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.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 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_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 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 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'], 1)
self.conditioning_length = opt_get(hparams, ['conditioning_length'], 44100)
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
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'], True)
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 __getitem__(self, index):
self.skipped_items += 1
try:
tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index])
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,
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()}")
return self[(index+1) % len(self)]
if self.load_aligned_codes:
aligned_codes = self.audiopaths_and_text[index][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]
if wav.shape[-1] != self.max_wav_len:
wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1]))
if self.load_aligned_codes:
# 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,
'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
if self.load_aligned_codes:
res['aligned_codes'] = aligned_codes
return res
def __len__(self):
return len(self.audiopaths_and_text)
class PairedVoiceDebugger:
def __init__(self):
self.total_items = 0
self.loaded_items = 0
self.self_conditioning_items = 0
def get_state(self):
return {'total_items': self.total_items,
'loaded_items': self.loaded_items,
'self_conditioning_items': self.self_conditioning_items}
def load_state(self, state):
if isinstance(state, dict):
self.total_items = opt_get(state, ['total_items'], 0)
self.loaded_items = opt_get(state, ['loaded_items'], 0)
self.self_conditioning_items = opt_get(state, ['self_conditioning_items'], 0)
def update(self, batch):
self.total_items += batch['wav'].shape[0]
self.loaded_items += batch['skipped_items'].sum().item()
if 'conditioning' in batch.keys():
self.self_conditioning_items += batch['conditioning_contains_self'].sum().item()
def get_debugging_map(self):
return {
'total_samples_loaded': self.total_items,
'percent_skipped_samples': (self.loaded_items - self.total_items) / self.loaded_items,
'percent_conditioning_is_self': self.self_conditioning_items / self.loaded_items,
}
if __name__ == '__main__':
batch_sz = 8
params = {
'mode': 'paired_voice_audio',
'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
'fetcher_mode': ['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': 2,
'conditioning_length': 44000,
'use_bpe_tokenizer': True,
'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