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| import time | |
| import os | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.utils.data | |
| import commons | |
| from mel_processing import spectrogram_torch | |
| from utils import load_wav_to_torch, load_filepaths_and_text | |
| from text import text_to_sequence, cleaned_text_to_sequence | |
| """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.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.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_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 self.min_text_len <= len(text) and len(text) <= self.max_text_len: | |
| audiopaths_sid_text_new.append([audiopath, sid, text]) | |
| lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) | |
| self.audiopaths_sid_text = audiopaths_sid_text_new | |
| self.lengths = lengths | |
| 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) | |
| emo = torch.FloatTensor(np.load(audiopath+".emo.npy")) | |
| return (text, spec, wav, sid, emo) | |
| 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 / self.max_wav_value | |
| audio_norm = audio_norm.unsqueeze(0) | |
| spec_filename = filename.replace(".wav", ".spec.pt") | |
| if os.path.exists(spec_filename): | |
| spec = torch.load(spec_filename) | |
| 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) | |
| emo = torch.FloatTensor(len(batch), 1024) | |
| text_padded.zero_() | |
| spec_padded.zero_() | |
| wav_padded.zero_() | |
| emo.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] | |
| emo[i, :] = row[4] | |
| 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,emo | |
| 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 | |