Owen Song
174 lines
4.9 KiB
Python
174 lines
4.9 KiB
Python
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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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_indices = [
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i for i, x in enumerate(phones_per_word) if x == min_tasks
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]
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chosen_index = min_indices[len(min_indices) // 2]
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phones_per_word[chosen_index] += 1
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return phones_per_word
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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", "S", "AH1", "EY2", "AE2", "EH0", "OW2", "UH0", "NG", "B",
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"G", "AY0", "M", "AA0", "F", "AO0", "ER2", "UH1", "IY1", "AH2",
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"DH", "IY0", "EY1", "IH0", "K", "N", "W", "IY2", "T", "AA1",
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"ER1", "EH2", "OY0", "UH2", "UW1", "Z", "AW2", "AW1", "V", "UW2",
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"AA2", "ER", "AW0", "UW0", "R", "OW1", "EH1", "ZH", "AE0", "IH2",
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"IH", "Y", "JH", "P", "AY1", "EY0", "OY2", "TH", "HH", "D",
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"ER0", "CH", "AO1", "AE1", "AO2", "OY1", "AY2", "IH1", "OW0", "L", "SH",
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}
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def map_phoneme(ph):
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rep_map = {
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"：": ",", "；": ",", "，": ",", "。": ".", "！": "!",
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"？": "?", "\n": ".", "·": ",", "、": ",", "...": "…", "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 parse_phoneme(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 parse_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 = parse_phoneme(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 normalize_text(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 grapheme_to_phoneme(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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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 = parse_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 = parse_phoneme(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 = [map_phoneme(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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