Spaces:
Running
Running
File size: 6,947 Bytes
0d80816 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch.nn.utils.rnn import pad_sequence
from utils.data_utils import *
from models.tts.base.tts_dataset import (
TTSDataset,
TTSCollator,
TTSTestDataset,
TTSTestCollator,
)
from utils.tokenizer import tokenize_audio
class VALLEDataset(TTSDataset):
def __init__(self, cfg, dataset, is_valid=False):
super().__init__(cfg, dataset, is_valid=is_valid)
"""
Args:
cfg: config
dataset: dataset name
is_valid: whether to use train or valid dataset
"""
assert isinstance(dataset, str)
assert cfg.preprocess.use_acoustic_token == True
if cfg.preprocess.use_acoustic_token:
self.utt2acousticToken_path = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
self.utt2acousticToken_path[utt] = os.path.join(
cfg.preprocess.processed_dir,
dataset,
cfg.preprocess.acoustic_token_dir, # code
uid + ".npy",
)
def __len__(self):
return super().__len__()
def get_metadata(self):
metadata_filter = []
with open(self.metafile_path, "r", encoding="utf-8") as f:
metadata = json.load(f)
for utt_info in metadata:
duration = utt_info['Duration']
if duration >= self.cfg.preprocess.max_duration or duration <= self.cfg.preprocess.min_duration:
continue
metadata_filter.append(utt_info)
return metadata_filter
def get_dur(self, idx):
utt_info = self.metadata[idx]
return utt_info['Duration']
def __getitem__(self, index):
single_feature = super().__getitem__(index)
utt_info = self.metadata[index]
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
# acoustic token
if self.cfg.preprocess.use_acoustic_token:
acoustic_token = np.load(self.utt2acousticToken_path[utt])
if "target_len" not in single_feature.keys():
single_feature["target_len"] = acoustic_token.shape[0]
single_feature["acoustic_token"] = acoustic_token # [T, 8]
return single_feature
class VALLECollator(TTSCollator):
def __init__(self, cfg):
super().__init__(cfg)
def __call__(self, batch):
parsed_batch_features = super().__call__(batch)
return parsed_batch_features
class VALLETestDataset(TTSTestDataset):
def __init__(self,args, cfg):
super().__init__(args, cfg)
# prepare data
assert cfg.preprocess.use_acoustic_token == True
if cfg.preprocess.use_acoustic_token:
self.utt2acousticToken = {}
for utt_info in self.metadata:
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
# extract acoustic token
audio_file = utt_info["Audio_pormpt_path"]
encoded_frames = tokenize_audio(self.audio_tokenizer, audio_file)
audio_prompt_token = encoded_frames[0][0].transpose(2, 1).squeeze(0).cpu().numpy()
self.utt2acousticToken[utt] = audio_prompt_token
def __getitem__(self, index):
utt_info = self.metadata[index]
dataset = utt_info["Dataset"]
uid = utt_info["Uid"]
utt = "{}_{}".format(dataset, uid)
single_feature = dict()
# acoustic token
if self.cfg.preprocess.use_acoustic_token:
acoustic_token = self.utt2acousticToken[utt]
if "target_len" not in single_feature.keys():
single_feature["target_len"] = acoustic_token.shape[0]
single_feature["acoustic_token"] = acoustic_token # [T, 8]
# phone sequence todo
if self.cfg.preprocess.use_phone:
single_feature["phone_seq"] = np.array(self.utt2seq[utt])
single_feature["phone_len"] = len(self.utt2seq[utt])
single_feature["pmt_phone_seq"] = np.array(self.utt2pmtseq[utt])
single_feature["pmt_phone_len"] = len(self.utt2pmtseq[utt])
return single_feature
def get_metadata(self):
with open(self.metafile_path, "r", encoding="utf-8") as f:
metadata = json.load(f)
return metadata
def __len__(self):
return len(self.metadata)
class VALLETestCollator(TTSTestCollator):
def __init__(self, cfg):
self.cfg = cfg
def __call__(self, batch):
packed_batch_features = dict()
for key in batch[0].keys():
if key == "target_len":
packed_batch_features["target_len"] = torch.LongTensor(
[b["target_len"] for b in batch]
)
masks = [
torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
]
packed_batch_features["mask"] = pad_sequence(
masks, batch_first=True, padding_value=0
)
elif key == "phone_len":
packed_batch_features["phone_len"] = torch.LongTensor(
[b["phone_len"] for b in batch]
)
masks = [
torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch
]
packed_batch_features["phn_mask"] = pad_sequence(
masks, batch_first=True, padding_value=0
)
elif key == "pmt_phone_len":
packed_batch_features["pmt_phone_len"] = torch.LongTensor(
[b["pmt_phone_len"] for b in batch]
)
masks = [
torch.ones((b["pmt_phone_len"], 1), dtype=torch.long) for b in batch
]
packed_batch_features["pmt_phone_len_mask"] = pad_sequence(
masks, batch_first=True, padding_value=0
)
elif key == "audio_len":
packed_batch_features["audio_len"] = torch.LongTensor(
[b["audio_len"] for b in batch]
)
masks = [
torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch
]
else:
values = [torch.from_numpy(b[key]) for b in batch]
packed_batch_features[key] = pad_sequence(
values, batch_first=True, padding_value=0
)
return packed_batch_features
|