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| import os | |
| import random | |
| import time | |
| import numpy as np | |
| import torch | |
| import torch.utils.data | |
| from src.Modules import commons | |
| from src.Audio import (mel_spectrogram_torch, spec_to_mel_torch, | |
| spectrogram_torch) | |
| from src.Text import cleaned_text_to_sequence, text_to_sequence | |
| from src.utils import load_filepaths_and_text, load_wav_to_torch | |
| class TextAudioLoader(torch.utils.data.Dataset): | |
| """ | |
| 1) loads audio, text pairs | |
| 2) normalizes text and converts them to sequences of integers | |
| 3) computes spectrograms from audio files. | |
| """ | |
| def __init__(self, audiopaths_and_text, hparams): | |
| self.hparams = hparams | |
| self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) | |
| self.text_cleaners = hparams.text_cleaners | |
| 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.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) | |
| self.cleaned_text = getattr(hparams, "cleaned_text", False) | |
| self.add_blank = hparams.add_blank | |
| self.min_text_len = getattr(hparams, "min_text_len", 1) | |
| self.max_text_len = getattr(hparams, "max_text_len", 190) | |
| random.seed(1234) | |
| random.shuffle(self.audiopaths_and_text) | |
| self._filter() | |
| def _filter(self): | |
| """ | |
| Filter text & store spec lengths | |
| """ | |
| # Store spectrogram lengths for Bucketing | |
| # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) | |
| # spec_length = wav_length // hop_length | |
| audiopaths_and_text_new = [] | |
| lengths = [] | |
| for audiopath, text 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]) | |
| lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) | |
| self.audiopaths_and_text = audiopaths_and_text_new | |
| self.lengths = lengths | |
| def get_audio_text_pair(self, audiopath_and_text): | |
| # separate filename and text | |
| audiopath, text = audiopath_and_text[0], audiopath_and_text[1] | |
| text = self.get_text(text) | |
| spec, wav = self.get_audio(audiopath) | |
| return (text, spec, wav) | |
| def get_audio(self, filename): | |
| # TODO : if linear spec exists convert to mel from existing linear spec | |
| audio, sampling_rate = load_wav_to_torch(filename) | |
| # --- DEBUG 1: VER LA FORMA DEL AUDIO AL CARGAR --- | |
| #print(f"DEBUG 1 (Después de cargar): Archivo={os.path.basename(filename)}, Forma={audio.shape}") | |
| if 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) | |
| # --- DEBUG 2: VER LA FORMA DESPUÉS DE LA CORRECCIÓN --- | |
| #print(f"DEBUG 2 (Después de unsqueeze): Forma={audio_norm.shape}") | |
| spec_filename = filename.replace(".wav", ".spec.pt") | |
| if self.use_mel_spec_posterior: | |
| spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") | |
| if os.path.exists(spec_filename): | |
| spec = torch.load(spec_filename) | |
| else: | |
| if self.use_mel_spec_posterior: | |
| """TODO : (need verification) | |
| if linear spec exists convert to | |
| mel from existing linear spec (uncomment below lines)""" | |
| # if os.path.exists(filename.replace(".wav", ".spec.pt")): | |
| # # spec, n_fft, num_mels, sampling_rate, fmin, fmax | |
| # spec = spec_to_mel_torch( | |
| # torch.load(filename.replace(".wav", ".spec.pt")), | |
| # self.filter_length, self.n_mel_channels, self.sampling_rate, | |
| # self.hparams.mel_fmin, self.hparams.mel_fmax) | |
| 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 = torch.squeeze(spec, 0) | |
| torch.save(spec, spec_filename) | |
| return spec, audio_norm | |
| def get_text(self, text): | |
| if self.cleaned_text: | |
| text_norm = cleaned_text_to_sequence(text) | |
| else: | |
| text_norm = text_to_sequence(text, self.text_cleaners) | |
