Module] = None,
+ feed_forward_macaron: tp.Optional[nn.Module] = None,
+ conv_module: tp.Optional[nn.Module] = None,
+ dropout_rate: float = 0.1,
+ normalize_before: bool = True,
+ ):
+ """Construct an EncoderLayer object."""
+ super().__init__()
+ self.self_attn = self_attn
+ self.src_attn = src_attn
+ self.feed_forward = feed_forward
+ self.feed_forward_macaron = feed_forward_macaron
+ self.conv_module = conv_module
+ self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module
+ self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module
+ if src_attn is not None:
+ self.norm_mha2 = nn.LayerNorm(size, eps=1e-5) # for the MHA module(src_attn)
+ if feed_forward_macaron is not None:
+ self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5)
+ self.ff_scale = 0.5
+ else:
+ self.ff_scale = 1.0
+ if self.conv_module is not None:
+ self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module
+ self.norm_final = nn.LayerNorm(
+ size, eps=1e-5) # for the final output of the block
+ self.dropout = nn.Dropout(dropout_rate)
+ self.size = size
+ self.normalize_before = normalize_before
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ mask: torch.Tensor,
+ # src-attention
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+ cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+ ) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Compute encoded features.
+
+ Args:
+ x (torch.Tensor): (#batch, time, size)
+ mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+ (0, 0, 0) means fake mask.
+ pos_emb (torch.Tensor): positional encoding, must not be None
+ for ConformerEncoderLayer.
+ mask_pad (torch.Tensor): batch padding mask used for conv module.
+ (#batch, 1, time), (0, 0, 0) means fake mask.
+ att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+ (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+ cnn_cache (torch.Tensor): Convolution cache in conformer layer
+ (#batch=1, size, cache_t2)
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time, time).
+ torch.Tensor: att_cache tensor,
+ (#batch=1, head, cache_t1 + time, d_k * 2).
+ torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
+ """
+
+ # whether to use macaron style
+ if self.feed_forward_macaron is not None:
+ residual = x
+ if self.normalize_before:
+ x = self.norm_ff_macaron(x)
+ x = residual + self.ff_scale * self.dropout(
+ self.feed_forward_macaron(x))
+ if not self.normalize_before:
+ x = self.norm_ff_macaron(x)
+
+ # multi-headed self-attention module
+ residual = x
+ if self.normalize_before:
+ x = self.norm_mha(x)
+ x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb,
+ att_cache)
+ x = residual + self.dropout(x_att)
+ if not self.normalize_before:
+ x = self.norm_mha(x)
+
+ # multi-headed cross-attention module
+ if self.src_attn is not None:
+ residual = x
+ if self.normalize_before:
+ x = self.norm_mha2(x)
+ x_att, _ = self.src_attn(x, memory, memory, memory_mask)
+ x = residual + self.dropout(x_att)
+ if not self.normalize_before:
+ x = self.norm_mha2(x)
+
+ # convolution module
+ # Fake new cnn cache here, and then change it in conv_module
+ new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+ if self.conv_module is not None:
+ residual = x
+ if self.normalize_before:
+ x = self.norm_conv(x)
+ x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+ x = residual + self.dropout(x)
+
+ if not self.normalize_before:
+ x = self.norm_conv(x)
+
+ # feed forward module
+ residual = x
+ if self.normalize_before:
+ x = self.norm_ff(x)
+
+ x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+ if not self.normalize_before:
+ x = self.norm_ff(x)
+
+ if self.conv_module is not None:
+ x = self.norm_final(x)
+
+ return x, mask, new_att_cache, new_cnn_cache
+
+
+class EspnetRelPositionalEncoding(torch.nn.Module):
+ """Relative positional encoding module (new implementation).
+
+ Details can be found in https://github.com/espnet/espnet/pull/2816.
+
+ See : Appendix B in https://arxiv.org/abs/1901.02860
+
+ Args:
+ d_model (int): Embedding dimension.
+ dropout_rate (float): Dropout rate.
+ max_len (int): Maximum input length.
+
+ """
+
+ def __init__(self, d_model, dropout_rate, max_len=5000):
+ """Construct an PositionalEncoding object."""
+ super(EspnetRelPositionalEncoding, self).__init__()
+ self.d_model = d_model
+ self.xscale = math.sqrt(self.d_model)
+ self.dropout = torch.nn.Dropout(p=dropout_rate)
+ self.pe = None
+ self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+
+ def extend_pe(self, x):
+ """Reset the positional encodings."""
+ if self.pe is not None:
+ # self.pe contains both positive and negative parts
+ # the length of self.pe is 2 * input_len - 1
+ if self.pe.size(1) >= x.size(1) * 2 - 1:
+ if self.pe.dtype != x.dtype or self.pe.device != x.device:
+ self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+ return
+ # Suppose `i` means to the position of query vecotr and `j` means the
+ # position of key vector. We use position relative positions when keys
+ # are to the left (i>j) and negative relative positions otherwise (i torch.Tensor:
+ """ For getting encoding in a streaming fashion
+
+ Attention!!!!!
+ we apply dropout only once at the whole utterance level in a none
+ streaming way, but will call this function several times with
+ increasing input size in a streaming scenario, so the dropout will
+ be applied several times.
+
+ Args:
+ offset (int or torch.tensor): start offset
+ size (int): required size of position encoding
+
+ Returns:
+ torch.Tensor: Corresponding encoding
+ """
+ pos_emb = self.pe[
+ :,
+ self.pe.size(1) // 2 - size + 1 : self.pe.size(1) // 2 + size,
+ ]
+ return pos_emb
+
+
+class LinearNoSubsampling(torch.nn.Module):
+ """Linear transform the input without subsampling
+
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an linear object."""
+ super().__init__()
+ self.out = torch.nn.Sequential(
+ torch.nn.Linear(idim, odim),
+ torch.nn.LayerNorm(odim, eps=1e-5),
+ torch.nn.Dropout(dropout_rate),
+ )
+ self.pos_enc = pos_enc_class
+ self.right_context = 0
+ self.subsampling_rate = 1
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: tp.Union[int, torch.Tensor] = 0
+ ) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Input x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: linear input tensor (#batch, time', odim),
+ where time' = time .
+ torch.Tensor: linear input mask (#batch, 1, time'),
+ where time' = time .
+
+ """
+ x = self.out(x)
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask
+
+
+class ConformerDecoderV2(nn.Module):
+ def __init__(self,
+ input_size: int = 512,
+ output_size: int = 512,
+ attention_heads: int = 8,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.01,
+ srcattention_start_index: int = 0,
+ srcattention_end_index: int = 2,
+ attention_dropout_rate: float = 0.01,
+ positional_dropout_rate: float = 0.01,
+ key_bias: bool = True,
+ normalize_before: bool = True,
+ ):
+ super().__init__()
+ self.num_blocks = num_blocks
+ self.normalize_before = normalize_before
+ self.output_size = output_size
+
+ self.embed = LinearNoSubsampling(
+ input_size,
+ output_size,
+ dropout_rate,
+ EspnetRelPositionalEncoding(output_size, positional_dropout_rate),
+ )
+
+ self.encoders = torch.nn.ModuleList()
+ for i in range(self.num_blocks):
+ # construct src attention
+ if srcattention_start_index <= i <= srcattention_end_index:
+ srcattention_layer = MultiHeadedAttention(
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ key_bias
+ )
+ else:
+ srcattention_layer = None
+ # construct self attention
+ selfattention_layer = RelPositionMultiHeadedAttention(
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ key_bias
+ )
+ # construct ffn
+ ffn_layer = PositionwiseFeedForward(
+ output_size,
+ linear_units,
+ dropout_rate,
+ torch.nn.SiLU()
+ )
+ self.encoders.append(
+ ConformerDecoderLayer(
+ output_size,
+ selfattention_layer,
+ srcattention_layer,
+ ffn_layer,
+ None,
+ None,
+ dropout_rate,
+ normalize_before=normalize_before
+ )
+ )
+ self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
+
+ def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
+ memory: torch.Tensor, memory_masks: torch.Tensor,
+ pos_emb: torch.Tensor, mask_pad: torch.Tensor) -> torch.Tensor:
+ for layer in self.encoders:
+ xs, chunk_masks, _, _ = layer(xs, chunk_masks, memory, memory_masks, pos_emb, mask_pad)
+ return xs
+
+ def forward(self,
+ xs:torch.Tensor,
+ xs_lens:torch.Tensor,
+ memory:torch.Tensor,
+ memory_lens: torch.Tensor,
+ ):
+ T = xs.size(1)
+ masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
+ T2 = memory.size(1)
+ memory_masks = ~make_pad_mask(memory_lens, T2).unsqueeze(1) # (B, 1, T2)
+
+ xs, pos_emb, masks = self.embed(xs, masks)
+
+ xs = self.forward_layers(xs, masks, memory, memory_masks, pos_emb, masks)
+
+ if self.normalize_before:
+ xs = self.after_norm(xs)
+
+ return xs, masks
+
diff --git a/fireredtts/modules/flow/decoder.py b/fireredtts/modules/flow/decoder.py
new file mode 100644
index 0000000..6bf24da
--- /dev/null
+++ b/fireredtts/modules/flow/decoder.py
@@ -0,0 +1,396 @@
+import math
+from typing import Optional
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import pack, rearrange, repeat
+from diffusers.models.activations import get_activation
+
+from fireredtts.modules.flow.transformer import BasicTransformerBlock
+
+
+class SinusoidalPosEmb(torch.nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.dim = dim
+ assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even"
+
+ def forward(self, x, scale=1000):
+ if x.ndim < 1:
+ x = x.unsqueeze(0)
+ device = x.device
+ half_dim = self.dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+ emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+ return emb
+
+
+class Block1D(torch.nn.Module):
+ def __init__(self, dim, dim_out, groups=8):
+ super().__init__()
+ self.block = torch.nn.Sequential(
+ torch.nn.Conv1d(dim, dim_out, 3, padding=1),
+ torch.nn.GroupNorm(groups, dim_out),
+ nn.Mish(),
+ )
+
+ def forward(self, x, mask):
+ output = self.block(x * mask)
+ return output * mask
+
+
+class ResnetBlock1D(torch.nn.Module):
+ def __init__(self, dim, dim_out, time_emb_dim, groups=8):
+ super().__init__()
+ self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out))
+
+ self.block1 = Block1D(dim, dim_out, groups=groups)
+ self.block2 = Block1D(dim_out, dim_out, groups=groups)
+
+ self.res_conv = torch.nn.Conv1d(dim, dim_out, 1)
+
+ def forward(self, x, mask, time_emb):
+ h = self.block1(x, mask)
+ h += self.mlp(time_emb).unsqueeze(-1)
+ h = self.block2(h, mask)
+ output = h + self.res_conv(x * mask)
+ return output
+
+
+class Downsample1D(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1)
+
+ def forward(self, x):
+ return self.conv(x)
+
+
+class TimestepEmbedding(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ time_embed_dim: int,
+ act_fn: str = "silu",
+ out_dim: int = None,
+ post_act_fn: Optional[str] = None,
+ cond_proj_dim=None,
+ ):
+ super().__init__()
+
+ self.linear_1 = nn.Linear(in_channels, time_embed_dim)
+
+ if cond_proj_dim is not None:
+ self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
+ else:
+ self.cond_proj = None
+
+ self.act = get_activation(act_fn)
+
+ if out_dim is not None:
+ time_embed_dim_out = out_dim
+ else:
+ time_embed_dim_out = time_embed_dim
+ self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
+
+ if post_act_fn is None:
+ self.post_act = None
+ else:
+ self.post_act = get_activation(post_act_fn)
+
+ def forward(self, sample, condition=None):
+ if condition is not None:
+ sample = sample + self.cond_proj(condition)
+ sample = self.linear_1(sample)
+
+ if self.act is not None:
+ sample = self.act(sample)
+
+ sample = self.linear_2(sample)
+
+ if self.post_act is not None:
+ sample = self.post_act(sample)
+ return sample
+
+
+class Upsample1D(nn.Module):
+ """A 1D upsampling layer with an optional convolution.
