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