import os import os import random import torch import torch.nn.functional as F import torch.utils.data import torchaudio from munch import munchify from tokenizers import Tokenizer from tqdm import tqdm from transformers import GPT2TokenizerFast from data.audio.unsupervised_audio_dataset import load_audio from data.util import find_files_of_type, is_audio_file from models.tacotron2.taco_utils import load_filepaths_and_text from models.tacotron2.text import text_to_sequence, sequence_to_text from utils.util import opt_get def load_tsv(filename): with open(filename, encoding='utf-8') as f: components = [line.strip().split('\t') for line in f] base = os.path.dirname(filename) filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0]] for component in components] return filepaths_and_text 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 CharacterTokenizer: def encode(self, txt): return munchify({'ids': text_to_sequence(txt, ['english_cleaners'])}) def decode(self, seq): return sequence_to_text(seq) 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.load_conditioning = opt_get(hparams, ['load_conditioning'], False) self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 3) self.conditioning_length = opt_get(hparams, ['conditioning_length'], 44100) self.debug_failures = opt_get(hparams, ['debug_loading_failures'], False) 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 == 'tsv': fetcher_fn = load_tsv elif fm == 'mozilla_cv': assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv fetcher_fn = load_mozilla_cv elif fm == 'voxpopuli': assert not self.load_conditioning # Conditioning inputs are incompatible with 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 self.use_bpe_tokenizer = opt_get(hparams, ['use_bpe_tokenizer'], True) if self.use_bpe_tokenizer: self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/bpe_lowercase_asr_256.json')) else: self.tokenizer = CharacterTokenizer() 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): tokens = self.tokenizer.encode(text.strip().lower()).ids tokens = torch.IntTensor(tokens) if self.use_bpe_tokenizer: # Assert if any UNK,start tokens encountered. assert not torch.any(tokens == 1) # The stop token should always be sacred. assert not torch.any(tokens == 0) return tokens def load_conditioning_candidates(self, path): candidates = find_files_of_type('img', os.path.dirname(path), qualifier=is_audio_file)[0] assert len(candidates) < 50000 # Sanity check to ensure we aren't loading "related files" that aren't actually related. if len(candidates) == 0: print(f"No conditioning candidates found for {path} (not even the clip itself??)") raise NotImplementedError() # Sample with replacement. This can get repeats, but more conveniently handles situations where there are not enough candidates. related_clips = [] for k in range(self.conditioning_candidates): rel_clip = load_audio(random.choice(candidates), self.sample_rate) gap = rel_clip.shape[-1] - self.conditioning_length if gap < 0: rel_clip = F.pad(rel_clip, pad=(0, abs(gap))) elif gap > 0: rand_start = random.randint(0, gap) rel_clip = rel_clip[:, rand_start:rand_start+self.conditioning_length] related_clips.append(rel_clip) return torch.stack(related_clips, dim=0) def __getitem__(self, index): try: tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index]) cond = self.load_conditioning_candidates(self.audiopaths_and_text[index][0]) if self.load_conditioning else None except: if self.debug_failures: print(f"error loading {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])) res = { 'real_text': text, 'padded_text': tseq, 'text_lengths': torch.tensor(orig_text_len, dtype=torch.long), 'wav': wav, 'wav_lengths': torch.tensor(orig_output, dtype=torch.long), 'filenames': path } if self.load_conditioning: res['conditioning'] = cond return res return tseq, wav, path, text, cond 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 = [] conds = [] 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]) c = batch[ids_sorted_decreasing[i]][4] if c is not None: conds.append(c) # 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) res = { 'padded_text': text_padded, 'text_lengths': input_lengths, 'wav': wav_padded, 'wav_lengths': output_lengths, 'filenames': filenames, 'real_text': real_text, } if len(conds) > 0: res['conditioning'] = torch.stack(conds) return res if __name__ == '__main__': batch_sz = 8 params = { 'mode': 'paired_voice_audio', 'path': ['Z:\\clips\\podcasts-0-transcribed.tsv'], 'fetcher_mode': ['tsv'], 'phase': 'train', 'n_workers': 0, 'batch_size': batch_sz, 'needs_collate': False, 'max_wav_length': 255995, 'max_text_length': 200, 'sample_rate': 22050, 'load_conditioning': False, 'use_bpe_tokenizer': False, } 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 i, b in tqdm(enumerate(dl)): for ib in range(batch_sz): print(f"text_seq: {b['text_lengths'].max()}, speech_seq: {b['wav_lengths'].max()//1024}")