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cfbcb75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | # Copyright (c) 2025 ASLP-LAB
# 2025 Huakang Chen (huakang@mail.nwpu.edu.cn)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import random
class DiffusionDataset(torch.utils.data.Dataset):
def __init__(self, file_path, max_frames=2048, min_frames=512, sampling_rate=44100, downsample_rate=2048, precision='fp16'):
self.max_frames = max_frames
self.min_frames = min_frames
self.sampling_rate = sampling_rate
self.downsample_rate = 2048
self.max_secs = max_frames / (sampling_rate / downsample_rate)
self.file_path = file_path
with open(file_path, 'r') as f:
self.file_lst = [line.strip() for line in f.readlines()]
self.pad_token_id = 0
self.comma_token_id = 1
self.period_token_id = 2
self.start_token_id = 355
if precision == 'fp16':
self.feature_dtype = torch.float16
elif precision == 'bf16':
self.feature_dtype = torch.bfloat16
elif precision == 'fp32':
self.feature_dtype = torch.float32
random.seed(42)
random.shuffle(self.file_lst)
def load_item(self, item, field):
try:
item, reader_idx = item[field]
item = self.lance_connections[reader_idx].get_datas_by_rowids([item._rowid])[0]
except Exception as e:
return None
return item
def get_triple(self, item):
utt, lrc_path, latent_path, style_path = item.split("|")
time_lrc = torch.load(lrc_path, map_location='cpu')
input_times = time_lrc['time']
input_lrcs = time_lrc['lrc']
lrc_with_time = list(zip(input_times, input_lrcs))
latent = torch.load(latent_path, map_location='cpu') # [b, d, t]
latent = latent.squeeze(0)
prompt = torch.load(style_path, map_location='cpu') # [b, d]
prompt = prompt.squeeze(0)
max_start_frame = max(0, latent.shape[-1] - self.max_frames)
start_frame = random.randint(0, max_start_frame)
start_time = start_frame * self.downsample_rate / self.sampling_rate
normalized_start_time = start_frame / latent.shape[-1]
latent = latent[:, start_frame:]
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
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
if len(lrc_with_time) >= 1:
latent_end_time = lrc_with_time[-1][0]
else:
raise
if self.max_frames == 2048:
lrc_with_time = lrc_with_time[:-1] if len(lrc_with_time) >= 1 else lrc_with_time # drop last, can be empty
lrc = torch.zeros((self.max_frames,), dtype=torch.long)
tokens_count = 0
last_end_pos = 0
for time_start, line in lrc_with_time:
tokens = [token if token != self.period_token_id else self.comma_token_id for token in line] + [self.period_token_id]
tokens = torch.tensor(tokens, dtype=torch.long)
num_tokens = tokens.shape[0]
gt_frame_start = int(time_start * self.sampling_rate / self.downsample_rate)
frame_shift = 0
frame_start = max(gt_frame_start - frame_shift, last_end_pos)
frame_len = min(num_tokens, self.max_frames - frame_start)
lrc[frame_start:frame_start + frame_len] = tokens[:frame_len]
tokens_count += num_tokens
last_end_pos = frame_start + frame_len
latent = latent[:, :int(latent_end_time * self.sampling_rate / self.downsample_rate)]
latent = latent.to(self.feature_dtype)
prompt = prompt.to(self.feature_dtype)
return prompt, lrc, latent, normalized_start_time
def __getitem__(self, index):
idx = index
while True:
try:
prompt, lrc, latent, start_time = self.get_triple(self.file_lst[idx])
if latent.shape[-1] < self.min_frames: # Too short
raise
item = {'prompt': prompt, "lrc": lrc, "latent": latent, "start_time": start_time}
return item
except Exception as e:
idx = random.randint(0, self.__len__() - 1)
continue
def __len__(self):
return len(self.file_lst)
def custom_collate_fn(self, batch):
latent_list = [item['latent'] for item in batch]
prompt_list = [item['prompt'] for item in batch]
lrc_list = [item['lrc'] for item in batch]
start_time_list = [item['start_time'] for item in batch]
latent_lengths = torch.LongTensor([latent.shape[-1] for latent in latent_list])
prompt_lengths = torch.LongTensor([prompt.shape[-1] for prompt in prompt_list])
lrc_lengths = torch.LongTensor([lrc.shape[-1] for lrc in lrc_list])
max_prompt_length = prompt_lengths.amax()
padded_prompt_list = []
for prompt in prompt_list:
padded_prompt = torch.nn.functional.pad(prompt, (0, max_prompt_length - prompt.shape[-1]))
padded_prompt_list.append(padded_prompt)
padded_latent_list = []
for latent in latent_list:
padded_latent = torch.nn.functional.pad(latent, (0, self.max_frames - latent.shape[-1]))
padded_latent_list.append(padded_latent)
padded_start_time_list = []
for start_time in start_time_list:
padded_start_time = start_time
padded_start_time_list.append(padded_start_time)
prompt_tensor = torch.stack(padded_prompt_list)
lrc_tensor = torch.stack(lrc_list)
latent_tensor = torch.stack(padded_latent_list)
start_time_tensor = torch.tensor(padded_start_time_list)
return {'prompt': prompt_tensor, 'lrc': lrc_tensor, 'latent': latent_tensor, \
"prompt_lengths": prompt_lengths, "lrc_lengths": lrc_lengths, "latent_lengths": latent_lengths, \
"start_time": start_time_tensor}
if __name__ == "__main__":
dd = DiffusionDataset("train.scp", 2048, 512)
x = dd[0]
import pdb; pdb.set_trace()
print(x) |