+
+ Parameters:
+ channels (`int`):
+ number of channels in the inputs and outputs.
+ use_conv (`bool`, default `False`):
+ option to use a convolution.
+ use_conv_transpose (`bool`, default `False`):
+ option to use a convolution transpose.
+ out_channels (`int`, optional):
+ number of output channels. Defaults to `channels`.
+ """
+
+ def __init__(self, channels, use_conv=False, use_conv_transpose=True, 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
+
+ self.conv = None
+ if use_conv_transpose:
+ self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
+ elif use_conv:
+ self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
+
+ def forward(self, inputs):
+ assert inputs.shape[1] == self.channels
+ if self.use_conv_transpose:
+ return self.conv(inputs)
+
+ outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
+
+ if self.use_conv:
+ outputs = self.conv(outputs)
+
+ return outputs
+
+
+class ConditionalDecoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ channels=(256, 256),
+ dropout=0.0,
+ attention_head_dim=64,
+ n_blocks=4,
+ num_mid_blocks=12,
+ num_heads=8,
+ act_fn="gelu",
+ ):
+ """
+ This decoder requires an input with the same shape of the target. So, if your text content
+ is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+ """
+ super().__init__()
+ channels = tuple(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.time_embeddings = SinusoidalPosEmb(in_channels)
+ time_embed_dim = channels[0] * 4
+ self.time_mlp = TimestepEmbedding(
+ in_channels=in_channels,
+ time_embed_dim=time_embed_dim,
+ act_fn="silu",
+ )
+ self.down_blocks = nn.ModuleList([])
+ self.mid_blocks = nn.ModuleList([])
+ self.up_blocks = nn.ModuleList([])
+
+ output_channel = in_channels
+ for i in range(len(channels)): # pylint: disable=consider-using-enumerate
+ input_channel = output_channel
+ output_channel = channels[i]
+ is_last = i == len(channels) - 1
+ resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+ downsample = (
+ Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+ )
+ self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+ for i in range(num_mid_blocks):
+ input_channel = channels[-1]
+ out_channels = channels[-1]
+ resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+
+ self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+ channels = channels[::-1] + (channels[0],)
+ for i in range(len(channels) - 1):
+ input_channel = channels[i] * 2
+ output_channel = channels[i + 1]
+ is_last = i == len(channels) - 2
+ resnet = ResnetBlock1D(
+ dim=input_channel,
+ dim_out=output_channel,
+ time_emb_dim=time_embed_dim,
+ )
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+ upsample = (
+ Upsample1D(output_channel, use_conv_transpose=True)
+ if not is_last
+ else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+ )
+ self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+ self.final_block = Block1D(channels[-1], channels[-1])
+ self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+ self.initialize_weights()
+
+
+ def initialize_weights(self):
+ for m in self.modules():
+ if isinstance(m, nn.Conv1d):
+ nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.GroupNorm):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.Linear):
+ nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+
+ def forward(self, x, mask, mu, t):
+ """Forward pass of the UNet1DConditional model.
+
+ Args:
+ x (torch.Tensor): shape (batch_size, in_channels, time)
+ mask (_type_): shape (batch_size, 1, time)
+ t (_type_): shape (batch_size)
+ spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+ cond (_type_, optional): placeholder for future use. Defaults to None.
+
+ Raises:
+ ValueError: _description_
+ ValueError: _description_
+
+ Returns:
+ _type_: _description_
+ """
+
+ t = self.time_embeddings(t)
+ t = self.time_mlp(t)
+
+ x = pack([x, mu], "b * t")[0]
+
+ hiddens = []
+ masks = [mask]
+ for resnet, transformer_blocks, downsample in self.down_blocks:
+ mask_down = masks[-1]
+ x = resnet(x, mask_down, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ attn_mask = torch.matmul(mask_down.transpose(1, 2).contiguous(), mask_down)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+ hiddens.append(x) # Save hidden states for skip connections
+ x = downsample(x * mask_down)
+ masks.append(mask_down[:, :, ::2])
+ masks = masks[:-1]
+ mask_mid = masks[-1]
+
+ for resnet, transformer_blocks in self.mid_blocks:
+ x = resnet(x, mask_mid, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ attn_mask = torch.matmul(mask_mid.transpose(1, 2).contiguous(), mask_mid)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+
+ for resnet, transformer_blocks, upsample in self.up_blocks:
+ mask_up = masks.pop()
+ skip = hiddens.pop()
+ x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+ x = resnet(x, mask_up, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ attn_mask = torch.matmul(mask_up.transpose(1, 2).contiguous(), mask_up)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+ x = upsample(x * mask_up)
+ x = self.final_block(x, mask_up)
+ output = self.final_proj(x * mask_up)
+ return output * mask
+
+
+class ConditionalCFM(nn.Module):
+ def __init__(self,
+ estimator: nn.Module,
+ t_scheduler: str = "cosine",
+ inference_cfg_rate: float = 0.7,
+ ):
+ super().__init__()
+ self.estimator = estimator
+ self.t_scheduler = t_scheduler
+ self.inference_cfg_rate = inference_cfg_rate
+
+ def solve_euler(self, x, t_span, mu, mask):
+ t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+
+ # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+ # Or in future might add like a return_all_steps flag
+ sol = []
+
+ for step in range(1, len(t_span)):
+ dphi_dt = self.estimator(x, mask, mu, t)
+ # Classifier-Free Guidance inference introduced in VoiceBox
+ if self.inference_cfg_rate > 0:
+ cfg_dphi_dt = self.estimator(x, mask, torch.zeros_like(mu), t)
+ dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt -
+ self.inference_cfg_rate * cfg_dphi_dt)
+ x = x + dt * dphi_dt
+ t = t + dt
+ sol.append(x)
+ if step < len(t_span) - 1:
+ dt = t_span[step + 1] - t
+
+ return sol[-1]
+
+ def inference(self, mu, mask, n_timesteps, temperature: float=1.0):
+ z = torch.randn_like(mu) * temperature
+ t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
+ if self.t_scheduler == 'cosine':
+ t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+ return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask)
+
diff --git a/fireredtts/modules/flow/flow_model.py b/fireredtts/modules/flow/flow_model.py
new file mode 100644
index 0000000..fd3dcd8
--- /dev/null
+++ b/fireredtts/modules/flow/flow_model.py
@@ -0,0 +1,89 @@
+import time
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from fireredtts.modules.flow.utils import make_pad_mask
+
+
+class InterpolateRegulator(nn.Module):
+ def __init__(
+ self,
+ channels: int = 512,
+ num_blocks: int = 4,
+ groups: int = 1,
+ ):
+ super().__init__()
+ model = []
+ for _ in range(num_blocks):
+ model.extend([
+ nn.Conv1d(channels, channels, 3, 1, 1),
+ nn.GroupNorm(groups, channels),
+ nn.Mish(),
+ ])
+ model.append(
+ nn.Conv1d(channels, channels, 1, 1)
+ )
+ self.model = nn.Sequential(*model)
+
+ def forward(self, x, ylens=None):
+ # x in (B, T, D)
+ mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1)
+ x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
+ out = self.model(x).transpose(1, 2).contiguous()
+ olens = ylens
+ return out * mask, olens
+
+
+class CrossAttnFlowMatching(nn.Module):
+ def __init__(self,
+ output_size: int,
+ input_embedding: nn.Module,
+ encoder: nn.Module,
+ length_regulator: nn.Module,
+ mel_encoder: nn.Module,
+ decoder: nn.Module,
+ ):
+ super().__init__()
+ self.input_embedding = input_embedding
+ self.encoder = encoder
+ self.length_regulator = length_regulator
+ self.encoder_proj = torch.nn.Linear(self.encoder.output_size, output_size)
+ self.prompt_prenet = mel_encoder
+ self.decoder = decoder
+
+ def inference(self,
+ token: torch.Tensor,
+ token_len: torch.Tensor,
+ prompt_mel: torch.Tensor,
+ prompt_mel_len: torch.Tensor,
+ n_timesteps:int=10,
+ ):
+ # prompt projection
+ prompt_feat = self.prompt_prenet(prompt_mel)
+ prompt_feat_len = torch.ceil(prompt_mel_len/self.prompt_prenet.reduction_rate).long()
+
+ # concat text and prompt_text
+ mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(token_len.device)
+ token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+ # 40ms shift to 10ms shift
+ feat_len = (token_len *4).int()
+
+ # first encoder