| if self.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_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: | |
| """Zero-pads model inputs and targets""" | |
| def __init__(self, return_ids=False): | |
| self.return_ids = return_ids | |
| def __call__(self, batch): | |
| """Collate's training batch from normalized text and aduio | |
| PARAMS | |
| ------ | |
| batch: [text_normalized, spec_normalized, wav_normalized] | |
| """ | |
| # 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 | |
| ) | |
| max_text_len = max([len(x[0]) for x in batch]) | |
| max_spec_len = max([x[1].size(1) for x in batch]) | |
| max_wav_len = max([x[2].size(1) for x in batch]) | |
| text_lengths = torch.LongTensor(len(batch)) | |
| spec_lengths = torch.LongTensor(len(batch)) | |
| wav_lengths = torch.LongTensor(len(batch)) | |
| text_padded = torch.LongTensor(len(batch), max_text_len) | |
| spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) | |
| wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) | |
| text_padded.zero_() | |
| spec_padded.zero_() | |
| wav_padded.zero_() | |
| for i in range(len(ids_sorted_decreasing)): | |
| row = batch[ids_sorted_decreasing[i]] | |
| text = row[0] | |
| text_padded[i, : text.size(0)] = text | |
| text_lengths[i] = text.size(0) | |
| spec = row[1] | |
| spec_padded[i, :, : spec.size(1)] = spec | |
| spec_lengths[i] = spec.size(1) | |
| wav = row[2] | |
| wav_padded[i, :, : wav.size(1)] = wav | |
| wav_lengths[i] = wav.size(1) | |
| if self.return_ids: | |
| return ( | |
| text_padded, | |
| text_lengths, | |
| spec_padded, | |
| spec_lengths, | |
| wav_padded, | |
| wav_lengths, | |
| ids_sorted_decreasing, | |
| ) | |
| return ( | |
| text_padded, | |
| text_lengths, | |
| spec_padded, | |
| spec_lengths, | |
| wav_padded, | |
| wav_lengths, | |
| ) | |
| """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.hparams = hparams | |
| self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) | |
| self.text_cleaners = hparams.text_cleaners | |
| 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.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) | |
| self.cleaned_text = getattr(hparams, "cleaned_text", False) | |
| self.add_blank = hparams.add_blank | |
| self.min_text_len = getattr(hparams, "min_text_len", 1) | |
| self.max_text_len = getattr(hparams, "max_text_len", 190) | |
| self.min_audio_len = getattr(hparams, "min_audio_len", 8192) | |
| random.seed(1234) | |
| random.shuffle(self.audiopaths_sid_text) | |
| self._filter() | |
| def _filter(self): | |
| """ | |
| Filter text & store spec lengths | |
| """ | |
| # Store spectrogram lengths for Bucketing | |
| # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) | |
| # spec_length = wav_length // hop_length | |
| audiopaths_sid_text_new = [] | |
| lengths = [] | |
| for audiopath, sid, text in self.audiopaths_sid_text: | |
| if not os.path.isfile(audiopath): | |
| continue | |
| if self.min_text_len <= len(text) and len(text) <= self.max_text_len: | |
| audiopaths_sid_text_new.append([audiopath, sid, text]) | |
| length = os.path.getsize(audiopath) // (2 * self.hop_length) | |
| if length < self.min_audio_len // self.hop_length: | |
| continue | |
| lengths.append(length) | |
| self.audiopaths_sid_text = audiopaths_sid_text_new | |
| self.lengths = lengths | |
| print( | |
| len(self.lengths) | |
| ) # if we use large corpus dataset, we can check how much time it takes. | |
| def get_audio_text_speaker_pair(self, audiopath_sid_text): | |
| # separate filename, speaker_id and text | |
| audiopath, sid, text = ( | |
| audiopath_sid_text[0], | |
| audiopath_sid_text[1], | |
| audiopath_sid_text[2], | |
| ) | |
| text = self.get_text(text) | |
| spec, wav = self.get_audio(audiopath) | |
| sid = self.get_sid(sid) | |
| return (text, spec, wav, sid) | |
| def get_audio(self, filename): | |
