diff --git a/codes/data/audio/nv_tacotron_dataset.py b/codes/data/audio/nv_tacotron_dataset.py new file mode 100644 index 00000000..b8adf4b7 --- /dev/null +++ b/codes/data/audio/nv_tacotron_dataset.py @@ -0,0 +1,244 @@ +import os +import os +import random + +import torch +import torch.nn.functional as F +import torch.utils.data +import torchaudio +from tqdm import tqdm + +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 +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 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.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 + + 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 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: + 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': 'nv_tacotron', + 'path': ['Z:\\bigasr_dataset\\libritts\\test-clean_list.txt'], + 'fetcher_mode': ['libritts'], + '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': True, + 'num_conditioning_candidates': 3, + 'conditioning_length': 44100, + } + 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)): + if i > 5: + break + 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) + for c in range(3): + torchaudio.save(f'{i}_clip_{ib}_cond{c}.wav', b['conditioning'][ib, c], ds.sample_rate)