+ h, _ = self.encoder(token, token_len, prompt_feat, prompt_feat_len)
+ # length regulate
+ h, _ = self.length_regulator(h, feat_len)
+ # final projection
+ h = self.encoder_proj(h)
+
+ mask = (~make_pad_mask(feat_len)).to(h)
+
+ feat = self.decoder.inference(
+ mu=h.transpose(1, 2).contiguous(),
+ mask=mask.unsqueeze(1),
+ n_timesteps=n_timesteps,
+ )
+ return feat
+
+
diff --git a/fireredtts/modules/flow/mel_encoder.py b/fireredtts/modules/flow/mel_encoder.py
new file mode 100644
index 0000000..dbbe2b6
--- /dev/null
+++ b/fireredtts/modules/flow/mel_encoder.py
@@ -0,0 +1,170 @@
+import typing as tp
+import torch
+import torch.nn as nn
+
+
+class ConvLayer(nn.Module):
+ def __init__(self,
+ in_channels:int,
+ out_channels:int,
+ kernel_size:int,
+ stride:int,
+ activation:str="GELU",
+ dropout_rate:float=0.0,
+ ):
+ super().__init__()
+ self.conv = nn.Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=(kernel_size-stride)//2,
+ )
+ self.drop = nn.Dropout(dropout_rate)
+ self.norm = nn.LayerNorm(out_channels)
+ self.activ = getattr(nn, activation)()
+
+ def forward(self, x:torch.Tensor):
+ """
+ Args:
+ x: (b, t, c)
+ Return:
+ x: (b, t, c)
+ """
+ x = x.transpose(2, 1)
+ x = self.conv(x)
+ x = x.transpose(2, 1)
+ x = self.drop(x)
+ x = self.norm(x)
+ x = self.activ(x)
+ return x
+
+
+class ResidualConvLayer(nn.Module):
+ def __init__(self,
+ hidden_channels:int,
+ n_layers:int=2,
+ kernel_size:int=5,
+ activation:str="GELU",
+ dropout_rate:float=0.0,
+ ):
+ super().__init__()
+ layers = [
+ ConvLayer(hidden_channels, hidden_channels, kernel_size, 1, activation, dropout_rate)
+ for _ in range(n_layers)
+ ]
+ self.layers = nn.Sequential(*layers)
+
+ def forward(self, x:torch.Tensor):
+ """
+ Args:
+ x: (b, t, c)
+ Returns:
+ x: (b, t, c)
+ """
+ return x + self.layers(x)
+
+
+class ResidualConvBlock(nn.Module):
+ def __init__(self,
+ in_channels:int,
+ hidden_channels:int,
+ out_channels:int,
+ n_layers:int=2,
+ n_blocks:int=5,
+ middle_layer:tp.Optional[nn.Module]=None,
+ kernel_size:int=5,
+ activation:str="GELU",
+ dropout_rate:float=0.0,
+ ):
+ super().__init__()
+ self.in_proj = nn.Conv1d(
+ in_channels,
+ hidden_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ padding=(kernel_size-1)//2,
+ ) if in_channels != hidden_channels else nn.Identity()
+
+ self.conv1 = nn.Sequential(*[
+ ResidualConvLayer(hidden_channels, n_layers, kernel_size, activation, dropout_rate)
+ for _ in range(n_blocks)
+ ])
+
+ if middle_layer is None:
+ self.middle_layer = nn.Identity()
+ elif isinstance(middle_layer, nn.Module):
+ self.middle_layer = middle_layer
+ else:
+ raise TypeError("unknown middle layer type:{}".format(type(middle_layer)))
+
+ self.conv2 = nn.Sequential(*[
+ ResidualConvLayer(hidden_channels, n_layers, kernel_size, activation, dropout_rate)
+ for _ in range(n_blocks)
+ ])
+
+ self.out_proj = nn.Conv1d(
+ hidden_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ padding=(kernel_size-1)//2,
+ ) if out_channels != hidden_channels else nn.Identity()
+
+ def forward(self, x:torch.Tensor, **middle_layer_kwargs):
+ """
+ Args:
+ x: (b, t1, c)
+ Return:
+ x: (b, t2, c)
+ """
+ x = self.in_proj(x.transpose(2, 1)).transpose(2, 1)
+ x = self.conv1(x)
+ if isinstance(self.middle_layer, nn.MaxPool1d) or isinstance(self.middle_layer, nn.Conv1d):
+ x = self.middle_layer(x.transpose(2, 1)).transpose(2, 1)
+ elif isinstance(self.middle_layer, nn.Identity):
+ x = self.middle_layer(x)
+ else:
+ # incase of phoneme-pooling layer
+ x = self.middle_layer(x, **middle_layer_kwargs)
+ x = self.conv2(x)
+ x = self.out_proj(x.transpose(2, 1)).transpose(2, 1)
+ return x
+
+
+class MelReduceEncoder(nn.Module):
+ def __init__(self,
+ in_channels:int,
+ out_channels:int,
+ hidden_channels:int=384,
+ reduction_rate:int=4,
+ n_layers:int=2,
+ n_blocks:int=5,
+ kernel_size:int=3,
+ activation:str="GELU",
+ dropout:float=0.0,
+ ):
+ super().__init__()
+ self.reduction_rate = reduction_rate
+ middle_conv = nn.Conv1d(
+ in_channels=hidden_channels,
+ out_channels=hidden_channels,
+ kernel_size=reduction_rate,
+ stride=reduction_rate,
+ padding=0
+ )
+ self.encoder = ResidualConvBlock(
+ in_channels=in_channels,
+ hidden_channels=hidden_channels,
+ out_channels=out_channels,
+ n_layers=n_layers,
+ n_blocks=n_blocks,
+ middle_layer=middle_conv,
+ kernel_size=kernel_size,
+ activation=activation,
+ dropout_rate=dropout
+ )
+
+ def forward(self, x:torch.Tensor):
+ return self.encoder(x)
+
diff --git a/fireredtts/modules/flow/mel_spectrogram.py b/fireredtts/modules/flow/mel_spectrogram.py
new file mode 100644
index 0000000..4b0fde8
--- /dev/null
+++ b/fireredtts/modules/flow/mel_spectrogram.py
@@ -0,0 +1,132 @@
+from functools import partial
+import torch
+import numpy as np
+import librosa
+from librosa.filters import mel as librosa_mel_fn
+from torchaudio.functional import resample as ta_resample_fn
+
+MAX_WAV_VALUE = 32767.0 # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+ return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+ return np.exp(x) / C
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+ return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+ return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+ output = dynamic_range_compression_torch(magnitudes)
+ return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+ output = dynamic_range_decompression_torch(magnitudes)
+ return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def mel_spectrogram(
+ y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
+):
+ global mel_basis, hann_window
+ if fmax not in mel_basis:
+ mel = librosa_mel_fn(
+ sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
+ )
+ str_key_mel_basis = str(fmax) + "_" + str(y.device)
+ mel_basis[str_key_mel_basis] = torch.from_numpy(mel).float().to(y.device)
+ hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
+
+ y = torch.nn.functional.pad(
+ y.unsqueeze(1),
+ (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+ mode="reflect",
+ )
+ y = y.squeeze(1)
+
+ # complex tensor as default, then use view_as_real for future pytorch compatibility
+ spec = torch.stft(
+ y,
+ n_fft,
+ hop_length=hop_size,
+ win_length=win_size,
+ window=hann_window[str(y.device)],
+ center=center,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=True,
+ )
+ spec = torch.view_as_real(spec)
+ spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+
+ spec = torch.matmul(mel_basis[str_key_mel_basis], spec)
+ spec = spectral_normalize_torch(spec)
+
+ return spec
+
+
+kaiser_best_resampling_fn = partial(
+ ta_resample_fn,
+ resampling_method="sinc_interp_kaiser", # DO NOT CHANGE!
+ rolloff=0.917347, # DO NOT CHANGE!
+ beta=12.9846, # DO NOT CHANGE!
+ lowpass_filter_width=50, # DO NOT CHANGE!
+)
+
+
+class MelSpectrogramExtractor(object):
+ def __init__(
+ self,
+ n_fft=1024,
+ win_size=1024,
+ num_mels=100,
+ hop_size=160,
+ sampling_rate=16000,
+ fmin=0,
+ fmax=None,
+ ):
+ self.n_fft = n_fft
+ self.win_size = win_size
+ self.num_mels = num_mels
+ self.hop_size = hop_size
+ self.sampling_rate = sampling_rate
+ self.fmin = fmin
+ self.fmax = fmax
+
+ def __call__(self, wav_path) -> np.ndarray:
+ wav_data, wav_sr = librosa.load(wav_path, sr=None, mono=True)
+ wav_data = torch.from_numpy(wav_data.copy()).unsqueeze(0)
+ # for 16k wavs, up-downsample to reduce artifects
+ if wav_sr == self.sampling_rate:
+ wav_data = kaiser_best_resampling_fn(wav_data, orig_freq=wav_sr, new_freq=24000)
+ wav_data = kaiser_best_resampling_fn(wav_data, orig_freq=24000, new_freq=self.sampling_rate)
+ else:
+ wav_data = kaiser_best_resampling_fn(wav_data, orig_freq=wav_sr, new_freq=self.sampling_rate)
+
+ # (1, num_mels, t)
+ mel = mel_spectrogram(
+ wav_data,
+ self.n_fft,
+ self.num_mels,
+ self.sampling_rate,
+ self.hop_size,
+ self.win_size,
+ self.fmin,
+ self.fmax,
+ )
+ mel = mel.squeeze(0).transpose(1, 0)
+ return mel # (t, num_mels)
diff --git a/fireredtts/modules/flow/transformer.py b/fireredtts/modules/flow/transformer.py
new file mode 100644
index 0000000..aefadda
--- /dev/null
+++ b/fireredtts/modules/flow/transformer.py
@@ -0,0 +1,249 @@
+from typing import Any, Dict, Optional
+
+import torch
+import torch.nn as nn
+from diffusers.models.attention import (
+ GEGLU,
+ GELU,
+ AdaLayerNorm,
+ AdaLayerNormZero,
+ ApproximateGELU,
+)
+from diffusers.models.attention_processor import Attention
+# from diffusers.models.lora import LoRACompatibleLinear
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+
+
+class FeedForward(nn.Module):
+ r"""
+ A feed-forward layer.
+
+ Parameters:
+ dim (`int`): The number of channels in the input.
+ dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+ mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+ activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+ final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+ """
+
+ def __init__(
+ self,
+ dim: int,
+ dim_out: Optional[int] = None,
+ mult: int = 4,
+ dropout: float = 0.0,
+ activation_fn: str = "geglu",
+ final_dropout: bool = False,
+ ):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = dim_out if dim_out is not None else dim
+
+ if activation_fn == "gelu":
+ act_fn = GELU(dim, inner_dim)
+ if activation_fn == "gelu-approximate":
+ act_fn = GELU(dim, inner_dim, approximate="tanh")
+ elif activation_fn == "geglu":
+ act_fn = GEGLU(dim, inner_dim)
+ elif activation_fn == "geglu-approximate":
+ act_fn = ApproximateGELU(dim, inner_dim)
+
+ self.net = nn.ModuleList([])
+ # project in
+ self.net.append(act_fn)
+ # project dropout
+ self.net.append(nn.Dropout(dropout))
+ # project out
+ self.net.append(nn.Linear(inner_dim, dim_out))
+ # self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
+ # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+ if final_dropout:
+ self.net.append(nn.Dropout(dropout))
+
+ def forward(self, hidden_states):
+ for module in self.net:
+ hidden_states = module(hidden_states)
+ return hidden_states
+
+
+@maybe_allow_in_graph
+class BasicTransformerBlock(nn.Module):
+ r"""
+ A basic Transformer block.
+
+ Parameters:
+ dim (`int`): The number of channels in the input and output.
+ num_attention_heads (`int`): The number of heads to use for multi-head attention.
+ attention_head_dim (`int`): The number of channels in each head.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+ cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+ only_cross_attention (`bool`, *optional*):
+ Whether to use only cross-attention layers. In this case two cross attention layers are used.
+ double_self_attention (`bool`, *optional*):
+ Whether to use two self-attention layers. In this case no cross attention layers are used.
+ activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+ num_embeds_ada_norm (:
+ obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+ attention_bias (:
+ obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+ """
+
+ 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,
+ double_self_attention: bool = False,
+ upcast_attention: bool = False,
+ norm_elementwise_affine: bool = True,
+ norm_type: str = "layer_norm",
+ final_dropout: bool = False,
+ ):
+ super().__init__()
+ self.only_cross_attention = only_cross_attention
+
+ self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
+ self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
+
+ if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+ raise ValueError(
+ f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+ f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+ )
+
+ # Define 3 blocks. Each block has its own normalization layer.
+ # 1. Self-Attn
+ if self.use_ada_layer_norm:
+ self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+ elif self.use_ada_layer_norm_zero:
+ self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+ else:
+ self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+ self.attn1 = Attention(
+ query_dim=dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+ upcast_attention=upcast_attention,
+ )
+
+ # 2. Cross-Attn
+ if cross_attention_dim is not None or double_self_attention:
+ # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+ # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+ # the second cross attention block.
+ self.norm2 = (
+ AdaLayerNorm(dim, num_embeds_ada_norm)
+ if self.use_ada_layer_norm
+ else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+ )
+ self.attn2 = Attention(
+ query_dim=dim,
+ cross_attention_dim=cross_attention_dim if not double_self_attention else None,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+ # scale_qk=False, # uncomment this to not to use flash attention
+ ) # is self-attn if encoder_hidden_states is none
+ else:
+ self.norm2 = None
+ self.attn2 = None
+
+ # 3. Feed-forward
+ self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+ self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
+
+ # let chunk size default to None
+ self._chunk_size = None
+ self._chunk_dim = 0
+
+ def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+ # Sets chunk feed-forward
+ self._chunk_size = chunk_size
+ self._chunk_dim = dim
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ cross_attention_kwargs: Dict[str, Any] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+ ):
+ # Notice that normalization is always applied before the real computation in the following blocks.
+ # 1. Self-Attention
+ if self.use_ada_layer_norm:
+ norm_hidden_states = self.norm1(hidden_states, timestep)
+ elif self.use_ada_layer_norm_zero:
+ norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+ hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+ )
+ else:
+ norm_hidden_states = self.norm1(hidden_states)
+
+ cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+
+ attn_output = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
+ **cross_attention_kwargs,
+ )
+ if self.use_ada_layer_norm_zero:
+ attn_output = gate_msa.unsqueeze(1) * attn_output
+ hidden_states = attn_output + hidden_states
+
+ # 2. Cross-Attention
+ if self.attn2 is not None:
+ norm_hidden_states = (
+ self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+ )
+
+ attn_output = self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ **cross_attention_kwargs,
+ )
+ hidden_states = attn_output + hidden_states
+
+ # 3. Feed-forward
+ norm_hidden_states = self.norm3(hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+ if self._chunk_size is not None:
+ # "feed_forward_chunk_size" can be used to save memory
+ if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+ raise ValueError(
+ f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+ )
+
+ num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+ ff_output = torch.cat(
+ [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
+ dim=self._chunk_dim,
+ )
+ else:
+ ff_output = self.ff(norm_hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+ hidden_states = ff_output + hidden_states
+
+ return hidden_states
diff --git a/fireredtts/modules/flow/utils.py b/fireredtts/modules/flow/utils.py
new file mode 100644
index 0000000..24665e7
--- /dev/null
+++ b/fireredtts/modules/flow/utils.py
@@ -0,0 +1,30 @@
+import torch
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+ """Make mask tensor containing indices of padded part.
+
+ See description of make_non_pad_mask.
+
+ Args:
+ lengths (torch.Tensor): Batch of lengths (B,).
+ Returns:
+ torch.Tensor: Mask tensor containing indices of padded part.
+
+ Examples:
+ >>> lengths = [5, 3, 2]
+ >>> make_pad_mask(lengths)
+ masks = [[0, 0, 0, 0 ,0],
+ [0, 0, 0, 1, 1],
+ [0, 0, 1, 1, 1]]
+ """
+ batch_size = lengths.size(0)
+ max_len = max_len if max_len > 0 else lengths.max().item()
+ seq_range = torch.arange(0,
+ max_len,
+ dtype=torch.int64,
+ device=lengths.device)
+ seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+ seq_length_expand = lengths.unsqueeze(-1)
+ mask = seq_range_expand >= seq_length_expand
+ return mask
\ No newline at end of file
diff --git a/fireredtts/modules/gpt/__init__.py b/fireredtts/modules/gpt/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fireredtts/modules/gpt/gpt.py b/fireredtts/modules/gpt/gpt.py
new file mode 100644
index 0000000..b0d49e5
--- /dev/null
+++ b/fireredtts/modules/gpt/gpt.py
@@ -0,0 +1,356 @@
+# ported from: https://github.com/neonbjb/tortoise-tts
+# ported from: https://github.com/coqui-ai/TTS/blob/dev/TTS/tts/layers/xtts/gpt.py
+
+import functools
+import math
+import random
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import GPT2Config, GPT2Model, GPT2PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+
+
+class GPT2InferenceModel(GPT2PreTrainedModel):
+ """Override GPT2LMHeadModel to allow for prefix conditioning."""
+
+ def __init__(self, config, gpt, pos_emb, embeddings, norm, linear, kv_cache):
+ super().__init__(config)
+ self.transformer = gpt
+ self.pos_embedding = pos_emb
+ self.embeddings = embeddings
+ self.final_norm = norm
+ self.lm_head = nn.Sequential(norm, linear)
+ self.kv_cache = kv_cache
+
+ def store_prefix_emb(self, prefix_emb):
+ self.cached_prefix_emb = prefix_emb
+
+ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+ token_type_ids = kwargs.get("token_type_ids", None) # usually None
+ if not self.kv_cache:
+ past_key_values = None
+
+ # only last token for inputs_ids if past is defined in kwargs
+ if past_key_values is not None:
+ input_ids = input_ids[:, -1].unsqueeze(-1)
+ if token_type_ids is not None:
+ token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+ attention_mask = kwargs.get("attention_mask", None)
+ position_ids = kwargs.get("position_ids", None)
+
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values is not None:
+ position_ids = position_ids[:, -1].unsqueeze(-1)
+ else:
+ position_ids = None
+ return {
+ "input_ids": input_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "position_ids": position_ids,
+ "attention_mask": attention_mask,
+ "token_type_ids": token_type_ids,
+ }
+
+ def forward(
+ self,
+ input_ids=None,
+ past_key_values=None,
+ attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ labels=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ assert self.cached_prefix_emb is not None
+ assert inputs_embeds is None # Not supported by this inference model.
+ assert labels is None # Training not supported by this inference model.
