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