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Updated paired to randomly index data, offsetting memory costs and speeding up initialization

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This commit is contained in:
James Betker 2022-01-16 21:09:22 -07:00
parent 37e4e737b5
commit 7331862755
1 changed files with 91 additions and 74 deletions
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165
codes/data/audio/paired_voice_audio_dataset.py
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@ -15,49 +15,39 @@ 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 parse_libri(line, base_path, split="|"):
fpt = line.strip().split(split)
fpt[0] = os.path.join(base_path, fpt[0])
return fpt
def load_tsv_aligned_codes(filename):
with open(filename, encoding='utf-8') as f:
components = [line.strip().split('\t') for line in f]
base = os.path.dirname(filename)
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)
filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0], convert_string_list_to_tensor(component[2])] for component in components]
return filepaths_and_text
def parse_tsv(line, base_path):
fpt = line.strip().split('\t')
return os.path.join(base_path, f'{fpt[1]}'), fpt[0]
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 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])
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
def parse_mozilla_cv(line, base_path):
components = line.strip().split('\t')
return os.path.join(base_path, f'clips/{components[1]}'), components[2]
def parse_voxpopuli(line, base_path):
line = line.strip().split('\t')
file, raw_text, norm_text, speaker_id, split, gender = line
year = file[:4]
return os.path.join(base_path, year, f'{file}.ogg.wav'), raw_text
class CharacterTokenizer:
@ -70,14 +60,16 @@ class CharacterTokenizer:
class TextWavLoader(torch.utils.data.Dataset):
def __init__(self, hparams):
self.path = hparams['path']
if not isinstance(self.path, list):
self.path = [self.path]
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)
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.fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
if not isinstance(self.fetcher_mode, list):
self.fetcher_mode = [self.fetcher_mode]
assert len(self.paths) == len(self.fetcher_mode)
self.load_conditioning = opt_get(hparams, ['load_conditioning'], False)
self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 1)
@ -85,25 +77,8 @@ class TextWavLoader(torch.utils.data.Dataset):
self.debug_failures = opt_get(hparams, ['debug_loading_failures'], False)
self.load_aligned_codes = opt_get(hparams, ['load_aligned_codes'], False)
self.aligned_codes_to_audio_ratio = opt_get(hparams, ['aligned_codes_ratio'], 443)
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_aligned_codes if self.load_aligned_codes else 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)
if self.max_wav_len is not None:
self.max_aligned_codes = self.max_wav_len // self.aligned_codes_to_audio_ratio
@ -134,23 +109,64 @@ class TextWavLoader(torch.utils.data.Dataset):
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]
fm = self.fetcher_mode[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)
if fm == 'lj' or fm == 'libritts':
return parse_libri(l2, base_path)
elif fm == 'tsv':
return parse_tsv_aligned_codes(l2, base_path) if self.load_aligned_codes else parse_tsv(l2, base_path)
elif fm == 'mozilla_cv':
assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv
return parse_mozilla_cv(l2, base_path)
elif fm == 'voxpopuli':
assert not self.load_conditioning # Conditioning inputs are incompatible with voxpopuli
return parse_voxpopuli(l2, base_path)
else:
raise NotImplementedError()
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(self.audiopaths_and_text[index])
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(self.audiopaths_and_text[index][0], self.conditioning_length, self.sample_rate,
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 {self.audiopaths_and_text[index][0]} {sys.exc_info()}")
print(f"error loading {apt[0]} {sys.exc_info()}")
return self[(index+1) % len(self)]
if self.load_aligned_codes:
aligned_codes = self.audiopaths_and_text[index][2]
aligned_codes = apt[2]
actually_skipped_items = self.skipped_items
self.skipped_items = 0
@ -189,7 +205,7 @@ class TextWavLoader(torch.utils.data.Dataset):
return res
def __len__(self):
return len(self.audiopaths_and_text)
return self.total_size_bytes // 1000 # 1000 cuts down a TSV file to the actual length pretty well, but doesn't work with the other formats.
class PairedVoiceDebugger:
@ -224,22 +240,23 @@ class PairedVoiceDebugger:
if __name__ == '__main__':
batch_sz = 8
batch_sz = 16
params = {
'mode': 'paired_voice_audio',
'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
'fetcher_mode': ['tsv'],
#'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
'path': ['Y:\\bigasr_dataset\\mozcv\\en\\train.tsv'],
'fetcher_mode': ['mozilla_cv'],
'phase': 'train',
'n_workers': 0,
'batch_size': batch_sz,
'max_wav_length': 255995,
'max_text_length': 200,
'sample_rate': 22050,
'load_conditioning': True,
'load_conditioning': False,
'num_conditioning_candidates': 2,
'conditioning_length': 44000,
'use_bpe_tokenizer': True,
'load_aligned_codes': True,
'load_aligned_codes': False,
}
from data import create_dataset, create_dataloader
@ -256,7 +273,7 @@ if __name__ == '__main__':
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
#save(b, i, ib, 'wav')
#if i > 5:
# break