import os import random import audio2numpy import numpy as np import torch import torch.utils.data import torch.nn.functional as F from tqdm import tqdm import models.tacotron2.layers as layers from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text from models.tacotron2.text import text_to_sequence from utils.util import opt_get def load_mozilla_cv(filename): with open(filename, encoding='utf-8') as f: components = [line.strip().split('\t') for line in f][1:] # First line is the header base = os.path.dirname(filename) filepaths_and_text = [[os.path.join(base, f'clips/{component[1]}'), component[2]] for component in components] return filepaths_and_text def load_voxpopuli(filename): with open(filename, encoding='utf-8') as f: lines = [line.strip().split('\t') for line in f][1:] # First line is the header base = os.path.dirname(filename) filepaths_and_text = [] for line in lines: if len(line) == 0: continue file, raw_text, norm_text, speaker_id, split, gender = line year = file[:4] filepaths_and_text.append([os.path.join(base, year, f'{file}.ogg'), raw_text]) return filepaths_and_text class TextMelLoader(torch.utils.data.Dataset): """ 1) loads audio,text pairs 2) normalizes text and converts them to sequences of one-hot vectors 3) computes mel-spectrograms from audio files. """ def __init__(self, hparams): self.path = hparams['path'] if not isinstance(self.path, list): self.path = [self.path] fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj') if not isinstance(fetcher_mode, list): fetcher_mode = [fetcher_mode] assert len(self.path) == len(fetcher_mode) self.audiopaths_and_text = [] for p, fm in zip(self.path, fetcher_mode): if fm == 'lj' or fm == 'libritts': fetcher_fn = load_filepaths_and_text elif fm == 'mozilla_cv': fetcher_fn = load_mozilla_cv elif fm == 'voxpopuli': fetcher_fn = load_voxpopuli else: raise NotImplementedError() self.audiopaths_and_text.extend(fetcher_fn(p)) self.text_cleaners = hparams.text_cleaners self.sampling_rate = hparams.sampling_rate self.load_mel_from_disk = opt_get(hparams, ['load_mel_from_disk'], False) self.return_wavs = opt_get(hparams, ['return_wavs'], False) self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate) assert not (self.load_mel_from_disk and self.return_wavs) self.stft = layers.TacotronSTFT( hparams.filter_length, hparams.hop_length, hparams.win_length, hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin, hparams.mel_fmax) random.seed(hparams.seed) random.shuffle(self.audiopaths_and_text) self.max_mel_len = opt_get(hparams, ['max_mel_length'], None) self.max_text_len = opt_get(hparams, ['max_text_length'], None) # If needs_collate=False, all outputs will be aligned and padded at maximum length. self.needs_collate = opt_get(hparams, ['needs_collate'], True) if not self.needs_collate: assert self.max_mel_len is not None and self.max_text_len is not None def get_mel_text_pair(self, audiopath_and_text): # separate filename and text audiopath, text = audiopath_and_text[0], audiopath_and_text[1] text_seq = self.get_text(text) mel = self.get_mel(audiopath) return (text_seq, mel, text, audiopath_and_text[0]) def get_mel(self, filename): if self.load_mel_from_disk and os.path.exists(f'{filename}_mel.npy'): melspec = torch.from_numpy(np.load(f'{filename}_mel.npy')) assert melspec.size(0) == self.stft.n_mel_channels, ( 'Mel dimension mismatch: given {}, expected {}'.format(melspec.size(0), self.stft.n_mel_channels)) else: if filename.endswith('.wav'): audio, sampling_rate = load_wav_to_torch(filename) else: audio, sampling_rate = audio2numpy.audio_from_file(filename) audio = torch.tensor(audio) if sampling_rate != self.input_sample_rate: if sampling_rate < self.input_sample_rate: print(f'{filename} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.input_sample_rate}. This is not a good idea.') audio_norm = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.input_sample_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze() else: audio_norm = audio if audio_norm.std() > 1: print(f"Something is very wrong with the given audio. std_dev={audio_norm.std()}. file={filename}") return None audio_norm.clip_(-1, 1) audio_norm = audio_norm.unsqueeze(0) audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False) if self.input_sample_rate != self.sampling_rate: ratio = self.sampling_rate / self.input_sample_rate audio_norm = torch.nn.functional.interpolate(audio_norm.unsqueeze(0), scale_factor=ratio, mode='area').squeeze(0) if self.return_wavs: melspec = audio_norm else: melspec = self.stft.mel_spectrogram(audio_norm) melspec = torch.squeeze(melspec, 0) return melspec def