210 lines
9.1 KiB
Python
210 lines
9.1 KiB
Python
import os
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import os
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import random
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import sys
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import torch
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import torch.nn.functional as F
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import torch.utils.data
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import torchaudio
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from tqdm import tqdm
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from data.audio.paired_voice_audio_dataset import CharacterTokenizer
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from data.audio.unsupervised_audio_dataset import load_audio, load_similar_clips
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from models.tacotron2.taco_utils import load_filepaths_and_text
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from models.tacotron2.text import text_to_sequence, sequence_to_text
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from utils.util import opt_get
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def parse_tsv_aligned_codes(line, base_path):
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fpt = line.strip().split('\t')
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def convert_string_list_to_tensor(strlist):
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if strlist.startswith('['):
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strlist = strlist[1:]
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if strlist.endswith(']'):
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strlist = strlist[:-1]
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as_ints = [int(s) for s in strlist.split(', ')]
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return torch.tensor(as_ints)
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return os.path.join(base_path, f'{fpt[1]}'), fpt[0], convert_string_list_to_tensor(fpt[2])
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class FastPairedVoiceDataset(torch.utils.data.Dataset):
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"""
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This dataset is derived from paired_voice_audio, but it only supports loading from TSV files generated from the
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ocotillo transcription engine, which includes alignment codes. To support the vastly larger TSV files, this dataset
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uses an indexing mechanism which randomly selects offsets within the translation file to seek to. The data returned
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is relative to these offsets.
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In practice, this means two things:
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1) Index {i} of this dataset means nothing: fetching from the same index will almost always return different data.
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As a result, this dataset should not be used for validation or test runs.
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2) This dataset has a slight bias for items with longer text or longer filenames.
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The upshot is that this dataset loads extremely quickly and consumes almost no system memory.
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"""
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def __init__(self, hparams):
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self.paths = hparams['path']
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if not isinstance(self.paths, list):
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self.paths = [self.paths]
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self.paths_size_bytes = [os.path.getsize(p) for p in self.paths]
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self.total_size_bytes = sum(self.paths_size_bytes)
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self.load_conditioning = opt_get(hparams, ['load_conditioning'], False)
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self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 1)
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self.conditioning_length = opt_get(hparams, ['conditioning_length'], 44100)
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self.debug_failures = opt_get(hparams, ['debug_loading_failures'], False)
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self.aligned_codes_to_audio_ratio = opt_get(hparams, ['aligned_codes_ratio'], 443)
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self.text_cleaners = hparams.text_cleaners
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self.sample_rate = hparams.sample_rate
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self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
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if self.max_wav_len is not None:
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self.max_aligned_codes = self.max_wav_len // self.aligned_codes_to_audio_ratio
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self.max_text_len = opt_get(hparams, ['max_text_length'], None)
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assert self.max_wav_len is not None and self.max_text_len is not None
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self.use_bpe_tokenizer = opt_get(hparams, ['use_bpe_tokenizer'], False)
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if self.use_bpe_tokenizer:
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from data.audio.voice_tokenizer import VoiceBpeTokenizer
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self.tokenizer = VoiceBpeTokenizer(opt_get(hparams, ['tokenizer_vocab'], '../experiments/bpe_lowercase_asr_256.json'))
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else:
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self.tokenizer = CharacterTokenizer()
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self.skipped_items = 0 # records how many items are skipped when accessing an index.
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def get_wav_text_pair(self, audiopath_and_text):
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# separate filename and text
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audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
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text_seq = self.get_text(text)
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wav = load_audio(audiopath, self.sample_rate)
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return (text_seq, wav, text, audiopath_and_text[0])
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def get_text(self, text):
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tokens = self.tokenizer.encode(text)
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tokens = torch.IntTensor(tokens)
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if self.use_bpe_tokenizer:
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# Assert if any UNK,start tokens encountered.
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assert not torch.any(tokens == 1)
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# The stop token should always be sacred.
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assert not torch.any(tokens == 0)
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return tokens
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def load_random_line(self, depth=0):
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assert depth < 10
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rand_offset = random.randint(0, self.total_size_bytes)
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for i in range(len(self.paths)):
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if rand_offset < self.paths_size_bytes[i]:
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break
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else:
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rand_offset -= self.paths_size_bytes[i]
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path = self.paths[i]
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with open(path, 'r', encoding='utf-8') as f:
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f.seek(rand_offset)
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# Read the rest of the line we seeked to, then the line after that.
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try: # This can fail when seeking to a UTF-8 escape byte.
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f.readline()
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except:
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return self.load_random_line(depth=depth + 1) # On failure, just recurse and try again.
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l2 = f.readline()
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if l2:
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try:
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base_path = os.path.dirname(path)
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return parse_tsv_aligned_codes(l2, base_path)
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except:
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print(f"error parsing random offset: {sys.exc_info()}")
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return self.load_random_line(depth=depth+1) # On failure, just recurse and try again.
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def __getitem__(self, index):
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self.skipped_items += 1
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apt = self.load_random_line()
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try:
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tseq, wav, text, path = self.get_wav_text_pair(apt)
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if text is None or len(text.strip()) == 0:
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raise ValueError
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cond, cond_is_self = load_similar_clips(apt[0], self.conditioning_length, self.sample_rate,
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n=self.conditioning_candidates) if self.load_conditioning else (None, False)
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except:
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if self.skipped_items > 100:
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raise # Rethrow if we have nested too far.
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if self.debug_failures:
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print(f"error loading {apt[0]} {sys.exc_info()}")
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return self[(index+1) % len(self)]
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aligned_codes = apt[2]
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actually_skipped_items = self.skipped_items
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self.skipped_items = 0
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if wav is None or \
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(self.max_wav_len is not None and wav.shape[-1] > self.max_wav_len) or \
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(self.max_text_len is not None and tseq.shape[0] > self.max_text_len):
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# Basically, this audio file is nonexistent or too long to be supported by the dataset.
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# 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.
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if self.debug_failures:
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print(f"error loading {path}: ranges are out of bounds; {wav.shape[-1]}, {tseq.shape[0]}")
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rv = random.randint(0,len(self)-1)
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return self[rv]
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orig_output = wav.shape[-1]
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orig_text_len = tseq.shape[0]
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orig_aligned_code_length = aligned_codes.shape[0]
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if wav.shape[-1] != self.max_wav_len:
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wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1]))
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# These codes are aligned to audio inputs, so make sure to pad them as well.
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aligned_codes = F.pad(aligned_codes, (0, self.max_aligned_codes-aligned_codes.shape[0]))
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if tseq.shape[0] != self.max_text_len:
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tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
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res = {
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'real_text': text,
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'padded_text': tseq,
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'aligned_codes': aligned_codes,
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'aligned_codes_lengths': orig_aligned_code_length,
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'text_lengths': torch.tensor(orig_text_len, dtype=torch.long),
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'wav': wav,
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'wav_lengths': torch.tensor(orig_output, dtype=torch.long),
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'filenames': path,
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'skipped_items': actually_skipped_items,
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}
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if self.load_conditioning:
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res['conditioning'] = cond
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res['conditioning_contains_self'] = cond_is_self
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return res
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def __len__(self):
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return self.total_size_bytes // 1000 # 1000 cuts down a TSV file to the actual length pretty well.
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if __name__ == '__main__':
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batch_sz = 16
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params = {
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'mode': 'fast_paired_voice_audio',
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'path': ['Y:\\libritts\\train-clean-360\\transcribed-w2v.tsv', 'Y:\\clips\\books1\\transcribed-w2v.tsv'],
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'phase': 'train',
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'n_workers': 0,
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'batch_size': batch_sz,
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'max_wav_length': 255995,
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'max_text_length': 200,
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'sample_rate': 22050,
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'load_conditioning': True,
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'num_conditioning_candidates': 1,
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'conditioning_length': 44000,
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'use_bpe_tokenizer': False,
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'load_aligned_codes': True,
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}
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from data import create_dataset, create_dataloader
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def save(b, i, ib, key, c=None):
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if c is not None:
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torchaudio.save(f'{i}_clip_{ib}_{key}_{c}.wav', b[key][ib][c], 22050)
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else:
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torchaudio.save(f'{i}_clip_{ib}_{key}.wav', b[key][ib], 22050)
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ds, c = create_dataset(params, return_collate=True)
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dl = create_dataloader(ds, params, collate_fn=c)
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i = 0
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m = None
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for i, b in tqdm(enumerate(dl)):
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for ib in range(batch_sz):
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print(f'{i} {ib} {b["real_text"][ib]}')
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save(b, i, ib, 'wav')
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if i > 5:
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break
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