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This commit is contained in:
James Betker 2021-12-22 19:21:29 -07:00
parent 191e0130ee
commit b9de8a8eda
3 changed files with 9 additions and 6 deletions
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1
codes/data/audio/gpt_tts_tokenizer.json Normal file
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File diff suppressed because one or more lines are too long

4
codes/data/audio/paired_voice_audio_dataset.py
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@ -85,7 +85,7 @@ class TextWavLoader(torch.utils.data.Dataset):
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
self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/gpt_tts_tokenizer.json'))
self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/custom_lowercase_gptvoice_tokenizer.json'))
def get_wav_text_pair(self, audiopath_and_text):
# separate filename and text
@ -95,7 +95,7 @@ class TextWavLoader(torch.utils.data.Dataset):
return (text_seq, wav, text, audiopath_and_text[0])
def get_text(self, text):
tokens = self.tokenizer.encode(text.lower()).ids
tokens = self.tokenizer.encode(text.strip().lower()).ids
tokens = torch.IntTensor(tokens)
# Assert if any UNK,start,stop tokens encountered.
assert not torch.any(tokens == 0)

10
codes/data/audio/voice_tokenizer_builder.py
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@ -35,17 +35,19 @@ def train():
bcd = datasets.load_dataset('bookcorpus', cache_dir='Z:\\huggingface_datasets\\cache')['train']
wkd = datasets.load_dataset('wikipedia', '20200501.en', cache_dir='Z:\\huggingface_datasets\\cache')['train']
allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\']+$')
def preprocess_word(word):
word = word.lower()
allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\'\?—ʼ]+$')
def preprocess_word(word, report=False):
word = word.strip().lower()
if not bool(allowed_characters_re.match(word)):
if report and word:
print(f"REPORTING: '{word}'")
return ''
return word
def batch_iterator(batch_size=1000):
print("Processing ASR texts.")
for i in range(0, len(ttsd), batch_size):
yield [preprocess_word(t) for t in ttsd[i:i+batch_size]]
yield [preprocess_word(t, True) for t in ttsd[i:i+batch_size]]
print("Processing bookcorpus.")
for i in range(0, len(bcd), batch_size):