import os import numpy as np import torch import torch.utils.data from mel_processing import spectrogram_torch from utils import load_filepaths_and_text, load_wav_to_torch class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset): """ Dataset that loads text and audio pairs. Args: hparams: Hyperparameters. """ def __init__(self, hparams): self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files) self.max_wav_value = hparams.max_wav_value self.sample_rate = hparams.sample_rate self.filter_length = hparams.filter_length self.hop_length = hparams.hop_length self.win_length = hparams.win_length self.sample_rate = hparams.sample_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): """ Filters audio paths and text pairs based on text 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) // (3 * self.hop_length)) self.audiopaths_and_text = audiopaths_and_text_new self.lengths = lengths def get_sid(self, sid): """ Converts speaker ID to a LongTensor. Args: sid (str): Speaker ID. """ try: sid = torch.LongTensor([int(sid)]) except ValueError as error: print(f"Error converting speaker ID '{sid}' to integer. Exception: {error}") sid = torch.LongTensor([0]) return sid def get_audio_text_pair(self, audiopath_and_text): """ Loads and processes audio and text data for a single pair. Args: audiopath_and_text (list): List containing audio path, text, pitch, pitchf, and speaker ID. """ 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] 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, :] pitch = pitch[:len_min] pitchf = pitchf[:len_min] return (spec, wav, phone, pitch, pitchf, dv) def get_labels(self, phone, pitch, pitchf): """ Loads and processes phoneme, pitch, and pitchf labels. Args: phone (str): Path to phoneme label file. pitch (str): Path to pitch label file. pitchf (str): Path to pitchf label file. """ 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) 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): """ Loads and processes audio data. Args: filename (str): Path to audio file. """ audio, sample_rate = load_wav_to_torch(filename) if sample_rate != self.sample_rate: raise ValueError( "{} SR doesn't match target {} SR".format(sample_rate, self.sample_rate) ) audio_norm = audio 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 Exception as error: print(f"An error occurred getting spec from {spec_filename}: {error}") spec = spectrogram_torch( audio_norm, self.filter_length, 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.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): """ Returns a single audio-text pair. Args: index (int): Index of the data sample. """ return self.get_audio_text_pair(self.audiopaths_and_text[index]) def __len__(self): """ Returns the length of the dataset. """ return len(self.audiopaths_and_text) class TextAudioCollateMultiNSFsid: """ Collates text and audio data for training. Args: return_ids (bool, optional): Whether to return sample IDs. Defaults to False. """ def __init__(self, return_ids=False): self.return_ids = return_ids def __call__(self, batch): """ Collates a batch of data samples. Args: batch (list): List of data samples. """ _, 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] ) 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_() 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 sid[i] = row[5] return ( phone_padded, phone_lengths, pitch_padded, pitchf_padded, spec_padded, spec_lengths, wave_padded, wave_lengths, sid, ) class TextAudioLoader(torch.utils.data.Dataset): """ Dataset that loads text and audio pairs. Args: hparams: Hyperparameters. """ def __init__(self, hparams): self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files) self.max_wav_value = hparams.max_wav_value self.sample_rate = hparams.sample_rate self.filter_length = hparams.filter_length self.hop_length = hparams.hop_length self.win_length = hparams.win_length self.sample_rate = hparams.sample_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): """ Filters audio paths and text pairs based on text length. """ audiopaths_and_text_new = [] lengths = [] for entry in self.audiopaths_and_text: if len(entry) >= 3: audiopath, text, dv = entry[:3] 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) // (3 * self.hop_length)) self.audiopaths_and_text = audiopaths_and_text_new self.lengths = lengths def get_sid(self, sid): """ Converts speaker ID to a LongTensor. Args: sid (str): Speaker ID. """ try: sid = torch.LongTensor([int(sid)]) except ValueError as error: print(f"Error converting speaker ID '{sid}' to integer. Exception: {error}") sid = torch.LongTensor([0]) return sid def get_audio_text_pair(self, audiopath_and_text): """ Loads and processes audio and text data for a single pair. Args: audiopath_and_text (list): List containing audio path, text, and speaker ID. """ 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): """ Loads and processes phoneme labels. Args: phone (str): Path to phoneme label file. """ phone = np.load(phone) phone = np.repeat(phone, 2, axis=0) n_num = min(phone.shape[0], 900) phone = phone[:n_num, :] phone = torch.FloatTensor(phone) return phone def get_audio(self, filename): """ Loads and processes audio data. Args: filename (str): Path to audio file. """ audio, sample_rate = load_wav_to_torch(filename) if sample_rate != self.sample_rate: raise ValueError( "{} SR doesn't match target {} SR".format(sample_rate, self.sample_rate) ) audio_norm = audio 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 Exception as error: print(f"An error occurred getting spec from {spec_filename}: {error}") spec = spectrogram_torch( audio_norm, self.filter_length, 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.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): """ Returns a single audio-text pair. Args: index (int): Index of the data sample. """ return self.get_audio_text_pair(self.audiopaths_and_text[index]) def __len__(self): """ Returns the length of the dataset. """ return len(self.audiopaths_and_text) class TextAudioCollate: """ Collates text and audio data for training. Args: return_ids (bool, optional): Whether to return sample IDs. Defaults to False. """ def __init__(self, return_ids=False): self.return_ids = return_ids def __call__(self, batch): """ Collates a batch of data samples. Args: batch (list): List of data samples. """ _, 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): """ Distributed sampler that groups data into buckets based on length. Args: dataset (torch.utils.data.Dataset): Dataset to sample from. batch_size (int): Batch size. boundaries (list): List of length boundaries for buckets. num_replicas (int, optional): Number of processes participating in distributed training. Defaults to None. rank (int, optional): Rank of the current process. Defaults to None. shuffle (bool, optional): Whether to shuffle the data. Defaults to True. """ 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): """ Creates buckets of data samples based on length. """ 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): """ Iterates over batches of data samples. """ 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] rem = num_samples_bucket - len_bucket ids_bucket = ( ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[: (rem % len_bucket)] ) 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): """ Performs binary search to find the bucket index for a given length. Args: x (int): Length to find the bucket for. lo (int, optional): Lower bound of the search range. Defaults to 0. hi (int, optional): Upper bound of the search range. Defaults to 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): """ Returns the length of the sampler. """ return self.num_samples // self.batch_size