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import os
import numpy as np
import torch
import torch.utils.data
from mel_processing import spectrogram_torch
from utils import load_filepaths_and_text, load_wav_to_torch
class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
def __init__(self, hparams):
self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files)
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.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
audiopaths_and_text_new = []
lengths = []
for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def get_audio_text_pair(self, audiopath_and_text):
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
pitch = audiopath_and_text[2]
pitchf = audiopath_and_text[3]
dv = audiopath_and_text[4]
phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
pitch = pitch[:len_min]
pitchf = pitchf[:len_min]
return (spec, wav, phone, pitch, pitchf, dv)
def get_labels(self, phone, pitch, pitchf):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
pitch = np.load(pitch)
pitchf = np.load(pitchf)
n_num = min(phone.shape[0], 900)
phone = phone[:n_num, :]
pitch = pitch[:n_num]
pitchf = pitchf[:n_num]
phone = torch.FloatTensor(phone)
pitch = torch.LongTensor(pitch)
pitchf = torch.FloatTensor(pitchf)
return phone, pitch, pitchf
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
)
)
audio_norm = audio
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)
except Exception as error:
print(f"{spec_filename}: {error}")
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollateMultiNSFsid:
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
)
pitch_padded = torch.LongTensor(len(batch), max_phone_len)
pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
phone_padded.zero_()
pitch_padded.zero_()
pitchf_padded.zero_()
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
pitch = row[3]
pitch_padded[i, : pitch.size(0)] = pitch
pitchf = row[4]
pitchf_padded[i, : pitchf.size(0)] = pitchf
sid[i] = row[5]
return (
phone_padded,
phone_lengths,
pitch_padded,
pitchf_padded,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
sid,
)
class TextAudioLoader(torch.utils.data.Dataset):
def __init__(self, hparams):
self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files)
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.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
audiopaths_and_text_new = []
lengths = []
for entry in self.audiopaths_and_text:
if len(entry) >= 3:
audiopath, text, dv = entry[:3]
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, dv])
lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = os.path.basename(os.path.dirname(sid))
try:
sid = torch.LongTensor([int("".join(filter(str.isdigit, sid)))])
except ValueError as error:
print(f"Error converting speaker ID '{sid}' to integer. Exception: {error}")
sid = torch.LongTensor([0])
return sid
def get_audio_text_pair(self, audiopath_and_text):
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
dv = audiopath_and_text[2]
phone = self.get_labels(phone)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
return (spec, wav, phone, dv)
def get_labels(self, phone):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
n_num = min(phone.shape[0], 900)
phone = phone[:n_num, :]
phone = torch.FloatTensor(phone)
return phone
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
)
)
audio_norm = audio
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)
except Exception as error:
print(f"{spec_filename}: {error}")
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollate:
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
)
phone_padded.zero_()
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
sid[i] = row[3]
return (
phone_padded,
phone_lengths,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
sid,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
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)):
length = self.lengths[i]
idx_bucket = self._bisect(length)
if idx_bucket != -1:
buckets[idx_bucket].append(i)
for i in range(len(buckets) - 1, -1, -1): #
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
num_samples_per_bucket = []
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
num_samples_per_bucket.append(len_bucket + rem)
return buckets, num_samples_per_bucket
def __iter__(self):
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]
rem = num_samples_bucket - len_bucket
ids_bucket = (
ids_bucket
+ ids_bucket * (rem // len_bucket)
+ ids_bucket[: (rem % len_bucket)]
)
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
def __len__(self):
return self.num_samples // self.batch_size
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