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ # Create embedding
+ prefix_len = self.cached_prefix_emb.shape[1]
+ if input_ids.shape[1] != 1:
+ gen_inputs = input_ids[:, prefix_len:]
+ gen_emb = self.embeddings(gen_inputs)
+ gen_emb = gen_emb + self.pos_embedding(gen_emb)
+ if self.cached_prefix_emb.shape[0] != gen_emb.shape[0]:
+ prefix_emb = self.cached_prefix_emb.repeat_interleave(
+ gen_emb.shape[0] // self.cached_prefix_emb.shape[0], 0
+ )
+ else:
+ prefix_emb = self.cached_prefix_emb.to(gen_emb.dtype)
+ emb = torch.cat([prefix_emb, gen_emb], dim=1)
+ else:
+ emb = self.embeddings(input_ids)
+ emb = emb + self.pos_embedding.get_fixed_embedding(
+ attention_mask.shape[1] - (prefix_len + 1), attention_mask.device
+ )
+ transformer_outputs = self.transformer(
+ inputs_embeds=emb,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ lm_logits = self.lm_head(hidden_states)
+
+ if not return_dict:
+ return (lm_logits,) + transformer_outputs[1:]
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=None,
+ logits=lm_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ cross_attentions=transformer_outputs.cross_attentions,
+ )
+
+ @staticmethod
+ def _reorder_cache(past, beam_idx):
+ """
+ This function is used to re-order the :obj:`past_key_values` cache if
+ :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
+ called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
+ """
+ return tuple(
+ tuple(
+ past_state.index_select(0, beam_idx.to(past_state.device))
+ for past_state in layer_past
+ )
+ for layer_past in past
+ )
+
+
+def null_position_embeddings(range, dim):
+ return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
+
+
+class LearnedPositionEmbeddings(nn.Module):
+ def __init__(self, seq_len, model_dim, init=0.02):
+ super().__init__()
+ self.emb = torch.nn.Embedding(seq_len, model_dim)
+ # Initializing this way is standard for GPT-2
+ self.emb.weight.data.normal_(mean=0.0, std=init)
+
+ def forward(self, x):
+ sl = x.shape[1]
+ return self.emb(torch.arange(0, sl, device=x.device))
+
+ def get_fixed_embedding(self, ind, dev):
+ return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
+
+
+def build_hf_gpt_transformer(
+ layers,
+ model_dim,
+ heads,
+ max_mel_seq_len,
+ max_text_seq_len,
+ max_prompt_len,
+ checkpointing,
+):
+ """
+ GPT-2 implemented by the HuggingFace library.
+ """
+ gpt_config = GPT2Config(
+ vocab_size=256, # Unused.
+ n_positions=max_mel_seq_len + max_text_seq_len + max_prompt_len,
+ n_ctx=max_mel_seq_len + max_text_seq_len + max_prompt_len,
+ n_embd=model_dim,
+ n_layer=layers,
+ n_head=heads,
+ gradient_checkpointing=checkpointing,
+ use_cache=not checkpointing,
+ )
+ gpt = GPT2Model(gpt_config)
+ # Override the built in positional embeddings
+ del gpt.wpe
+ gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
+ # Built-in token embeddings are unused.
+ del gpt.wte
+
+ mel_pos_emb = (
+ LearnedPositionEmbeddings(max_mel_seq_len, model_dim)
+ if max_mel_seq_len != -1
+ else functools.partial(null_position_embeddings, dim=model_dim)
+ )
+ text_pos_emb = (
+ LearnedPositionEmbeddings(max_text_seq_len, model_dim)
+ if max_mel_seq_len != -1
+ else functools.partial(null_position_embeddings, dim=model_dim)
+ )
+ return gpt, mel_pos_emb, text_pos_emb, None, None
+
+
+class GPT(nn.Module):
+ def __init__(
+ self,
+ start_text_token=261,
+ stop_text_token=0,
+ layers=8,
+ model_dim=512,
+ heads=8,
+ max_text_tokens=120,
+ max_mel_tokens=250,
+ max_prompt_tokens=70,
+ max_conditioning_inputs=1,
+ code_stride_len=1024,
+ number_text_tokens=256,
+ num_audio_tokens=8194,
+ start_audio_token=8192,
+ stop_audio_token=8193,
+ checkpointing=False,
+ label_smoothing=0.0,
+ ):
+ """
+ Args:
+
+ """
+ super().__init__()
+
+ self.label_smoothing = label_smoothing
+ self.number_text_tokens = number_text_tokens
+ self.start_text_token = start_text_token
+ self.stop_text_token = stop_text_token
+ self.num_audio_tokens = num_audio_tokens
+ self.start_audio_token = start_audio_token
+ self.stop_audio_token = stop_audio_token
+ self.start_prompt_token = start_audio_token
+ self.stop_prompt_token = stop_audio_token
+ self.layers = layers
+ self.heads = heads
+ self.model_dim = model_dim
+ self.max_conditioning_inputs = max_conditioning_inputs
+ self.max_gen_mel_tokens = max_mel_tokens - self.max_conditioning_inputs - 2
+ self.max_mel_tokens = (
+ -1
+ if max_mel_tokens == -1
+ else max_mel_tokens + 2 + self.max_conditioning_inputs
+ )
+ self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens + 2
+ self.max_prompt_tokens = max_prompt_tokens
+ self.code_stride_len = code_stride_len
+ self.conditioning_dropout = nn.Dropout1d(0.1)
+ self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
+ self.mel_embedding = nn.Embedding(self.num_audio_tokens, model_dim)
+
+ (
+ self.gpt,
+ self.mel_pos_embedding,
+ self.text_pos_embedding,
+ self.mel_layer_pos_embedding,
+ self.text_layer_pos_embedding,
+ ) = build_hf_gpt_transformer(
+ layers,
+ model_dim,
+ heads,
+ self.max_mel_tokens,
+ self.max_text_tokens,
+ self.max_prompt_tokens,
+ checkpointing,
+ )
+
+ self.final_norm = nn.LayerNorm(model_dim)
+ self.text_head = nn.Linear(model_dim, self.number_text_tokens)
+ self.mel_head = nn.Linear(model_dim, self.num_audio_tokens)
+
+ # reference_embedding
+ self.reference_embedding = nn.Sequential(
+ nn.Linear(512, 256),
+ nn.Tanh(),
+ nn.Linear(256, self.model_dim),
+ )
+
+ def init_gpt_for_inference(self, kv_cache=True, use_deepspeed=False):
+ seq_length = (
+ self.max_prompt_tokens + self.max_mel_tokens + self.max_text_tokens + 1
+ )
+ gpt_config = GPT2Config(
+ vocab_size=self.max_mel_tokens,
+ n_positions=seq_length,
+ n_ctx=seq_length,
+ n_embd=self.model_dim,
+ n_layer=self.layers,
+ n_head=self.heads,
+ gradient_checkpointing=False,
+ use_cache=True,
+ )
+ self.gpt_inference = GPT2InferenceModel(
+ gpt_config,
+ self.gpt,
+ self.mel_pos_embedding,
+ self.mel_embedding,
+ self.final_norm,
+ self.mel_head,
+ kv_cache=kv_cache,
+ )
+ self.gpt.wte = self.mel_embedding
+
+ def inference(self, cond_latents, text_inputs, **hf_generate_kwargs):
+ self.compute_embeddings(cond_latents, text_inputs)
+ return self.generate(cond_latents, text_inputs, **hf_generate_kwargs)
+
+ def compute_embeddings(
+ self,
+ cond_latents,
+ text_inputs,
+ ):
+ text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
+ text_inputs = F.pad(text_inputs, (1, 0), value=self.start_text_token)
+ emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
+ emb = torch.cat([cond_latents, emb], dim=1)
+ self.gpt_inference.store_prefix_emb(emb)
+ gpt_inputs = torch.full(
+ (
+ emb.shape[0],
+ emb.shape[1] + 1, # +1 for the start_audio_token
+ ),
+ fill_value=1,
+ dtype=torch.long,
+ device=text_inputs.device,
+ )
+ gpt_inputs[:, -1] = self.start_audio_token
+ return gpt_inputs
+
+ def generate(
+ self,
+ cond_latents,
+ text_inputs,
+ **hf_generate_kwargs,
+ ):
+ gpt_inputs = self.compute_embeddings(cond_latents, text_inputs)
+ gen = self.gpt_inference.generate(
+ gpt_inputs,
+ bos_token_id=self.start_audio_token,
+ pad_token_id=self.stop_audio_token,
+ eos_token_id=self.stop_audio_token,
+ max_length=self.max_gen_mel_tokens + gpt_inputs.shape[-1],
+ **hf_generate_kwargs,
+ )
+ if "return_dict_in_generate" in hf_generate_kwargs:
+ return gen.sequences[:, gpt_inputs.shape[1] :], gen
+ return gen[:, gpt_inputs.shape[1] :]
diff --git a/fireredtts/modules/text_normalizer/__init__.py b/fireredtts/modules/text_normalizer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fireredtts/modules/text_normalizer/normalize.py b/fireredtts/modules/text_normalizer/normalize.py
new file mode 100644
index 0000000..9296d52
--- /dev/null
+++ b/fireredtts/modules/text_normalizer/normalize.py
@@ -0,0 +1,178 @@
+import re
+import regex
+import inflect
+import unicodedata
+from lingua import Language, LanguageDetectorBuilder
+from builtins import str as unicode
+
+from tn.chinese.normalizer import Normalizer as ZhNormalizer
+from tn.english.normalizer import Normalizer as EnNormalizer
+
+from fireredtts.modules.text_normalizer.regex_common import *
+from fireredtts.modules.text_normalizer.utils import *
+
+
+def preprocess_text(sentence):
+ # preprocessing
+ sentence = bytes(sentence, "utf-8").decode("utf-8", "ignore")
+ sentence = regex.sub("[\p{Cf}--[\u200d]]", "", sentence, flags=regex.V1)
+ sentence = regex.sub("\p{Co}", "", sentence)
+ sentence = sentence.replace("\u00a0", " ")
+ sentence = sentence.replace("\ufffd", "")
+ sentence = regex.sub("\p{Zl}", "\n", sentence)
+ sentence = regex.sub("\p{Zp}", "\n", sentence)
+
+ sentence = unicode(sentence)
+ sentence = "".join(
+ char
