| 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 = 'dccuchile/bert-base-spanish-wwm-uncased' |
| if not os.path.exists(model_id): |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| tokenizer.save_pretrained(model_id) |
| else: |
| tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=f"./{model_id}") |
|
|
| def g2p(text, pad_start_end=True, tokenized=None): |
| if tokenized is None: |
| tokenized = tokenizer.tokenize(text) |
| |
| 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 = [] |
| |
| 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 |
| |
| |
|
|
| 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." |
| |
| 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) |
|
|
|
|
|
|
