text/spanish.py

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'
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)
    # 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)