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fix

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This commit is contained in:
rcell
2023-07-24 15:16:06 +08:00
parent 1fa3ce1d03
commit 9fd6e9a259
7 changed files with 348 additions and 50345 deletions
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157
data_utils.py
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@@ -5,26 +5,29 @@ import numpy as np
import torch
import torch.utils.data
import commons
import commons
from mel_processing import spectrogram_torch
from utils import load_wav_to_torch, load_filepaths_and_text
from text import cleaned_text_to_sequence, get_bert
"""Multi speaker version"""
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
1) loads audio, speaker_id, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_sid_text, hparams):
self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.spk_map = hparams.spk2id
self.cleaned_text = getattr(hparams, "cleaned_text", False)
@@ -49,12 +52,12 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
lengths = []
skipped = 0
for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
audiopath = f'dataset/{spk}/{_id}.wav'
audiopath = f'dataset/{spk}/{_id}.wav'
if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
phones = phones.split(" ")
tone = [int(i) for i in tone.split(" ")]
word2ph = [int(i) for i in word2ph.split(" ")]
audiopaths_sid_text_new.append([audiopath, spk, language,text, phones, tone, word2ph])
audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
else:
skipped += 1
@@ -66,7 +69,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
# separate filename, speaker_id and text
audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language,audiopath)
bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
spec, wav = self.get_audio(audiopath)
sid = torch.LongTensor([int(self.spk_map[sid])])
@@ -80,17 +83,17 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename)
else:
except:
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
self.sampling_rate, self.hop_length, self.win_length,
center=False)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename)
return spec, audio_norm
def get_text(self,text, word2ph,phone, tone, language_str, wav_path):
def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
# print(text, word2ph,phone, tone, language_str)
pold = phone
w2pho = [i for i in word2ph]
@@ -115,8 +118,12 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
assert bert.shape[-1] == len(phone)
except:
bert = get_bert(text, word2ph, language_str)
assert bert.shape[-1] == len(phone), (bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2,word2ph, text,w2pho)
torch.save(bert, bert_path)
torch.save(bert, bert_path)
print(bert.shape[-1], bert_path, text, pold)
assert bert.shape[-1] == len(phone)
assert bert.shape[-1] == len(phone), (
bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
@@ -136,6 +143,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
class TextAudioSpeakerCollate():
""" Zero-pads model inputs and targets
"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
@@ -206,20 +214,21 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
Maintain similar input lengths in a batch.
Length groups are specified by boundaries.
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
It removes samples which are not included in the boundaries.
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
"""
def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
self.lengths = dataset.lengths
self.batch_size = batch_size
self.boundaries = boundaries
self.buckets, self.num_samples_per_bucket = self._create_buckets()
self.total_size = sum(self.num_samples_per_bucket)
self.num_samples = self.total_size // self.num_replicas
def _create_buckets(self):
buckets = [[] for _ in range(len(self.boundaries) - 1)]
for i in range(len(self.lengths)):
@@ -227,12 +236,12 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
idx_bucket = self._bisect(length)
if idx_bucket != -1:
buckets[idx_bucket].append(i)
for i in range(len(buckets) - 1, 0, -1):
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i+1)
self.boundaries.pop(i + 1)
num_samples_per_bucket = []
for i in range(len(buckets)):
len_bucket = len(buckets[i])
@@ -240,61 +249,61 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
num_samples_per_bucket.append(len_bucket + rem)
return buckets, num_samples_per_bucket
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
if self.shuffle:
for bucket in self.buckets:
indices.append(torch.randperm(len(bucket), generator=g).tolist())
else:
for bucket in self.buckets:
indices.append(list(range(len(bucket))))
batches = []
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# add extra samples to make it evenly divisible
rem = num_samples_bucket - len_bucket
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
# subsample
ids_bucket = ids_bucket[self.rank::self.num_replicas]
# batching
for j in range(len(ids_bucket) // self.batch_size):
batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
batches.append(batch)
if self.shuffle:
batch_ids = torch.randperm(len(batches), generator=g).tolist()
batches = [batches[i] for i in batch_ids]
self.batches = batches
assert len(self.batches) * self.batch_size == self.num_samples
return iter(self.batches)
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
if self.shuffle:
for bucket in self.buckets:
indices.append(torch.randperm(len(bucket), generator=g).tolist())
else:
for bucket in self.buckets:
indices.append(list(range(len(bucket))))
batches = []
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# add extra samples to make it evenly divisible
rem = num_samples_bucket - len_bucket
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
# subsample
ids_bucket = ids_bucket[self.rank::self.num_replicas]
# batching
for j in range(len(ids_bucket) // self.batch_size):
batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
batches.append(batch)
if self.shuffle:
batch_ids = torch.randperm(len(batches), generator=g).tolist()
batches = [batches[i] for i in batch_ids]
self.batches = batches
assert len(self.batches) * self.batch_size == self.num_samples
return iter(self.batches)
def _bisect(self, x, lo=0, hi=None):
if hi is None:
hi = len(self.boundaries) - 1
if hi > lo:
mid = (hi + lo) // 2
if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
return mid
elif x <= self.boundaries[mid]:
return self._bisect(x, lo, mid)
else:
return self._bisect(x, mid + 1, hi)
else:
return -1
if hi is None:
hi = len(self.boundaries) - 1
if hi > lo:
mid = (hi + lo) // 2
if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
return mid
elif x <= self.boundaries[mid]:
return self._bisect(x, lo, mid)
else:
return self._bisect(x, mid + 1, hi)
else:
return -1
def __len__(self):
return self.num_samples // self.batch_size