| 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 |