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import os
import sys
import torch
import numpy as np
import torch.utils.data as tdata
sys.path.append(os.getcwd())
from main.app.variables import translations
from main.inference.training.mel_processing import spectrogram_torch
from main.inference.training.utils import load_filepaths_and_text, load_wav_to_torch
class TextAudioLoaderMultiNSFsid(tdata.Dataset):
def __init__(self, hparams, energy=False):
self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files)
self.max_wav_value = hparams.max_wav_value
self.sample_rate = hparams.sample_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sample_rate = hparams.sample_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self.energy = energy
self._filter()
def _filter(self):
audiopaths_and_text_new, lengths = [], []
for item in self.audiopaths_and_text:
audiopath = item[0]
text = item[1]
if self.min_text_len <= len(text) <= self.max_text_len:
audiopaths_and_text_new.append(item)
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):
try:
sid = torch.LongTensor([int(sid)])
except ValueError:
sid = torch.LongTensor([0])
return sid
def get_audio_text_pair(self, audiopath_and_text):
if self.energy:
phone, pitch, pitchf, energy = self.get_labels(audiopath_and_text[1], audiopath_and_text[2], audiopath_and_text[3], audiopath_and_text[4])
spec, wav = self.get_audio(audiopath_and_text[0])
dv = self.get_sid(audiopath_and_text[5])
else:
phone, pitch, pitchf, _ = self.get_labels(audiopath_and_text[1], audiopath_and_text[2], audiopath_and_text[3])
spec, wav = self.get_audio(audiopath_and_text[0])
dv = self.get_sid(audiopath_and_text[4])
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, wav, phone = spec[:, :len_min], wav[:, :len_wav], phone[:len_min, :]
pitch, pitchf = pitch[:len_min], pitchf[:len_min]
if self.energy: energy = energy[:len_min]
return (spec, wav, phone, pitch, pitchf, dv, energy) if self.energy else (spec, wav, phone, pitch, pitchf, dv)
def get_labels(self, phone, pitch, pitchf, energy=None):
phone = np.repeat(np.load(phone), 2, axis=0)
n_num = min(phone.shape[0], 900)
return torch.FloatTensor(phone[:n_num, :]), torch.LongTensor(np.load(pitch)[:n_num]), torch.FloatTensor(np.load(pitchf)[:n_num]), torch.FloatTensor(np.load(energy)[:n_num]) if energy else None
def get_audio(self, filename):
audio, sample_rate = load_wav_to_torch(filename)
if sample_rate != self.sample_rate: raise ValueError(translations["sr_does_not_match"].format(sample_rate=sample_rate, sample_rate2=self.sample_rate))
audio_norm = audio.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename, weights_only=True)
except Exception:
spec = torch.squeeze(spectrogram_torch(audio_norm, self.filter_length, self.hop_length, self.win_length, center=False), 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = torch.squeeze(spectrogram_torch(audio_norm, self.filter_length, self.hop_length, self.win_length, center=False), 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, energy=False):
self.return_ids = return_ids
self.energy = energy
def __call__(self, batch):
_, ids_sorted_decreasing = torch.sort(torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True)
spec_lengths, wave_lengths = torch.LongTensor(len(batch)), torch.LongTensor(len(batch))
spec_padded, wave_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max([x[0].size(1) for x in batch])), torch.FloatTensor(len(batch), 1, max([x[1].size(1) for x in batch]))
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths, phone_padded = torch.LongTensor(len(batch)), torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1])
pitch_padded, pitchf_padded = torch.LongTensor(len(batch), max_phone_len), torch.FloatTensor(len(batch), max_phone_len)
phone_padded.zero_()
pitch_padded.zero_()
pitchf_padded.zero_()
sid = torch.LongTensor(len(batch))
if self.energy:
energy_padded = torch.FloatTensor(len(batch), max_phone_len)
energy_padded.zero_()
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]
if self.energy:
energy = row[6]
energy_padded[i, : energy.size(0)] = energy
return (phone_padded, phone_lengths, pitch_padded, pitchf_padded, spec_padded, spec_lengths, wave_padded, wave_lengths, sid, energy_padded) if self.energy else (phone_padded, phone_lengths, pitch_padded, pitchf_padded, spec_padded, spec_lengths, wave_padded, wave_lengths, sid)
class TextAudioLoader(tdata.Dataset):
