diff --git a/asrserver_http.py b/asrserver_http.py index 91ae0e6..8c26a45 100644 --- a/asrserver_http.py +++ b/asrserver_http.py @@ -31,7 +31,7 @@ from flask import Flask, Response, request from speech_model import ModelSpeech from speech_model_zoo import SpeechModel251BN from speech_features import Spectrogram -from LanguageModel2 import ModelLanguage +from language_model3 import ModelLanguage from utils.ops import decode_wav_bytes API_STATUS_CODE_OK = 200000 # OK @@ -62,7 +62,7 @@ ms = ModelSpeech(sm251bn, feat, max_label_length=64) ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5') ml = ModelLanguage('model_language') -ml.LoadModel() +ml.load_model() class AsrtApiResponse: @@ -131,7 +131,7 @@ def recognition_post(level): seq_pinyin = request_data['sequence_pinyin'] - result = ml.SpeechToText(seq_pinyin) + result = ml.pinyin_to_text(seq_pinyin) json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'language level') json_data.result = result @@ -150,7 +150,7 @@ def recognition_post(level): wavdata = decode_wav_bytes(samples_data=wavdata_bytes, channels=channels, byte_width=byte_width) result_speech = ms.recognize_speech(wavdata, sample_rate) - result = ml.SpeechToText(result_speech) + result = ml.pinyin_to_text(result_speech) json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'all level') json_data.result = result diff --git a/language_model3.py b/language_model3.py new file mode 100644 index 0000000..e2f884a --- /dev/null +++ b/language_model3.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Copyright 2016-2099 Ailemon.net +# +# This file is part of ASRT Speech Recognition Tool. +# +# ASRT is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# ASRT is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with ASRT. If not, see . +# ============================================================================ + +""" +@author: nl8590687 +ASRT语音识别的语言模型 + +基于N-Gram的语言模型 +""" + +import os + +from utils.ops import get_symbol_dict, get_language_model + +class ModelLanguage: + ''' + ASRT专用N-Gram语言模型 + ''' + def __init__(self, model_path: str): + self.model_path = model_path + self.dict_pinyin = dict() + self.model1 = dict() + self.model2 = dict() + + def load_model(self): + ''' + 加载N-Gram语言模型到内存 + ''' + self.dict_pinyin = get_symbol_dict('dict.txt') + self.model1 = get_language_model(os.path.join(self.model_path, 'language_model1.txt')) + self.model2 = get_language_model(os.path.join(self.model_path, 'language_model2.txt')) + model = (self.dict_pinyin, self.model1, self.model2 ) + return model + + def pinyin_to_text(self, list_pinyin: list, beam_size: int=100) -> str: + ''' + 拼音转文本，一次性取得全部结果 + ''' + result = list() + tmp_result_last = list() + for item_pinyin in list_pinyin: + tmp_result = self.pinyin_stream_decode(tmp_result_last, item_pinyin, beam_size) + if len(tmp_result) == 0 and len(tmp_result_last) > 0: + result.append(tmp_result_last[0][0]) + tmp_result = self.pinyin_stream_decode([], item_pinyin, beam_size) + if len(tmp_result) > 0: + result.append(tmp_result[0][0]) + tmp_result = [] + tmp_result_last = tmp_result + + if len(tmp_result_last) > 0: + result.append(tmp_result_last[0][0]) + + return ''.join(result) + + def pinyin_stream_decode(self, temple_result: list, + item_pinyin: str, + beam_size: int = 100) -> list: + ''' + 拼音流式解码，逐字转换，每次返回中间结果 + ''' + # 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 + if item_pinyin not in self.dict_pinyin: + return [] + + # 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 + cur_words = self.dict_pinyin[item_pinyin] + # 第一个字做初始处理 + if len(temple_result) == 0: + lst_result = list() + for word in cur_words: + # 添加该字到可能的句子列表，设置初始概率为1.0 + lst_result.append([word, 1.0]) + return lst_result + + # 开始处理已经至少有一个字的中间结果情况 + new_result = list() + for sequence in temple_result: + for cur_word in cur_words: + # 得到2-gram的汉字子序列 + tuple2_word = sequence[0][-1] + cur_word + if tuple2_word not in self.model2: + # 如果2-gram子序列不存在 + continue + # 计算状态转移概率 + prob_origin = sequence[1] # 原始概率 + count_two_word = float(self.model2[tuple2_word]) # 二字频数 + count_one_word = float(self.model1[tuple2_word[-2]]) # 单字频数 + cur_probility = prob_origin * count_two_word / count_one_word + new_result.append([sequence[0]+cur_word, cur_probility]) + + new_result = sorted(new_result, key=lambda x:x[1], reverse=True) + if len(new_result) > beam_size: + return new_result[0:beam_size] + return new_result + + +if __name__=='__main__': + ml = ModelLanguage('model_language') + ml.load_model() + + _str_pinyin = ['zhe4', 'zhen1', 'shi4', 'ji2', 'hao3', 'de5'] + _RESULT = ml.pinyin_to_text(_str_pinyin) + print('语音转文字结果:\n', _RESULT) diff --git a/predict_speech_file.py b/predict_speech_file.py index ab302bc..855d13e 100644 --- a/predict_speech_file.py +++ b/predict_speech_file.py @@ -28,7 +28,7 @@ import os from speech_model import ModelSpeech from speech_model_zoo import SpeechModel251BN from speech_features import Spectrogram -from LanguageModel2 import ModelLanguage +from language_model3 import ModelLanguage os.environ["CUDA_VISIBLE_DEVICES"] = "0" @@ -49,7 +49,7 @@ res = ms.recognize_speech_from_file('filename.wav') print('*[提示] 声学模型语音识别结果：\n', res) ml = ModelLanguage('model_language') -ml.LoadModel() +ml.load_model() str_pinyin = res -res = ml.SpeechToText(str_pinyin) +res = ml.pinyin_to_text(str_pinyin) print('语音识别最终结果：\n',res)