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123 lines
4.5 KiB
123 lines
4.5 KiB
# !/usr/bin/env python3 |
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# -*- coding: utf-8 -*- |
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# |
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# Copyright 2016-2099 Ailemon.net |
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# |
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# This file is part of ASRT Speech Recognition Tool. |
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# |
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# ASRT is free software: you can redistribute it and/or modify |
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# it under the terms of the GNU General Public License as published by |
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# the Free Software Foundation, either version 3 of the License, or |
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# (at your option) any later version. |
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# ASRT is distributed in the hope that it will be useful, |
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# but WITHOUT ANY WARRANTY; without even the implied warranty of |
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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# GNU General Public License for more details. |
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# |
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# You should have received a copy of the GNU General Public License |
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# along with ASRT. If not, see <https://www.gnu.org/licenses/>. |
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# ============================================================================ |
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""" |
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@author: nl8590687 |
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ASRT语音识别的语言模型 |
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基于N-Gram的语言模型 |
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""" |
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import os |
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from utils.ops import get_symbol_dict, get_language_model |
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class ModelLanguage: |
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""" |
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ASRT专用N-Gram语言模型 |
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""" |
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def __init__(self, model_path: str): |
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self.model_path = model_path |
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self.dict_pinyin = dict() |
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self.model1 = dict() |
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self.model2 = dict() |
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def load_model(self): |
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""" |
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加载N-Gram语言模型到内存 |
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""" |
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self.dict_pinyin = get_symbol_dict('dict.txt') |
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self.model1 = get_language_model(os.path.join(self.model_path, 'language_model1.txt')) |
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self.model2 = get_language_model(os.path.join(self.model_path, 'language_model2.txt')) |
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model = (self.dict_pinyin, self.model1, self.model2) |
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return model |
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def pinyin_to_text(self, list_pinyin: list, beam_size: int = 100) -> str: |
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""" |
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拼音转文本，一次性取得全部结果 |
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""" |
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result = list() |
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tmp_result_last = list() |
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for item_pinyin in list_pinyin: |
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tmp_result = self.pinyin_stream_decode(tmp_result_last, item_pinyin, beam_size) |
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if len(tmp_result) == 0 and len(tmp_result_last) > 0: |
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result.append(tmp_result_last[0][0]) |
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tmp_result = self.pinyin_stream_decode([], item_pinyin, beam_size) |
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if len(tmp_result) > 0: |
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result.append(tmp_result[0][0]) |
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tmp_result = [] |
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tmp_result_last = tmp_result |
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if len(tmp_result_last) > 0: |
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result.append(tmp_result_last[0][0]) |
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return ''.join(result) |
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def pinyin_stream_decode(self, temple_result: list, |
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item_pinyin: str, |
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beam_size: int = 100) -> list: |
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""" |
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拼音流式解码，逐字转换，每次返回中间结果 |
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""" |
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# 如果这个拼音不在汉语拼音字典里的话，直接返回空列表，不做decode |
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if item_pinyin not in self.dict_pinyin: |
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return [] |
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# 获取拼音下属的字的列表，cur_words包含了该拼音对应的所有的字 |
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cur_words = self.dict_pinyin[item_pinyin] |
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# 第一个字做初始处理 |
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if len(temple_result) == 0: |
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lst_result = list() |
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for word in cur_words: |
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# 添加该字到可能的句子列表，设置初始概率为1.0 |
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lst_result.append([word, 1.0]) |
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return lst_result |
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# 开始处理已经至少有一个字的中间结果情况 |
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new_result = list() |
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for sequence in temple_result: |
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for cur_word in cur_words: |
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# 得到2-gram的汉字子序列 |
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tuple2_word = sequence[0][-1] + cur_word |
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if tuple2_word not in self.model2: |
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# 如果2-gram子序列不存在 |
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continue |
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# 计算状态转移概率 |
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prob_origin = sequence[1] # 原始概率 |
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count_two_word = float(self.model2[tuple2_word]) # 二字频数 |
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count_one_word = float(self.model1[tuple2_word[-2]]) # 单字频数 |
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cur_probility = prob_origin * count_two_word / count_one_word |
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new_result.append([sequence[0] + cur_word, cur_probility]) |
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new_result = sorted(new_result, key=lambda x: x[1], reverse=True) |
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if len(new_result) > beam_size: |
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return new_result[0:beam_size] |
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return new_result |
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if __name__ == '__main__': |
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ml = ModelLanguage('model_language') |
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ml.load_model() |
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_str_pinyin = ['zhe4', 'zhen1', 'shi4', 'ji2', 'hao3', 'de5'] |
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_RESULT = ml.pinyin_to_text(_str_pinyin) |
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print('语音转文字结果:\n', _RESULT)
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