get_text(self, text): text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners)) return text_norm def __getitem__(self, index): tseq, mel, text, path = self.get_mel_text_pair(self.audiopaths_and_text[index]) if mel is None or \ (self.max_mel_len is not None and mel.shape[-1] > self.max_mel_len) or \ (self.max_text_len is not None and tseq.shape[0] > self.max_text_len): #if mel is not None: # print(f"Exception {index} mel_len:{mel.shape[-1]} text_len:{tseq.shape[0]} fname: {path}") # It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result. rv = random.randint(0,len(self)-1) return self[rv] orig_output = mel.shape[-1] orig_text_len = tseq.shape[0] if not self.needs_collate: if mel.shape[-1] != self.max_mel_len: mel = F.pad(mel, (0, self.max_mel_len - mel.shape[-1])) if tseq.shape[0] != self.max_text_len: tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0])) return { 'real_text': text, 'padded_text': tseq, 'input_lengths': torch.tensor(orig_text_len, dtype=torch.long), 'padded_mel': mel, 'output_lengths': torch.tensor(orig_output, dtype=torch.long), 'filenames': path } return tseq, mel, path, text def __len__(self): return len(self.audiopaths_and_text) class TextMelCollate(): """ Zero-pads model inputs and targets based on number of frames per setep """ def __init__(self, n_frames_per_step): self.n_frames_per_step = n_frames_per_step def __call__(self, batch): """Collate's training batch from normalized text and mel-spectrogram PARAMS ------ batch: [text_normalized, mel_normalized, filename] """ # Right zero-pad all one-hot text sequences to max input length input_lengths, ids_sorted_decreasing = torch.sort( torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True) max_input_len = input_lengths[0] text_padded = torch.LongTensor(len(batch), max_input_len) text_padded.zero_() filenames = [] real_text = [] for i in range(len(ids_sorted_decreasing)): text = batch[ids_sorted_decreasing[i]][0] text_padded[i, :text.size(0)] = text filenames.append(batch[ids_sorted_decreasing[i]][2]) real_text.append(batch[ids_sorted_decreasing[i]][3]) # Right zero-pad mel-spec num_mels = batch[0][1].size(0) max_target_len = max([x[1].size(1) for x in batch]) if max_target_len % self.n_frames_per_step != 0: max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step assert max_target_len % self.n_frames_per_step == 0 # include mel padded and gate padded mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len) mel_padded.zero_() gate_padded = torch.FloatTensor(len(batch), max_target_len) gate_padded.zero_() output_lengths = torch.LongTensor(len(batch)) for i in range(len(ids_sorted_decreasing)): mel = batch[ids_sorted_decreasing[i]][1] mel_padded[i, :, :mel.size(1)] = mel gate_padded[i, mel.size(1)-1:] = 1 output_lengths[i] = mel.size(1) return { 'padded_text': text_padded, 'input_lengths': input_lengths, 'padded_mel': mel_padded, 'padded_gate': gate_padded, 'output_lengths': output_lengths, 'filenames': filenames, 'real_text': real_text, } def save_mel_buffer_to_file(mel, path): np.save(path, mel.cpu().numpy()) def dump_mels_to_disk(): params = { 'mode': 'nv_tacotron', 'path': ['Z:\\voxpopuli\\audio\\transcribed_data\\en\\asr_test.tsv'], 'fetcher_mode': ['voxpopuli'], 'phase': 'train', 'n_workers': 0, 'batch_size': 1, 'needs_collate': True, 'max_mel_length': 4000, 'max_text_length': 600, #'return_wavs': True, #'input_sample_rate': 22050, #'sampling_rate': 8000 } output_path = 'D:\\dlas\\results\\mozcv_mels' os.makedirs(os.path.join(output_path, 'clips'), exist_ok=True) from data import create_dataset, create_dataloader ds, c = create_dataset(params, return_collate=True) dl = create_dataloader(ds, params, collate_fn=c) for i, b in tqdm(enumerate(dl)): mels = b['padded_mel'] fnames = b['filenames'] for j, fname in enumerate(fnames): save_mel_buffer_to_file(mels[j], f'{os.path.join(output_path, fname)}_mel.npy') if __name__ == '__main__': dump_mels_to_disk() ''' params = { 'mode': 'nv_tacotron', 'path': 'E:\\audio\\MozillaCommonVoice\\en\\train.tsv', 'phase': 'train', 'n_workers': 12, 'batch_size': 32, 'fetcher_mode': 'mozilla_cv', 'needs_collate': False, 'max_mel_length': 800, 'max_text_length': 200, #'return_wavs': True, #'input_sample_rate': 22050, #'sampling_rate': 8000 } from data import create_dataset, create_dataloader ds, c = create_dataset(params, return_collate=True) dl = create_dataloader(ds, params, collate_fn=c) i = 0 m = None for k in range(1000): for i, b in tqdm(enumerate(dl)): continue pm = b['padded_mel'] pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1])) m = pm if m is None else torch.cat([m, pm], dim=0) print(m.mean(), m.std()) '''