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# 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 torch.utils.data.sampler import (
BatchSampler,
RandomSampler,
SequentialSampler,
)
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",
)
self.all_num_frames = []
for i in range(len(self.metadata)):
self.all_num_frames.append(self.metadata[i]["Duration"])
self.num_frame_sorted = np.array(sorted(self.all_num_frames))
self.num_frame_indices = np.array(
sorted(
range(len(self.all_num_frames)), key=lambda k: self.all_num_frames[k]
)
)
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
def get_num_frames(self, index):
utt_info = self.metadata[index]
return int(
utt_info["Duration"]
* (self.cfg.preprocess.sample_rate // self.cfg.preprocess.codec_hop_size)
)
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
def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
if len(batch) == 0:
return 0
if len(batch) == max_sentences:
return 1
if num_tokens > max_tokens:
return 1
return 0
def batch_by_size(
indices,
num_tokens_fn,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
):
"""
Yield mini-batches of indices bucketed by size. Batches may contain
sequences of different lengths.
Args:
indices (List[int]): ordered list of dataset indices
num_tokens_fn (callable): function that returns the number of tokens at
a given index
max_tokens (int, optional): max number of tokens in each batch
(default: None).
max_sentences (int, optional): max number of sentences in each
batch (default: None).
required_batch_size_multiple (int, optional): require batch size to
be a multiple of N (default: 1).
"""
bsz_mult = required_batch_size_multiple
sample_len = 0
sample_lens = []
batch = []
batches = []
for i in range(len(indices)):
idx = indices[i]
num_tokens = num_tokens_fn(idx)
sample_lens.append(num_tokens)
sample_len = max(sample_len, num_tokens)
assert (
sample_len <= max_tokens
), "sentence at index {} of size {} exceeds max_tokens " "limit of {}!".format(
idx, sample_len, max_tokens
)
num_tokens = (len(batch) + 1) * sample_len
if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
mod_len = max(
bsz_mult * (len(batch) // bsz_mult),
len(batch) % bsz_mult,
)
batches.append(batch[:mod_len])
batch = batch[mod_len:]
sample_lens = sample_lens[mod_len:]
sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
batch.append(idx)
if len(batch) > 0:
batches.append(batch)
return batches
class VariableSampler(BatchSampler):
def __init__(self, sampler, drop_last: bool, use_random_sampler=False):
self.data_list = sampler
if use_random_sampler:
self.sampler = RandomSampler(sampler)
else:
self.sampler = SequentialSampler(sampler)
super().__init__(self.sampler, 1, drop_last)
def __iter__(self):
for batch_ids in self.data_list:
yield batch_ids
def __len__(self):
if self.drop_last:
return len(self.sampler) // self.batch_size
else:
return (len(self.sampler) + self.batch_size - 1) // self.batch_size
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