import os,traceback import numpy as np import torch import torch.utils.data from mel_processing import spectrogram_torch from utils import load_wav_to_torch, load_filepaths_and_text class TextAudioLoaderMultiNSFsid(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.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_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.min_text_len = getattr(hparams, "min_text_len", 1) self.max_text_len = getattr(hparams, "max_text_len", 5000) 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, pitch,pitchf,dv 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, pitch,pitchf,dv]) lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) self.audiopaths_and_text = audiopaths_and_text_new self.lengths = lengths def get_sid(self, sid): sid = torch.LongTensor([int(sid)]) return sid def get_audio_text_pair(self, audiopath_and_text): # separate filename and text file = audiopath_and_text[0] phone = audiopath_and_text[1] pitch = audiopath_and_text[2] pitchf = audiopath_and_text[3] dv = audiopath_and_text[4] phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf) spec, wav = self.get_audio(file) dv=self.get_sid(dv) len_phone = phone.size()[0] len_spec = spec.size()[-1] # print(123,phone.shape,pitch.shape,spec.shape) if len_phone != len_spec: len_min = min(len_phone, len_spec) # amor len_wav = len_min * self.hop_length spec = spec[:, :len_min] wav = wav[:, :len_wav] phone = phone[:len_min, :] pitch = pitch[:len_min] pitchf = pitchf[:len_min] return (spec, wav, phone, pitch,pitchf,dv) def get_labels(self, phone, pitch,pitchf): phone = np.load(phone) phone = np.repeat(phone, 2, axis=0) pitch = np.load(pitch) pitchf = np.load(pitchf) n_num = min(phone.shape[0], 900) # DistributedBucketSampler # print(234,phone.shape,pitch.shape) phone = phone[:n_num, :] pitch = pitch[:n_num] pitchf = pitchf[:n_num] phone = torch.FloatTensor(phone) pitch = torch.LongTensor(pitch) pitchf = torch.FloatTensor(pitchf) return phone, pitch,pitchf 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): try: spec = torch.load(spec_filename) except: print (spec_filename,traceback.format_exc()) 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, _use_new_zipfile_serialization=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, _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: """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[0].size(1) for x in batch]), dim=0, descending=True ) max_spec_len = max([x[0].size(1) for x in batch]) max_wave_len = max([x[1].size(1) for x in batch]) spec_lengths = torch.LongTensor(len(batch)) wave_lengths = torch.LongTensor(len(batch)) spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len) wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len) spec_padded.zero_() wave_padded.zero_() max_phone_len = max([x[2].size(0) for x in batch]) phone_lengths = torch.LongTensor(len(batch)) phone_padded = torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1])#(spec, wav, phone, pitch) pitch_padded = torch.LongTensor(len(batch), max_phone_len) pitchf_padded = torch.FloatTensor(len(batch), max_phone_len) phone_padded.zero_() pitch_padded.zero_() pitchf_padded.zero_() # dv = torch.FloatTensor(len(batch), 256)#gin=256 sid = torch.LongTensor(len(batch)) 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 # dv[i] = row[5] sid[i] = row[5] return ( phone_padded, phone_lengths, pitch_padded, pitchf_padded, spec_padded, spec_lengths, wave_padded, wave_lengths, # dv sid ) 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.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_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.min_text_len = getattr(hparams, "min_text_len", 1) self.max_text_len = getattr(hparams, "max_text_len", 5000) 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,dv 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,dv]) lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) self.audiopaths_and_text = audiopaths_and_text_new self.lengths = lengths def get_sid(self, sid): sid = torch.LongTensor([int(sid)]) return sid def get_audio_text_pair(self, audiopath_and_text): # separate filename and text file = audiopath_and_text[0] phone = audiopath_and_text[1] dv = audiopath_and_text[2] phone = self.get_labels(phone) spec, wav = self.get_audio(file) dv=self.get_sid(dv) 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, :] return (spec, wav, phone,dv) def get_labels(self, phone): phone = np.load(phone) phone = np.repeat(phone, 2, axis=0) n_num = min(phone.shape[0], 900) # DistributedBucketSampler phone = phone[:n_num, :] phone = torch.FloatTensor(phone) return phone 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): try: spec = torch.load(spec_filename) except: print (spec_filename,traceback.format_exc()) 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, _use_new_zipfile_serialization=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, _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: """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[0].size(1) for x in batch]), dim=0, descending=True ) max_spec_len = max([x[0].size(1) for x in batch]) max_wave_len = max([x[1].size(1) for x in batch]) spec_lengths = torch.LongTensor(len(batch)) wave_lengths = torch.LongTensor(len(batch)) spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len) wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len) spec_padded.zero_() wave_padded.zero_() max_phone_len = max([x[2].size(0) for x in batch]) phone_lengths = torch.LongTensor(len(batch)) phone_padded = torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1]) phone_padded.zero_() sid = torch.LongTensor(len(batch)) 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] return ( phone_padded, phone_lengths, spec_padded, spec_lengths, wave_padded, wave_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, -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) 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