+ for char in unicodedata.normalize("NFD", sentence)
+ if unicodedata.category(char) != "Mn"
+ ) # Strip accents
+
+ sentence = strip_kaomoji(sentence)
+ # full to half with exemption (to be converted after number TN): 。，：
+ sentence = f2b(sentence, exemption="。，：")
+
+ # clean spaces
+ sentence = sentence.replace("\n", "，")
+ sentence = sentence.replace("\t", "，")
+ sentence = sentence.replace("\r", "，")
+ sentence = re.sub(r"[。.]{3,}", "…", sentence)
+ sentence = re.sub(r"[…⋯]{1,}", "…", sentence)
+ sentence = re.sub(r"[ ]+", " ", sentence)
+ sentence = sentence.strip()
+
+ # punctuation reduction
+ result = ""
+ for idx, char in enumerate(sentence):
+ if char in symbol_reduction:
+ char = symbol_reduction[char]
+
+ if char == " ":
+ if idx == 0:
+ continue
+ if is_chinese(sentence[idx + 1]) and (
+ is_chinese(sentence[idx - 1]) or sentence[idx - 1] in '") '
+ ):
+ result += "，"
+ else:
+ result += " "
+ continue
+
+ if is_valid_char(char):
+ result += char
+ result = re.sub(r"[ ]+", " ", result)
+ return result
+
+
+def rettt(sentence):
+ # handle abbreviations for all languages
+ sentence = sentence.replace("&nd", "and")
+ sentence = sentence.replace("Jan.", "january")
+ sentence = sentence.replace("Feb.", "febrary")
+ sentence = sentence.replace("Mar.", "march")
+ sentence = sentence.replace("Apr.", "april")
+ sentence = sentence.replace("May.", "may")
+ sentence = sentence.replace("Jun.", "june")
+ sentence = sentence.replace("Jul.", "july")
+ sentence = sentence.replace("Aug.", "august")
+ sentence = sentence.replace("Sept.", "september")
+ sentence = sentence.replace("Sep.", "september")
+ sentence = sentence.replace("Oct.", "october")
+ sentence = sentence.replace("Nov.", "november")
+ sentence = sentence.replace("Dec.", "december")
+ sentence = sentence.replace("Mon.", "monday")
+ sentence = sentence.replace("Tues.", "tuesday")
+ sentence = sentence.replace("Wed.", "wednesday")
+ sentence = sentence.replace("Thur.", "thursday")
+ sentence = sentence.replace("Fri.", "friday")
+ sentence = sentence.replace("Sat.", "saturday")
+ if sentence != "Sun.":
+ sentence = sentence.replace("Sun.", "sunday")
+ sentence = re.sub(r" St\. ([A-Z])", r" saint \1", sentence)
+ sentence = re.sub(r" St\.", " street", sentence)
+ sentence = re.sub(r" Rd\.", " road", sentence)
+ sentence = re.sub(r"[Aa]\.[Mm]\.", "A_M", sentence)
+ sentence = re.sub(r"[Pp]\.[Mm]\.", "P_M", sentence)
+ sentence = re.sub(r"[Bb]\.[Cc]\.", "B_C", sentence)
+ sentence = re.sub(r"[Ad]\.[Dd]\.", "A_D", sentence)
+ sentence = sentence.replace("Mr.", "mister")
+ sentence = sentence.replace("Ms.", "miss")
+ sentence = sentence.replace("Mrs.", "misses")
+ sentence = sentence.replace("Ph.D", "P_H_D")
+ sentence = sentence.replace("i.e.", "that is")
+ sentence = sentence.replace("e.g.", "for example")
+ sentence = sentence.replace("btw.", "by the way")
+ sentence = sentence.replace("btw", "by the way")
+ sentence = sentence.replace("b.t.w.", "by the way")
+ sentence = sentence.replace("@", " at ")
+ return sentence
+
+
+class TextNormalizer:
+ def __init__(self):
+ self.language_detector = LanguageDetectorBuilder.from_languages(
+ Language.ENGLISH, Language.CHINESE
+ ).build()
+ self.zh_normalizer = ZhNormalizer()
+ self.en_normalizer = EnNormalizer()
+ self.inflect_parser = inflect.engine()
+ self.lang2token = {Language.ENGLISH: "en", Language.CHINESE: "zh"}
+
+ def tn(self, text):
+ text = preprocess_text(text)
+ text = rettt(text) # regex replacements
+ # for non chinese languages
+ language = self.language_detector.detect_language_of(text)
+ # enforce chinese if text contains any chinese character
+ if contains_chinese(text):
+ language = Language.CHINESE
+ text_lang = self.lang2token.get(language, "zh")
+
+ if is_upper_eng_and_digit(text):
+ language = Language.CHINESE
+
+ if language == Language.CHINESE:
+ text = self.zh_normalizer.normalize(text)
+ text = text.replace("\n", "")
+ text = re.sub(r"[，,]+$", "。", text)
+ else:
+ text = re.sub(r"[^ 0-9A-Za-z\[\]'.,:?!_\-]", "", text)
+ text = self.en_normalizer.normalize(text)
+ # fallback number normalization
+ pieces = re.split(r"(\d+)", text)
+ text = "".join(
+ [
+ self.inflect_parser.number_to_words(p) if p.isnumeric() else p
+ for p in pieces
+ if len(p) > 0
+ ]
+ )
+
+ # cleanup
+ text = text.replace("_", " ")
+ text = re.sub(r"[ ]+", " ", text)
+
+ # spell caplital words
+ pieces = re.split(r"([A-Z]{2,4}|[ ])", text)
+ for idx, p in enumerate(pieces):
+ if re.match("[A-Z]{2,4}", p):
+ pieces[idx] = " ".join(p)
+ text = " ".join([p for p in pieces if p != " "])
+
+ # post TN full to half
+ text = text.replace("。", ".")
+ text = text.replace("，", ",")
+ text = text.replace("：", ":")
+
+ # model limitations
+ text = text.lower().strip()
+ text = text.replace('"', "")
+ text = text.replace("·", " ")
+ text = re.sub("[…~、！，？：；!?:;]+", ",", text)
+ text = re.sub("[,]+", ",", text)
+ text = re.sub(r"[,. ]+$", ".", text)
+ if len(text) > 0 and text[-1] != ".":
+ text = text + "."
+
+ return text, text_lang
diff --git a/fireredtts/modules/text_normalizer/regex_common.py b/fireredtts/modules/text_normalizer/regex_common.py
new file mode 100644
index 0000000..2396789
--- /dev/null
+++ b/fireredtts/modules/text_normalizer/regex_common.py
@@ -0,0 +1,23 @@
+import re
+
+kaomoji_regex = re.compile(
+ r"[oヽwΣ┗╰O︿Ψ凸]?[(|≡*（].{0,4}[Д✿_▽→≧﹏`∩⊙∇☆≡๑〃′エ≦▔＠﹁εヘ•́ω益‿≖ฺ皿•̀艹￣△|ﾟ].{0,5}[|≡*)）][┛ブ凸cｄd︴oOΨ︿w╯ノ]?"
+)
+chinese_regex = re.compile(r"[\u4e00-\u9fa5]")
+digit_regex = re.compile(r"(\\d+)(\\.\\d+)?", re.UNICODE)
+
+chinese_char_regex = re.compile(r"^[\u4e00-\u9fa5]$", re.UNICODE)
+eng_and_digit_char_regex = re.compile(r"^[0-9.,A-Za-z]+$", re.UNICODE)
+upper_eng_and_digit_regex = re.compile(r"^[ 0-9A-Z\"'.,:?!\-]+$", re.UNICODE)
+valid_char_regex = re.compile(
+ r"[\t\r\n ]|"
+ r"[\u4e00-\u9fa5]|"
+ r"\u0080|[\u20a0-\u20bf]|\u00a2|\u00a3|\u00a5|\uffe0|\uffe1|\uffe5|\uffe6|"
+ r"\u3000|\u3002|\u00b7|\u2014|\u2019|\u2026|\uff01|\uff1f|\uff0e|\uff1a|\uff1b|\uff0b|\uff0c|\uff0d|\uff0f|[\ufe10-\ufe16]|[\ufe50-\ufe51]|[\ufe55-\ufe57]|\ufe6a|"
+ r"[\u0030-\u0039]|"
+ r"[\u0391-\u03c9]|"
+ r"[\u00b0-\u00b3]|[\u2015-\u2018]|[\u3000-\u303f]|"
+ r"[\u0022-\u002f\u003a-\u003e\u0040\u005b-\u0060\u007b-\u007e]|"
+ r"[\uff21-\uff3a]|[\uff41-\uff5a]|[\u0041-\u005a]|[\u0061-\u007a]",
+ re.UNICODE,
+)
diff --git a/fireredtts/modules/text_normalizer/utils.py b/fireredtts/modules/text_normalizer/utils.py
new file mode 100644
index 0000000..4bcfe72
--- /dev/null
+++ b/fireredtts/modules/text_normalizer/utils.py
@@ -0,0 +1,121 @@
+from fireredtts.modules.text_normalizer.regex_common import *
+
+
+def contains_chinese(text):
+ return bool(chinese_regex.search(text))
+
+
+def strip_kaomoji(text):
+ return kaomoji_regex.sub(" ", text)
+
+
+def is_chinese(char):
+ return chinese_char_regex.match(char)
+
+
+def is_eng_and_digit(char):
+ return eng_and_digit_char_regex.match(char)
+
+
+def is_upper_eng_and_digit(text):
+ return upper_eng_and_digit_regex.match(text)
+
+
+def is_valid_char(char):
+ return valid_char_regex.match(char)
+
+
+def is_digit(text):
+ return digit_regex.match(text)
+
+
+def contains_chinese(text):
+ return bool(chinese_regex.search(text))
+
+
+def f2b(ustr, exemption="。，："):
+ half = []
+ for u in ustr:
+ num = ord(u)
+ if num == 0x3000:
+ half.append(" ")
+ elif u in exemption: # exemption
+ half.append(u)
+ elif 0xFF01 <= num <= 0xFF5E:
+ num -= 0xFEE0
+ half.append(chr(num))
+ else:
+ half.append(u)
+ return "".join(half)
+
+
+symbol_reduction = {
+ "「": '"',
+ "」": '"',
+ "｀": '"',
+ "〝": '"',
+ "〞": '"',
+ "‟": '"',
+ "„": '"',
+ "｛": "(",
+ "｝": ")",
+ "【": "(",
+ "】": ")",
+ "〖": "(",
+ "〗": ")",
+ "〔": "(",
+ "〕": ")",
+ "〘": "(",
+ "〙": ")",
+ "《": "(",
+ "》": ")",
+ "｟": "(",
+ "｠": ")",
+ "〚": "(",
+ "〛": ")",
+ "『": '"',
+ "』": '"',
+ "｢": '"',
+ "｣": '"',
+ "{": "(",
+ "}": ")",
+ "〈": "(",
+ "〉": ")",
+ "•": "·",
+ "‧": "·",
+ "〰": "…",
+ "﹏": "…",
+ "〜": "~",
+ "～": "~",
+ "＋": "+",
+ "､": "、",
+ "｡": "。",
+ "︐": "，",
+ "﹐": "，",
+ "︑": "、",
+ "﹑": "、",
+ "︒": "。",
+ "︓": "：",
+ "﹕": "：",
+ "︔": "；",
+ "﹔": "；",
+ "︕": "！",
+ "﹗": "！",
+ "︖": "？",
+ "﹖": "？",
+ "﹙": "(",
+ "﹚": ")",
+ "﹪": "%",
+ "﹠": "&",
+ "＞": ">",
+ "|": "、",
+ "＝": "=",
+ "‐": "-",
+ "‑": "-",
+ "‒": "-",
+ "–": "-",
+ "—": "-",
+ "―": "-",
+ "％": "%",
+ "μ": "u",
+}
diff --git a/fireredtts/modules/tokenizer/__init__.py b/fireredtts/modules/tokenizer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fireredtts/modules/tokenizer/assets/multilingual.tiktoken b/fireredtts/modules/tokenizer/assets/multilingual.tiktoken
new file mode 100644
index 0000000..665c126
--- /dev/null
+++ b/fireredtts/modules/tokenizer/assets/multilingual.tiktoken
