171 lines
6.7 KiB
Python
171 lines
6.7 KiB
Python
# Copyright (c) 2025 ASLP-LAB
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# 2025 Huakang Chen (huakang@mail.nwpu.edu.cn)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import random
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class DiffusionDataset(torch.utils.data.Dataset):
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def __init__(self, file_path, max_frames=2048, min_frames=512, sampling_rate=44100, downsample_rate=2048, precision='fp16'):
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self.max_frames = max_frames
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self.min_frames = min_frames
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self.sampling_rate = sampling_rate
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self.downsample_rate = 2048
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self.max_secs = max_frames / (sampling_rate / downsample_rate)
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self.file_path = file_path
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with open(file_path, 'r') as f:
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self.file_lst = [line.strip() for line in f.readlines()]
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self.pad_token_id = 0
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self.comma_token_id = 1
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self.period_token_id = 2
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self.start_token_id = 355
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if precision == 'fp16':
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self.feature_dtype = torch.float16
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elif precision == 'bf16':
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self.feature_dtype = torch.bfloat16
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elif precision == 'fp32':
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self.feature_dtype = torch.float32
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random.seed(42)
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random.shuffle(self.file_lst)
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def load_item(self, item, field):
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try:
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item, reader_idx = item[field]
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item = self.lance_connections[reader_idx].get_datas_by_rowids([item._rowid])[0]
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except Exception as e:
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return None
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return item
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def get_triple(self, item):
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utt, lrc_path, latent_path, style_path = item.split("|")
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time_lrc = torch.load(lrc_path, map_location='cpu')
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input_times = time_lrc['time']
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input_lrcs = time_lrc['lrc']
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lrc_with_time = list(zip(input_times, input_lrcs))
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latent = torch.load(latent_path, map_location='cpu') # [b, d, t]
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latent = latent.squeeze(0)
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prompt = torch.load(style_path, map_location='cpu') # [b, d]
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prompt = prompt.squeeze(0)
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max_start_frame = max(0, latent.shape[-1] - self.max_frames)
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start_frame = random.randint(0, max_start_frame)
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start_time = start_frame * self.downsample_rate / self.sampling_rate
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normalized_start_time = start_frame / latent.shape[-1]
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latent = latent[:, start_frame:]
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lrc_with_time = [(time_start - start_time, line) for (time_start, line) in lrc_with_time if (time_start - start_time) >= 0] # empty for pure music
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lrc_with_time = [(time_start, line) for (time_start, line) in lrc_with_time if time_start < self.max_secs] # drop time longer than max_secs
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if len(lrc_with_time) >= 1:
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latent_end_time = lrc_with_time[-1][0]
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else:
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raise
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if self.max_frames == 2048:
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lrc_with_time = lrc_with_time[:-1] if len(lrc_with_time) >= 1 else lrc_with_time # drop last, can be empty
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lrc = torch.zeros((self.max_frames,), dtype=torch.long)
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tokens_count = 0
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last_end_pos = 0
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for time_start, line in lrc_with_time:
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tokens = [token if token != self.period_token_id else self.comma_token_id for token in line] + [self.period_token_id]
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tokens = torch.tensor(tokens, dtype=torch.long)
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num_tokens = tokens.shape[0]
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gt_frame_start = int(time_start * self.sampling_rate / self.downsample_rate)
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frame_shift = 0
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frame_start = max(gt_frame_start - frame_shift, last_end_pos)
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frame_len = min(num_tokens, self.max_frames - frame_start)
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lrc[frame_start:frame_start + frame_len] = tokens[:frame_len]
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tokens_count += num_tokens
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last_end_pos = frame_start + frame_len
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latent = latent[:, :int(latent_end_time * self.sampling_rate / self.downsample_rate)]
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latent = latent.to(self.feature_dtype)
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prompt = prompt.to(self.feature_dtype)
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return prompt, lrc, latent, normalized_start_time
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def __getitem__(self, index):
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idx = index
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while True:
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try:
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prompt, lrc, latent, start_time = self.get_triple(self.file_lst[idx])
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if latent.shape[-1] < self.min_frames: # Too short
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raise
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item = {'prompt': prompt, "lrc": lrc, "latent": latent, "start_time": start_time}
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return item
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except Exception as e:
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idx = random.randint(0, self.__len__() - 1)
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continue
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def __len__(self):
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return len(self.file_lst)
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def custom_collate_fn(self, batch):
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latent_list = [item['latent'] for item in batch]
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prompt_list = [item['prompt'] for item in batch]
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lrc_list = [item['lrc'] for item in batch]
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start_time_list = [item['start_time'] for item in batch]
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latent_lengths = torch.LongTensor([latent.shape[-1] for latent in latent_list])
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prompt_lengths = torch.LongTensor([prompt.shape[-1] for prompt in prompt_list])
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lrc_lengths = torch.LongTensor([lrc.shape[-1] for lrc in lrc_list])
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max_prompt_length = prompt_lengths.amax()
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padded_prompt_list = []
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for prompt in prompt_list:
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padded_prompt = torch.nn.functional.pad(prompt, (0, max_prompt_length - prompt.shape[-1]))
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padded_prompt_list.append(padded_prompt)
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padded_latent_list = []
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for latent in latent_list:
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padded_latent = torch.nn.functional.pad(latent, (0, self.max_frames - latent.shape[-1]))
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padded_latent_list.append(padded_latent)
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padded_start_time_list = []
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for start_time in start_time_list:
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padded_start_time = start_time
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padded_start_time_list.append(padded_start_time)
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prompt_tensor = torch.stack(padded_prompt_list)
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lrc_tensor = torch.stack(lrc_list)
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latent_tensor = torch.stack(padded_latent_list)
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start_time_tensor = torch.tensor(padded_start_time_list)
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return {'prompt': prompt_tensor, 'lrc': lrc_tensor, 'latent': latent_tensor, \
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"prompt_lengths": prompt_lengths, "lrc_lengths": lrc_lengths, "latent_lengths": latent_lengths, \
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"start_time": start_time_tensor}
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if __name__ == "__main__":
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dd = DiffusionDataset("train.scp", 2048, 512)
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x = dd[0]
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import pdb; pdb.set_trace()
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print(x) |