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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 = '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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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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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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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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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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