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124 lines
4.5 KiB
124 lines
4.5 KiB
1 year ago
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# !/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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