forked from mrq/DL-Art-School
77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
import re
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import datasets
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from tokenizers import Tokenizer
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from tokenizers.models import BPE
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from tokenizers.pre_tokenizers import Whitespace
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from tokenizers.trainers import BpeTrainer
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from data.audio.paired_voice_audio_dataset import load_mozilla_cv, load_voxpopuli, load_tsv
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from models.tacotron2.taco_utils import load_filepaths_and_text
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def build_text_file_from_priors(priors, output):
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with open(output, 'w', encoding='utf-8') as out:
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for p, fm in priors:
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if fm == 'lj' or fm == 'libritts':
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fetcher_fn = load_filepaths_and_text
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elif fm == 'tsv':
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fetcher_fn = load_tsv
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elif fm == 'mozilla_cv':
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fetcher_fn = load_mozilla_cv
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elif fm == 'voxpopuli':
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fetcher_fn = load_voxpopuli
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else:
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raise NotImplementedError()
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apt = fetcher_fn(p)
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for path, text in apt:
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out.write(text + "\n")
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out.flush()
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def train():
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with open('all_texts.txt', 'r', encoding='utf-8') as at:
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ttsd = at.readlines()
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bcd = datasets.load_dataset('bookcorpus', cache_dir='Z:\\huggingface_datasets\\cache')['train']
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wkd = datasets.load_dataset('wikipedia', '20200501.en', cache_dir='Z:\\huggingface_datasets\\cache')['train']
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allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\'\?—ʼ]+$')
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def preprocess_word(word, report=False):
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word = word.strip().lower()
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if not bool(allowed_characters_re.match(word)):
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if report and word:
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print(f"REPORTING: '{word}'")
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return ''
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return word
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def batch_iterator(batch_size=1000):
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print("Processing ASR texts.")
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for i in range(0, len(ttsd), batch_size):
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yield [preprocess_word(t, True) for t in ttsd[i:i+batch_size]]
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print("Processing bookcorpus.")
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for i in range(0, len(bcd), batch_size):
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yield [preprocess_word(t) for t in bcd[i:i+batch_size]['text']]
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print("Processing wikipedia.")
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for i in range(0, len(wkd), batch_size):
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yield [preprocess_word(t) for t in wkd[i:i+batch_size]['text']]
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trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=9999)
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tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
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tokenizer.pre_tokenizer = Whitespace()
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tokenizer.train_from_iterator(batch_iterator(), trainer, length=len(ttsd)+len(bcd)+len(wkd))
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tokenizer.save('gpt_tts_tokenizer.json')
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if __name__ == '__main__':
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'''
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build_text_file_from_priors([('Y:\\bigasr_dataset\\libritts\\train-all.txt', 'libritts'),
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('Y:\\bigasr_dataset\\libritts\\test-clean_list.txt', 'libritts'),
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#('Y:\\bigasr_dataset\\voxpopuli\\audio\\transcribed_data\\en\\asr_en.tsv', 'voxpopuli'),
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('Y:\\bigasr_dataset\\voxpopuli\\audio\\transcribed_data\\en\\asr_train.tsv', 'voxpopuli'),
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('Y:\\clips\\books1-transcribed.tsv', 'tsv'),
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('Y:\\clips\\books2-transcribed.tsv', 'tsv'),
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('Y:\\clips\\podcasts-0-transcribed.tsv', 'tsv')], 'all_texts.txt')
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'''
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train() |