import os import random import sys import numpy as np import torch import torch.utils.data from tqdm import tqdm import commons from config import config from mel_processing import mel_spectrogram_torch, spectrogram_torch from text import cleaned_text_to_sequence from common.log import logger from utils import load_filepaths_and_text, load_wav_to_torch """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.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.spk_map = hparams.spk2id self.hparams = hparams 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", 384) 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 = [] skipped = 0 logger.info("Init dataset...") for _id, spk, language, text, phones, tone, word2ph in tqdm( self.audiopaths_sid_text, file=sys.stdout ): audiopath = f"{_id}" if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len: phones = phones.split(" ") tone = [int(i) for i in tone.split(" ")] word2ph = [int(i) for i in word2ph.split(" ")] audiopaths_sid_text_new.append( [audiopath, spk, language, text, phones, tone, word2ph] ) lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) else: skipped += 1 logger.info( "skipped: " + str(skipped) + ", total: " + str(len(self.audiopaths_sid_text)) ) 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, language, text, phones, tone, word2ph = audiopath_sid_text bert, ja_bert, en_bert, phones, tone, language = self.get_text( text, word2ph, phones, tone, language, audiopath ) spec, wav = self.get_audio(audiopath) sid = torch.LongTensor([int(self.spk_map[sid])]) style_vec = torch.FloatTensor(np.load(f"{audiopath}.npy")) return ( phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert, style_vec, ) 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( filename, 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") try: spec = torch.load(spec_filename) except: if self.use_mel_spec_posterior: 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) if config.train_ms_config.spec_cache: torch.save(spec, spec_filename) return spec, audio_norm def get_text(self, text, word2ph, phone, tone, language_str, wav_path): phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) if self.add_blank: phone = commons.intersperse(phone, 0) tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 bert_path = wav_path.replace(".wav", ".bert.pt") try: bert_ori = torch.load(bert_path) assert bert_ori.shape[-1] == len(phone) except Exception as e: logger.warning("Bert load Failed") logger.warning(e) if language_str == "ZH": bert = bert_ori ja_bert = torch.zeros(1024, len(phone)) en_bert = torch.zeros(1024, len(phone)) elif language_str == "JP": bert = torch.zeros(1024, len(phone)) ja_bert = bert_ori en_bert = torch.zeros(1024, len(phone)) elif language_str == "EN": bert = torch.zeros(1024, len(phone)) ja_bert = torch.zeros(1024, len(phone)) en_bert = bert_ori phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) language = torch.LongTensor(language) return bert, ja_bert, en_bert, phone, tone, language 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) tone_padded = torch.LongTensor(len(batch), max_text_len) language_padded = torch.LongTensor(len(batch), max_text_len) bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) style_vec = torch.FloatTensor(len(batch), 256) 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_() tone_padded.zero_() language_padded.zero_() spec_padded.zero_() wav_padded.zero_() bert_padded.zero_() ja_bert_padded.zero_() en_bert_padded.zero_() style_vec.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] tone = row[4] tone_padded[i, : tone.size(0)] = tone language = row[5] language_padded[i, : language.size(0)] = language bert = row[6] bert_padded[i, :, : bert.size(1)] = bert ja_bert = row[7] ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert en_bert = row[8] en_bert_padded[i, :, : en_bert.size(1)] = en_bert style_vec[i, :] = row[9] return ( text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded, ja_bert_padded, en_bert_padded, style_vec, ) 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() logger.info(f"Bucket info: {self.num_samples_per_bucket}") # logger.info( # f"Unused samples: {len(self.lengths) - sum(self.num_samples_per_bucket)}" # ) # ↑マイナスになることあるし、別にこれは使われないサンプル数ではないようだ…… # バケットの仕組みはよく分からない 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) try: for i in range(len(buckets) - 1, 0, -1): if len(buckets[i]) == 0: buckets.pop(i) self.boundaries.pop(i + 1) assert all(len(bucket) > 0 for bucket in buckets) # When one bucket is not traversed except Exception as e: logger.info("Bucket warning ", e) 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): # 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) if len_bucket == 0: continue 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