@@ -0,0 +1,57184 @@
+IQ== 0
+Ig== 1
+Iw== 2
+JA== 3
+JQ== 4
+Jg== 5
+Jw== 6
+KA== 7
+KQ== 8
+Kg== 9
+Kw== 10
+LA== 11
+LQ== 12
+Lg== 13
+Lw== 14
+MA== 15
+MQ== 16
+Mg== 17
+Mw== 18
+NA== 19
+NQ== 20
+Ng== 21
+Nw== 22
+OA== 23
+OQ== 24
+Og== 25
+Ow== 26
+PA== 27
+PQ== 28
+Pg== 29
+Pw== 30
+QA== 31
+QQ== 32
+Qg== 33
+Qw== 34
+RA== 35
+RQ== 36
+Rg== 37
+Rw== 38
+SA== 39
+SQ== 40
+Sg== 41
+Sw== 42
+TA== 43
+TQ== 44
+Tg== 45
+Tw== 46
+UA== 47
+UQ== 48
+Ug== 49
+Uw== 50
+VA== 51
+VQ== 52
+Vg== 53
+Vw== 54
+WA== 55
+WQ== 56
+Wg== 57
+Ww== 58
+XA== 59
+XQ== 60
+Xg== 61
+Xw== 62
+YA== 63
+YQ== 64
+Yg== 65
+Yw== 66
+ZA== 67
+ZQ== 68
+Zg== 69
+Zw== 70
+aA== 71
+aQ== 72
+ag== 73
+aw== 74
+bA== 75
+bQ== 76
+bg== 77
+bw== 78
+cA== 79
+cQ== 80
+cg== 81
+cw== 82
+dA== 83
+dQ== 84
+dg== 85
+dw== 86
+eA== 87
+eQ== 88
+eg== 89
+ew== 90
+fA== 91
+fQ== 92
+fg== 93
+oQ== 94
+og== 95
+ow== 96
+pA== 97
+pQ== 98
+pg== 99
+pw== 100
+qA== 101
+qQ== 102
+qg== 103
+qw== 104
+rA== 105
+rg== 106
+rw== 107
+sA== 108
+sQ== 109
+sg== 110
+sw== 111
+tA== 112
+tQ== 113
+tg== 114
+tw== 115
+uA== 116
+uQ== 117
+ug== 118
+uw== 119
+vA== 120
+vQ== 121
+vg== 122
+vw== 123
+wA== 124
+wQ== 125
+wg== 126
+ww== 127
+xA== 128
+xQ== 129
+xg== 130
+xw== 131
+yA== 132
+yQ== 133
+yg== 134
+yw== 135
+zA== 136
+zQ== 137
+zg== 138
+zw== 139
+0A== 140
+0Q== 141
+0g== 142
+0w== 143
+1A== 144
+1Q== 145
+1g== 146
+1w== 147
+2A== 148
+2Q== 149
+2g== 150
+2w== 151
+3A== 152
+3Q== 153
+3g== 154
+3w== 155
+4A== 156
+4Q== 157
+4g== 158
+4w== 159
+5A== 160
+5Q== 161
+5g== 162
+5w== 163
+6A== 164
+6Q== 165
+6g== 166
+6w== 167
+7A== 168
+7Q== 169
+7g== 170
+7w== 171
+8A== 172
+8Q== 173
+8g== 174
+8w== 175
+9A== 176
+9Q== 177
+9g== 178
+9w== 179
++A== 180
++Q== 181
++g== 182
++w== 183
+/A== 184
+/Q== 185
+/g== 186
+/w== 187
+AA== 188
+AQ== 189
+Ag== 190
+Aw== 191
+BA== 192
+BQ== 193
+Bg== 194
+Bw== 195
+CA== 196
+CQ== 197
+Cg== 198
+Cw== 199
+DA== 200
+DQ== 201
+Dg== 202
+Dw== 203
+EA== 204
+EQ== 205
+Eg== 206
+Ew== 207
+FA== 208
+FQ== 209
+Fg== 210
+Fw== 211
+GA== 212
+GQ== 213
+Gg== 214
+Gw== 215
+HA== 216
+HQ== 217
+Hg== 218
+Hw== 219
+IA== 220
+fw== 221
+gA== 222
+gQ== 223
+gg== 224
+gw== 225
+hA== 226
+hQ== 227
+hg== 228
+hw== 229
+iA== 230
+iQ== 231
+ig== 232
+iw== 233
+jA== 234
+jQ== 235
+jg== 236
+jw== 237
+kA== 238
+kQ== 239
+kg== 240
+kw== 241
+lA== 242
+lQ== 243
+lg== 244
+lw== 245
+mA== 246
+mQ== 247
+mg== 248
+mw== 249
+nA== 250
+nQ== 251
+ng== 252
+nw== 253
+oA== 254
+rQ== 255
+IHQ= 256
+IGE= 257
+IHRo 258
+aW4= 259
+ZXI= 260
+IHc= 261
+IHM= 262
+b3U= 263
+IHRoZQ== 264
+cmU= 265
+b24= 266
+YXQ= 267
+ZW4= 268
+IGM= 269
+aXQ= 270
+aXM= 271
+IGI= 272
+bmQ= 273
+IGQ= 274
+IG0= 275
+IGg= 276
+IG8= 277
+aW5n 278
+ZXM= 279
+IHA= 280
+IHRv 281
+YW4= 282
+IGY= 283
+b3I= 284
+bGw= 285
+IEk= 286
+IGw= 287
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+6ZuS 56633
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+6Zug 56635
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+6Zyc 56659
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+6Zyy 56665
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+6Zy5 56667
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+6Z2Z 56672
+6Z2b 56673
+6Z2g 56674
+6Z2h 56675
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+6Z2x 56678
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+6Z20 56680
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+6aKA 56732
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+6b6K 57174
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+6b6M 57176
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+6b6a 57178
+6b6b 57179
+6b6f 57180
+6b6g 57181
+6b6i 57182
+= 57183
\ No newline at end of file
diff --git a/fireredtts/modules/tokenizer/tokenizer.py b/fireredtts/modules/tokenizer/tokenizer.py
new file mode 100644
index 0000000..a164e38
--- /dev/null
+++ b/fireredtts/modules/tokenizer/tokenizer.py
@@ -0,0 +1,46 @@
+import os
+import torch
+from fireredtts.modules.tokenizer.whisper_tokenizer import get_tokenizer
+from fireredtts.modules.text_normalizer.normalize import TextNormalizer
+
+
+DEFAULT_VOCAB_FILE = os.path.join(
+ os.path.dirname(os.path.realpath(__file__)), "../data/tokenizer.json"
+)
+
+
+class VoiceBpeTokenizer:
+ def __init__(self):
+ self.tokenizer = get_tokenizer(multilingual=True)
+ self.tn_engine = TextNormalizer()
+
+ def redtts_text_cleaner(self, text):
+ text = text.strip()
+ text, text_lang = self.tn_engine.tn(text)
+ # print("---text after tn:", text)
+ return text, text_lang
+
+ def encode(self, text, lang="auto"):
+ text, text_lang = self.redtts_text_cleaner(text=text)
+ if lang == "auto":
+ lang = text_lang
+ text = f"[{lang}]{text}"
+ return self.tokenizer.encode(text)
+
+ def decode(self, seq):
+ if isinstance(seq, torch.Tensor):
+ seq = seq.cpu().numpy()
+ text = self.tokenizer.decode(seq)
+ return text
+
+ def __len__(self):
+ return self.tokenizer.get_vocab_size()
+
+ def get_number_tokens(self):
+ return self.tokenizer.get_vocab_size()
+
+
+if __name__ == "__main__":
+ tok = VoiceBpeTokenizer()
+ codes = tok.encode("我、真是hello USA啊？谢谢你world！")
+ print([tok.decode([c]) for c in codes])
diff --git a/fireredtts/modules/tokenizer/whisper_tokenizer.py b/fireredtts/modules/tokenizer/whisper_tokenizer.py
new file mode 100644
index 0000000..98f2780
--- /dev/null
+++ b/fireredtts/modules/tokenizer/whisper_tokenizer.py
@@ -0,0 +1,456 @@
+# adapted from https://github.com/openai/whisper/blob/main/whisper/tokenizer.py
+# Copyright (c) 2022 OpenAI
+
+# MIT License for this file
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import base64
+import os
+import string
+from dataclasses import dataclass, field
+from functools import cached_property, lru_cache
+from typing import Dict, List, Optional, Tuple
+
+import tiktoken
+
+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",
+ "he": "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",
+ "yue": "cantonese",
+}
+
+# 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",
+ "mandarin": "zh",
+}
+
+
+@dataclass
+class Tokenizer:
+ """A thin wrapper around `tiktoken` providing quick access to special tokens"""
+
+ encoding: tiktoken.Encoding
+ num_languages: int
+ language: Optional[str] = None
+ task: Optional[str] = None
+ sot_sequence: Tuple[int] = ()
+ special_tokens: Dict[str, int] = field(default_factory=dict)
+
+ def __post_init__(self):
+ for special in self.encoding.special_tokens_set:
+ special_token = self.encoding.encode_single_token(special)
+ self.special_tokens[special] = special_token
+
+ sot: int = self.special_tokens["[startoftranscript]"]
+ translate: int = self.special_tokens["[translate]"]
+ transcribe: int = self.special_tokens["[transcribe]"]
+
+ langs = tuple(LANGUAGES.keys())[: self.num_languages]
+ sot_sequence = [sot]
+ if self.language is not None:
+ sot_sequence.append(sot + 1 + langs.index(self.language))
+ if self.task is not None:
+ task_token: int = transcribe if self.task == "transcribe" else translate
+ sot_sequence.append(task_token)
+
+ self.sot_sequence = tuple(sot_sequence)
+
+ def get_vocab_size(self):
+ return self.encoding.n_vocab
+
+ def encode(self, text):
+ return self.encoding.encode(text, allowed_special="all")
+
+ def decode(self, token_ids: List[int], **kwargs) -> str:
+ return self.encoding.decode(token_ids, **kwargs)
+
+ @cached_property
+ def eot(self) -> int:
+ return self.encoding.eot_token
+
+ @cached_property
+ def stop(self) -> int:
+ return self.special_tokens["[STOP]"]
+
+ @cached_property
+ def start(self) -> int:
+ return self.special_tokens["[START]"]
+
+ @cached_property
+ def transcribe(self) -> int:
+ return self.special_tokens["[transcribe]"]
+
+ @cached_property
+ def translate(self) -> int:
+ return self.special_tokens["[translate]"]
+
+ @cached_property
+ def sot(self) -> int:
+ return self.special_tokens["[startoftranscript]"]
+
+ @cached_property
+ def sot_lm(self) -> int:
+ return self.special_tokens["[startoflm]"]
+
+ @cached_property
+ def sot_prev(self) -> int:
+ return self.special_tokens["[startofprev]"]
+
+ @cached_property
+ def no_speech(self) -> int:
+ return self.special_tokens["[nospeech]"]
+
+ @cached_property
+ def language_token(self) -> int:
+ """Returns the token id corresponding to the value of the `language` field"""
+ if self.language is None:
+ raise ValueError("This tokenizer does not have language token configured")
+
+ return self.to_language_token(self.language)
+
+ def to_language_token(self, language):
+ if token := self.special_tokens.get(f"[{language}]", None):
+ return token
+
+ raise KeyError(f"Language {language} not found in tokenizer.")
