import os import random import audio2numpy import numpy as np import torch import torch.utils.data import torch.nn.functional as F import torchaudio from tqdm import tqdm import models.tacotron2.layers as layers from data.audio.unsupervised_audio_dataset import load_audio 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.wav'), raw_text]) return filepaths_and_text class TextWavLoader(torch.utils.data.Dataset): 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.sample_rate = hparams.sample_rate random.seed(hparams.seed) random.shuffle(self.audiopaths_and_text) self.max_wav_len = opt_get(hparams, ['max_wav_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_wav_len is not None and self.max_text_len is not None def get_wav_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) wav = load_audio(audiopath, self.sample_rate) return (text_seq, wav, text, audiopath_and_text[0]) def get_text(self, text): text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners)) return text_norm def __getitem__(self, index): try: tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index]) except: print(f"error loadding {self.audiopaths_and_text[index][0]}") return self[index+1] if wav is None or \ (self.max_wav_len is not None and wav.shape[-1] > self.max_wav_len) or \ (self.max_text_len is not None and tseq.shape[0] > self.max_text_len): # Basically, this audio file is nonexistent or too long to be supported by the dataset. # 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. #if wav is not None: # print(f"Exception {index} wav_len:{wav.shape[-1]} text_len:{tseq.shape[0]} fname: {path}") rv = random.randint(0,len(self)-1) return self[rv] orig_output = wav.shape[-1] orig_text_len = tseq.shape[0] if not self.needs_collate: if wav.shape[-1] != self.max_wav_len: wav = F.pad(wav, (0, self.max_wav_len - wav.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), 'wav': wav, 'output_lengths': torch.tensor(orig_output, dtype=torch.long), 'filenames': path } return tseq, wav, 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 step """ def __call__(self, batch): """Collate's training batch from normalized text and wav PARAMS ------ batch: [text_normalized, wav, filename, text] """ # 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 wav num_wavs = batch[0][1].size(0) max_target_len = max([x[1].size(1) for x in batch]) # include mel padded and gate padded wav_padded = torch.FloatTensor(len(batch), num_wavs, max_target_len) wav_padded.zero_() output_lengths = torch.LongTensor(len(batch)) for i in range(len(ids_sorted_decreasing)): wav = batch[ids_sorted_decreasing[i]][1] wav_padded[i, :, :wav.size(1)] = wav output_lengths[i] = wav.size(1) return { 'padded_text': text_padded, 'input_lengths': input_lengths, 'wav': wav_padded, 'output_lengths': output_lengths, 'filenames': filenames, 'real_text': real_text, } if __name__ == '__main__': batch_sz = 32 params = { 'mode': 'nv_tacotron', 'path': 'E:\\audio\\MozillaCommonVoice\\en\\test.tsv', 'phase': 'train', 'n_workers': 0, 'batch_size': batch_sz, 'fetcher_mode': 'mozilla_cv', 'needs_collate': True, #'max_wav_length': 256000, #'max_text_length': 200, 'sample_rate': 22050, } 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)): w = b['wav'] for ib in range(batch_sz): print(f'{i} {ib} {b["real_text"][ib]}') torchaudio.save(f'{i}_clip_{ib}.wav', b['wav'][ib], ds.sample_rate)