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Stardust-minus
2023-09-06 13:42:05 +00:00
committed by github-actions[bot]
parent d82ba3457a
commit eef212fd00
26 changed files with 2938 additions and 1453 deletions
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153
data_utils.py
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@@ -16,9 +16,9 @@ from text import cleaned_text_to_sequence, get_bert
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.
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):
@@ -32,7 +32,9 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
self.spk_map = hparams.spk2id
self.hparams = hparams
self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
self.use_mel_spec_posterior = getattr(
hparams, "use_mel_posterior_encoder", False
)
if self.use_mel_spec_posterior:
self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
@@ -58,17 +60,26 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
lengths = []
skipped = 0
logger.info("Init dataset...")
for _id, spk, language, text, phones, tone, word2ph in tqdm(self.audiopaths_sid_text):
audiopath = f'{_id}'
for _id, spk, language, text, phones, tone, word2ph in tqdm(
self.audiopaths_sid_text
):
audiopath = f"{_id}"
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
logger.info("skipped: " + str(skipped) + ", total: " + str(len(self.audiopaths_sid_text)))
logger.info(
"skipped: "
+ str(skipped)
+ ", total: "
+ str(len(self.audiopaths_sid_text))
)
self.audiopaths_sid_text = audiopaths_sid_text_new
self.lengths = lengths
@@ -76,7 +87,9 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
# separate filename, speaker_id and text
audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
bert, ja_bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
bert, ja_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])])
@@ -85,8 +98,11 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError("{} {} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate))
raise ValueError(
"{} {} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
@@ -96,13 +112,26 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
spec = torch.load(spec_filename)
except:
if self.use_mel_spec_posterior:
spec = mel_spectrogram_torch(audio_norm, self.filter_length,
self.n_mel_channels, self.sampling_rate, self.hop_length,
self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
spec = mel_spectrogram_torch(
audio_norm,
self.filter_length,
self.n_mel_channels,
self.sampling_rate,
self.hop_length,
self.win_length,
self.hparams.mel_fmin,
self.hparams.mel_fmax,
center=False,
)
else:
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
spec = spectrogram_torch(
audio_norm,
self.filter_length,
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
@@ -125,17 +154,29 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
torch.save(bert, bert_path)
assert bert.shape[-1] == len(phone), phone
if language_str=='ZH':
if language_str == "ZH":
bert = bert
ja_bert = torch.zeros(768, len(phone))
elif language_str=="JA":
elif language_str == "JA":
ja_bert = bert
bert = torch.zeros(1024, len(phone))
else:
bert = torch.zeros(1024, len(phone))
ja_bert = torch.zeros(768, len(phone))
assert bert.shape[-1] == len(phone), (
bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
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)
@@ -152,9 +193,8 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
return len(self.audiopaths_sid_text)
class TextAudioSpeakerCollate():
""" Zero-pads model inputs and targets
"""
class TextAudioSpeakerCollate:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
@@ -167,8 +207,8 @@ class TextAudioSpeakerCollate():
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[1].size(1) for x in batch]),
dim=0, descending=True)
torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
)
max_text_len = max([len(x[0]) for x in batch])
max_spec_len = max([x[1].size(1) for x in batch])
@@ -198,32 +238,44 @@ class TextAudioSpeakerCollate():
row = batch[ids_sorted_decreasing[i]]
text = row[0]
text_padded[i, :text.size(0)] = text
text_padded[i, : text.size(0)] = text
text_lengths[i] = text.size(0)
spec = row[1]
spec_padded[i, :, :spec.size(1)] = spec
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wav = row[2]
wav_padded[i, :, :wav.size(1)] = wav
wav_padded[i, :, : wav.size(1)] = wav
wav_lengths[i] = wav.size(1)
sid[i] = row[3]
tone = row[4]
tone_padded[i, :tone.size(0)] = tone
tone_padded[i, : tone.size(0)] = tone
language = row[5]
language_padded[i, :language.size(0)] = language
language_padded[i, : language.size(0)] = language
bert = row[6]
bert_padded[i, :, :bert.size(1)] = bert
bert_padded[i, :, : bert.size(1)] = bert
ja_bert = row[7]
ja_bert_padded[i, :, :ja_bert.size(1)] = ja_bert
ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded, ja_bert_padded
return (
text_padded,
text_lengths,
spec_padded,
spec_lengths,
wav_padded,
wav_lengths,
sid,
tone_padded,
language_padded,
bert_padded,
ja_bert_padded,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
@@ -236,7 +288,15 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
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):
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
@@ -254,7 +314,7 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
if idx_bucket != -1:
buckets[idx_bucket].append(i)
try:
try:
for i in range(len(buckets) - 1, 0, -1):
if len(buckets[i]) == 0:
buckets.pop(i)
@@ -262,7 +322,7 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
assert all(len(bucket) > 0 for bucket in buckets)
# When one bucket is not traversed
except Exception as e:
print('Bucket warning ', e)
print("Bucket warning ", e)
for i in range(len(buckets) - 1, -1, -1):
if len(buckets[i]) == 0:
buckets.pop(i)
@@ -272,7 +332,9 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
for i in range(len(buckets)):
len_bucket = len(buckets[i])
total_batch_size = self.num_replicas * self.batch_size
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
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
@@ -293,21 +355,30 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
if (len_bucket == 0):
if len_bucket == 0:
continue
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)]
ids_bucket = (
ids_bucket
+ ids_bucket * (rem // len_bucket)
+ ids_bucket[: (rem % len_bucket)]
)
# subsample
ids_bucket = ids_bucket[self.rank::self.num_replicas]
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]]
batch = [
bucket[idx]
for idx in ids_bucket[
j * self.batch_size : (j + 1) * self.batch_size
]
]
batches.append(batch)
if self.shuffle: