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