123 lines
3.1 KiB
Python
123 lines
3.1 KiB
Python
import pickle
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import os
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import re
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from . import symbols
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from .es_phonemizer import cleaner as es_cleaner
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from .es_phonemizer import es_to_ipa
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from transformers import AutoTokenizer
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def distribute_phone(n_phone, n_word):
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phones_per_word = [0] * n_word
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for task in range(n_phone):
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min_tasks = min(phones_per_word)
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min_index = phones_per_word.index(min_tasks)
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phones_per_word[min_index] += 1
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return phones_per_word
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def text_normalize(text):
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text = es_cleaner.spanish_cleaners(text)
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return text
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def post_replace_ph(ph):
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rep_map = {
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"：": ",",
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"；": ",",
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"，": ",",
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"。": ".",
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"！": "!",
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"？": "?",
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"\n": ".",
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"·": ",",
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"、": ",",
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"...": "…"
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}
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if ph in rep_map.keys():
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ph = rep_map[ph]
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if ph in symbols:
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return ph
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if ph not in symbols:
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ph = "UNK"
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return ph
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def refine_ph(phn):
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tone = 0
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if re.search(r"\d$", phn):
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tone = int(phn[-1]) + 1
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phn = phn[:-1]
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return phn.lower(), tone
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def refine_syllables(syllables):
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tones = []
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phonemes = []
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for phn_list in syllables:
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for i in range(len(phn_list)):
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phn = phn_list[i]
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phn, tone = refine_ph(phn)
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phonemes.append(phn)
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tones.append(tone)
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return phonemes, tones
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# model_id = 'bert-base-uncased'
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model_id = 'dccuchile/bert-base-spanish-wwm-uncased'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def g2p(text, pad_start_end=True, tokenized=None):
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if tokenized is None:
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tokenized = tokenizer.tokenize(text)
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# import pdb; pdb.set_trace()
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phs = []
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ph_groups = []
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for t in tokenized:
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if not t.startswith("#"):
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ph_groups.append([t])
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else:
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ph_groups[-1].append(t.replace("#", ""))
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phones = []
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tones = []
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word2ph = []
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# print(ph_groups)
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for group in ph_groups:
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w = "".join(group)
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phone_len = 0
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word_len = len(group)
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if w == '[UNK]':
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phone_list = ['UNK']
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else:
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phone_list = list(filter(lambda p: p != " ", es_to_ipa.es2ipa(w)))
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for ph in phone_list:
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phones.append(ph)
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tones.append(0)
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phone_len += 1
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aaa = distribute_phone(phone_len, word_len)
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word2ph += aaa
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# print(phone_list, aaa)
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# print('=' * 10)
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if pad_start_end:
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phones = ["_"] + phones + ["_"]
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tones = [0] + tones + [0]
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word2ph = [1] + word2ph + [1]
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return phones, tones, word2ph
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def get_bert_feature(text, word2ph, device=None):
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from text import spanish_bert
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return spanish_bert.get_bert_feature(text, word2ph, device=device)
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if __name__ == "__main__":
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text = "en nuestros tiempos estos dos pueblos ilustres empiezan a curarse, gracias sólo a la sana y vigorosa higiene de 1789."
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# print(text)
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text = text_normalize(text)
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print(text)
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phones, tones, word2ph = g2p(text)
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bert = get_bert_feature(text, word2ph)
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print(phones)
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print(len(phones), tones, sum(word2ph), bert.shape)
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