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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 text import symbols |
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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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return text |
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def g2p(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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if __name__ == "__main__": |
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print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) |
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