| # TODO : if linear spec exists convert to mel from existing linear spec | |
| 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 / self.max_wav_value | |
| audio_norm = audio_norm.unsqueeze(0) | |
| spec_filename = filename.replace(".wav", ".spec.pt") | |
| if self.use_mel_spec_posterior: | |
| spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") | |
| if os.path.exists(spec_filename): | |
| spec = torch.load(spec_filename) | |
| else: | |
| if self.use_mel_spec_posterior: | |
| """TODO : (need verification) | |
| if linear spec exists convert to | |
| mel from existing linear spec (uncomment below lines)""" | |
| # if os.path.exists(filename.replace(".wav", ".spec.pt")): | |
| # # spec, n_fft, num_mels, sampling_rate, fmin, fmax | |
| # spec = spec_to_mel_torch( | |
| # torch.load(filename.replace(".wav", ".spec.pt")), | |
| # self.filter_length, self.n_mel_channels, self.sampling_rate, | |
| # self.hparams.mel_fmin, self.hparams.mel_fmax) | |
| 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 = torch.squeeze(spec, 0) | |
| torch.save(spec, spec_filename) | |
| return spec, audio_norm | |
| def get_text(self, text): | |
| if self.cleaned_text: | |
| text_norm = cleaned_text_to_sequence(text) | |
| else: | |
| text_norm = text_to_sequence(text, self.text_cleaners) | |
| if self.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def get_sid(self, sid): | |
| sid = torch.LongTensor([int(sid)]) | |
| return sid | |
| def __getitem__(self, index): | |
| return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) | |
| def __len__(self): | |
| return len(self.audiopaths_sid_text) | |
| class TextAudioSpeakerCollate: | |
| """Zero-pads model inputs and targets""" | |
| def __init__(self, return_ids=False): | |
| self.return_ids = return_ids | |
| def __call__(self, batch): | |
| """Collate's training batch from normalized text, audio and speaker identities | |
| PARAMS | |
| ------ | |
| batch: [text_normalized, spec_normalized, wav_normalized, sid] | |
| """ | |
| # 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 | |
| ) | |
| max_text_len = max([len(x[0]) for x in batch]) | |
| max_spec_len = max([x[1].size(1) for x in batch]) | |
| max_wav_len = max([x[2].size(1) for x in batch]) | |
| text_lengths = torch.LongTensor(len(batch)) | |
| spec_lengths = torch.LongTensor(len(batch)) | |
| wav_lengths = torch.LongTensor(len(batch)) | |
| sid = torch.LongTensor(len(batch)) | |
| text_padded = torch.LongTensor(len(batch), max_text_len) | |
| spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) | |
| wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) | |
| text_padded.zero_() | |
| spec_padded.zero_() | |
| wav_padded.zero_() | |
| for i in range(len(ids_sorted_decreasing)): | |
| row = batch[ids_sorted_decreasing[i]] | |
| text = row[0] | |
| text_padded[i, : text.size(0)] = text | |
| text_lengths[i] = text.size(0) | |
| spec = row[1] | |
| spec_padded[i, :, : spec.size(1)] = spec | |
| spec_lengths[i] = spec.size(1) | |
| wav = row[2] | |
| wav_padded[i, :, : wav.size(1)] = wav | |
| wav_lengths[i] = wav.size(1) | |
| sid[i] = row[3] | |
| if self.return_ids: | |
| return ( | |
| text_padded, | |
| text_lengths, | |
| spec_padded, | |
| spec_lengths, | |
| wav_padded, | |
| wav_lengths, | |
| sid, | |
| ids_sorted_decreasing, | |
| ) | |
| return ( | |
| text_padded, | |
| text_lengths, | |
| spec_padded, | |
| spec_lengths, | |
| wav_padded, | |
| wav_lengths, | |
| sid, | |
| ) | |
| 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)): | |
| 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, 0, -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): | |
| # 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 | |
| def __len__(self): | |
| return self.num_samples // self.batch_size | |