def __init__(self, hparams, energy=False):
self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files)
self.max_wav_value = hparams.max_wav_value
self.sample_rate = hparams.sample_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sample_rate = hparams.sample_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self.energy = energy
self._filter()
def _filter(self):
audiopaths_and_text_new, lengths = [], []
for item in self.audiopaths_and_text:
audiopath = item[0]
text = item[1]
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append(item)
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):
try:
sid = torch.LongTensor([int(sid)])
except ValueError:
sid = torch.LongTensor([0])
return sid
def get_audio_text_pair(self, audiopath_and_text):
if self.energy:
phone, energy = self.get_labels(audiopath_and_text[1], audiopath_and_text[2])
spec, wav = self.get_audio(audiopath_and_text[0])
dv = self.get_sid(audiopath_and_text[3])
else:
phone, _ = self.get_labels(audiopath_and_text[1])
spec, wav = self.get_audio(audiopath_and_text[0])
dv = self.get_sid(audiopath_and_text[2])
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, :]
if self.energy: energy = energy[:len_min]
return (spec, wav, phone, dv, energy) if self.energy else (spec, wav, phone, dv)
def get_labels(self, phone, energy=None):
phone = np.repeat(np.load(phone), 2, axis=0)
n_num = min(phone.shape[0], 900)
return torch.FloatTensor(phone[:min(phone.shape[0], 900), :]), torch.FloatTensor(np.load(energy)[:n_num]) if energy else None
def get_audio(self, filename):
audio, sample_rate = load_wav_to_torch(filename)
if sample_rate != self.sample_rate: raise ValueError(translations["sr_does_not_match"].format(sample_rate=sample_rate, sample_rate2=self.sample_rate))
audio_norm = audio.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename, weights_only=True)
except Exception:
spec = torch.squeeze(spectrogram_torch(audio_norm, self.filter_length, self.hop_length, self.win_length, center=False), 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = torch.squeeze(spectrogram_torch(audio_norm, self.filter_length, self.hop_length, self.win_length, center=False), 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, energy=False):
self.return_ids = return_ids
self.energy = energy
def __call__(self, batch):
_, ids_sorted_decreasing = torch.sort(torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True)
spec_lengths, wave_lengths = torch.LongTensor(len(batch)), torch.LongTensor(len(batch))
spec_padded, wave_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max([x[0].size(1) for x in batch])), torch.FloatTensor(len(batch), 1, max([x[1].size(1) for x in batch]))
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths, phone_padded = torch.LongTensor(len(batch)), torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1])
phone_padded.zero_()
sid = torch.LongTensor(len(batch))
if self.energy:
energy_padded = torch.FloatTensor(len(batch), max_phone_len)
energy_padded.zero_()
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]
if self.energy:
energy = row[4]
energy_padded[i, : energy.size(0)] = energy
return (phone_padded, phone_lengths, spec_padded, spec_lengths, wave_padded, wave_lengths, sid, energy_padded) if self.energy else (phone_padded, phone_lengths, spec_padded, spec_lengths, wave_padded, wave_lengths, sid)
class DistributedBucketSampler(tdata.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)):
idx_bucket = self._bisect(self.lengths[i])
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
num_samples_per_bucket.append(len_bucket + ((total_batch_size - (len_bucket % total_batch_size)) % total_batch_size))
return buckets, num_samples_per_bucket
def __iter__(self):
g = torch.Generator()
g.manual_seed(self.epoch)
indices, batches = [], []
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))))
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
rem = self.num_samples_per_bucket[i] - len_bucket
ids_bucket = (ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[: (rem % len_bucket)])[self.rank :: self.num_replicas]
for j in range(len(ids_bucket) // self.batch_size):
batches.append([bucket[idx] for idx in ids_bucket[j * self.batch_size : (j + 1) * self.batch_size]])
if self.shuffle: batches = [batches[i] for i in torch.randperm(len(batches), generator=g).tolist()]
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 |