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import pickle
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import os
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import re
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from g2p_en import G2p
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from . import symbols
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from .english_utils.abbreviations import expand_abbreviations
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from .english_utils.time_norm import expand_time_english
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from .english_utils.number_norm import normalize_numbers
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from .japanese import distribute_phone
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from transformers import AutoTokenizer
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current_file_path = os.path.dirname(__file__)
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CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
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CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
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_g2p = G2p()
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arpa = {
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"AH0",
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"S",
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"AH1",
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"EY2",
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"AE2",
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"EH0",
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"OW2",
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"UH0",
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"NG",
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"B",
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"G",
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"AY0",
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"M",
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"AA0",
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"F",
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"AO0",
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"ER2",
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"UH1",
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"IY1",
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"AH2",
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"DH",
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"IY0",
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"EY1",
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"IH0",
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"K",
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"N",
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"W",
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"IY2",
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"T",
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"AA1",
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"ER1",
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"EH2",
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"OY0",
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"UH2",
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"UW1",
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"Z",
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"AW2",
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"AW1",
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"V",
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"UW2",
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"AA2",
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"ER",
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"AW0",
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"UW0",
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"R",
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"OW1",
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"EH1",
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"ZH",
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"AE0",
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"IH2",
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"IH",
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"Y",
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"JH",
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"P",
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"AY1",
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"EY0",
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"OY2",
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"TH",
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"HH",
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"D",
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"ER0",
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"CH",
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"AO1",
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"AE1",
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"AO2",
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"OY1",
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"AY2",
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"IH1",
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"OW0",
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"L",
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"SH",
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}
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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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"v": "V",
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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 read_dict():
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g2p_dict = {}
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start_line = 49
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with open(CMU_DICT_PATH) as f:
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line = f.readline()
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line_index = 1
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while line:
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if line_index >= start_line:
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line = line.strip()
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word_split = line.split(" ")
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word = word_split[0]
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syllable_split = word_split[1].split(" - ")
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g2p_dict[word] = []
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for syllable in syllable_split:
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phone_split = syllable.split(" ")
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g2p_dict[word].append(phone_split)
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line_index = line_index + 1
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line = f.readline()
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return g2p_dict
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def cache_dict(g2p_dict, file_path):
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with open(file_path, "wb") as pickle_file:
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pickle.dump(g2p_dict, pickle_file)
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def get_dict():
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if os.path.exists(CACHE_PATH):
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with open(CACHE_PATH, "rb") as pickle_file:
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g2p_dict = pickle.load(pickle_file)
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else:
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g2p_dict = read_dict()
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cache_dict(g2p_dict, CACHE_PATH)
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return g2p_dict
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eng_dict = get_dict()
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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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def text_normalize(text):
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text = text.lower()
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text = expand_time_english(text)
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text = normalize_numbers(text)
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text = expand_abbreviations(text)
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return text
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model_id = 'bert-base-uncased'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def g2p_old(text):
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tokenized = tokenizer.tokenize(text)
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phones = []
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tones = []
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words = re.split(r"([,;.\-\?\!\s+])", text)
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for w in words:
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if w.upper() in eng_dict:
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phns, tns = refine_syllables(eng_dict[w.upper()])
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phones += phns
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tones += tns
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else:
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phone_list = list(filter(lambda p: p != " ", _g2p(w)))
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for ph in phone_list:
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if ph in arpa:
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ph, tn = refine_ph(ph)
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phones.append(ph)
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tones.append(tn)
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else:
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phones.append(ph)
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tones.append(0)
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word2ph = [1 for i in phones]
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phones = [post_replace_ph(i) for i in phones]
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return phones, tones, word2ph
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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.upper() in eng_dict:
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phns, tns = refine_syllables(eng_dict[w.upper()])
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phones += phns
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tones += tns
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phone_len += len(phns)
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else:
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phone_list = list(filter(lambda p: p != " ", _g2p(w)))
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for ph in phone_list:
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if ph in arpa:
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ph, tn = refine_ph(ph)
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phones.append(ph)
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tones.append(tn)
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else:
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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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phones = [post_replace_ph(i) for i in phones]
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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 english_bert
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return english_bert.get_bert_feature(text, word2ph, device=device)
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if __name__ == "__main__":
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from text.english_bert import get_bert_feature
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text = "In this paper, we propose 1 DSPGAN, a N-F-T GAN-based universal vocoder."
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text = text_normalize(text)
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phones, tones, word2ph = g2p(text)
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import pdb; pdb.set_trace()
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bert = get_bert_feature(text, word2ph)
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print(phones, tones, word2ph, bert.shape)
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