+
+ @cached_property
+ def all_language_tokens(self) -> Tuple[int]:
+ result = []
+ for token, token_id in self.special_tokens.items():
+ if token.strip("[]") in LANGUAGES:
+ result.append(token_id)
+ return tuple(result)[: self.num_languages]
+
+ @cached_property
+ def all_language_codes(self) -> Tuple[str]:
+ return tuple(self.decode([_l]).strip("[]") for _l in self.all_language_tokens)
+
+ @cached_property
+ 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.encoding.encode(" -")[0], self.encoding.encode(" '")[0]}
+ for symbol in symbols + list(miscellaneous):
+ for tokens in [
+ self.encoding.encode(symbol),
+ self.encoding.encode(" " + symbol),
+ ]:
+ if len(tokens) == 1 or symbol in miscellaneous:
+ result.add(tokens[0])
+
+ return tuple(sorted(result))
+
+ def split_to_word_tokens(self, tokens: List[int]):
+ if self.language in {"zh", "ja", "th", "lo", "my", "yue"}:
+ # These languages don't typically use spaces, so it is difficult to split words
+ # without morpheme analysis. Here, we instead split words at any
+ # position where the tokens are decoded as valid unicode points
+ return self.split_tokens_on_unicode(tokens)
+
+ return self.split_tokens_on_spaces(tokens)
+
+ def split_tokens_on_unicode(self, tokens: List[int]):
+ decoded_full = self.decode(tokens)
+ replacement_char = "\ufffd"
+
+ words = []
+ word_tokens = []
+ current_tokens = []
+ unicode_offset = 0
+
+ for token in tokens:
+ current_tokens.append(token)
+ decoded = self.decode(current_tokens)
+
+ if (
+ replacement_char not in decoded
+ or decoded_full[unicode_offset + decoded.index(replacement_char)]
+ == replacement_char
+ ):
+ words.append(decoded)
+ word_tokens.append(current_tokens)
+ current_tokens = []
+ unicode_offset += len(decoded)
+
+ return words, word_tokens
+
+ def split_tokens_on_spaces(self, tokens: List[int]):
+ subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
+ words = []
+ word_tokens = []
+
+ for subword, subword_tokens in zip(subwords, subword_tokens_list):
+ special = subword_tokens[0] >= self.eot
+ with_space = subword.startswith(" ")
+ punctuation = subword.strip() in string.punctuation
+ if special or with_space or punctuation or len(words) == 0:
+ words.append(subword)
+ word_tokens.append(subword_tokens)
+ else:
+ words[-1] = words[-1] + subword
+ word_tokens[-1].extend(subword_tokens)
+
+ return words, word_tokens
+
+
+@lru_cache(maxsize=None)
+def get_encoding(name: str = "multilingual", num_languages: int = 100):
+ vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
+ ranks = {
+ base64.b64decode(token): int(rank)
+ for token, rank in (line.split() for line in open(vocab_path) if line)
+ }
+ n_vocab = len(ranks)
+ special_tokens = {}
+
+ specials = [
+ "[STOP]",
+ "[UNK]",
+ "[SPACE]",
+ "[START]",
+ "[nospk]",
+ "[spkemb]",
+ "[emotionemb]",
+ "[contextemb]",
+ "[sbreak]",
+ "[pbreak]",
+ "[uvbreak]",
+ "[bsing]",
+ "[esing]",
+ "[sing]",
+ "[hum]",
+ "[laugh]",
+ "[break]",
+ "[breath]",
+ "[oralsii]",
+ "[oralze]",
+ "[prolong]",
+ "[stress]",
+ "[bstrong]",
+ "[estrong]",
+ "[hiccup]",
+ "[inhale]",
+ "[exhale]",
+ "[emounknown]",
+ "[happy]",
+ "[neutral]",
+ "[sad]",
+ "[surprise]",
+ "[angry]",
+ "[disgust]",
+ "[emo]",
+ "[laugha]",
+ "[laughb]",
+ "[laughc]",
+ "[orala]",
+ "[oralb]",
+ "[oralc]",
+ "[orald]",
+ "[orale]",
+ "[breaka]",
+ "[breakb]",
+ "[breakc]",
+ "[breakd]",
+ "[breake]",
+ "[breakf]",
+ "[endoftext]",
+ "[startoftranscript]",
+ *[f"[{lang}]" for lang in list(LANGUAGES.keys())[:num_languages]],
+ "[translate]",
+ "[transcribe]",
+ "[startoflm]",
+ "[startofprev]",
+ "[nospeech]",
+ ]
+
+ for token in specials:
+ special_tokens[token] = n_vocab
+ n_vocab += 1
+
+ return tiktoken.Encoding(
+ name=os.path.basename(vocab_path),
+ explicit_n_vocab=n_vocab,
+ pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d|\[[A-Z]+\]|\[[a-z]+\]|[\x{4e00}-\x{9df5}]| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+ mergeable_ranks=ranks,
+ special_tokens=special_tokens,
+ )
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+ multilingual: bool,
+ *,
+ num_languages: int = 100,
+ language: Optional[str] = None,
+ task: Optional[str] = None, # Literal["transcribe", "translate", 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:
+ encoding_name = "multilingual"
+ language = language or "en"
+ task = task or "transcribe"
+ else:
+ encoding_name = "gpt2"
+ language = None
+ task = None
+
+ encoding = get_encoding(name=encoding_name, num_languages=num_languages)
+
+ return Tokenizer(
+ encoding=encoding, num_languages=num_languages, language=language, task=task
+ )
diff --git a/fireredtts/utils/__init__.py b/fireredtts/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fireredtts/utils/utils.py b/fireredtts/utils/utils.py
new file mode 100644
index 0000000..88bc74b
--- /dev/null
+++ b/fireredtts/utils/utils.py
@@ -0,0 +1,37 @@
+import os
+from pathlib import Path
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+
+
+def load_audio(audiopath, sampling_rate):
+ """_summary_
+
+ Args:
+ audiopath (_type_): audio_path
+ sampling_rate (_type_): sampling_rate
+
+ Returns:
+ _type_: _description_
+ """
+ audio, lsr = torchaudio.load(audiopath)
+
+ # stereo to mono if needed
+ if audio.size(0) != 1:
+ audio = torch.mean(audio, dim=0, keepdim=True)
+
+ # resample
+ audio_resampled = torchaudio.functional.resample(audio, lsr, sampling_rate)
+ if torch.any(audio > 10) or not torch.any(audio < 0):
+ print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
+
+ if torch.any(audio_resampled > 10) or not torch.any(audio_resampled < 0):
+ print(
+ f"Error with {audiopath}. Max={audio_resampled.max()} min={audio_resampled.min()}"
+ )
+ # clip audio invalid values
+ audio.clip_(-1, 1)
+ audio_resampled.clip_(-1, 1)
+ return audio, lsr, audio_resampled
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..bc83ed1
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,12 @@
+diffusers
+librosa
+soundfile
+einops
+transformers
+tiktoken
+inflect
+lingua-language-detector
+WeTextProcessing
+pydub
+zhon
+ffmpeg-python
\ No newline at end of file