#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: nl8590687 语音识别API的HTTP服务器程序 """ import http.server import urllib import keras from SpeechModel22 import ModelSpeech from LanguageModel import ModelLanguage datapath = 'data/' modelpath = 'model_speech/' ms = ModelSpeech(datapath) ms.LoadModel(modelpath + 'speech_model22_e_0_step_216500.model') ml = ModelLanguage('model_language') ml.LoadModel() class TestHTTPHandle(http.server.BaseHTTPRequestHandler): def _set_response(self): self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() def do_GET(self): buf = 'ASRT_SpeechRecognition API' self.protocal_version = 'HTTP/1.1' self._set_response() buf = bytes(buf,encoding="utf-8") self.wfile.write(buf) def do_POST(self): ''' 处理通过POST方式传递过来并接收的语音数据 通过语音模型和语言模型计算得到语音识别结果并返回 ''' path = self.path print(path) #获取post提交的数据 datas = self.rfile.read(int(self.headers['content-length'])) #datas = urllib.unquote(datas).decode("utf-8", 'ignore') datas = datas.decode('utf-8') datas_split = datas.split('&') token = '' fs = 0 wavs = [] #type = 'wavfilebytes' # wavfilebytes or python-list for line in datas_split: [key, value]=line.split('=') if('wavs' == key): wavs.append(int(value)) elif('fs' == key): fs = int(value) elif('token' == key ): token = value #elif('type' == key): # type = value else: print(key, value) #if('python-list' == type): r = self.recognize([wavs], fs) #else: # r = self.recognize_from_file('') if(token == 'qwertasd'): #buf = '成功\n'+'wavs:\n'+str(wavs)+'\nfs:\n'+str(fs) buf = r else: buf = '403' #print(datas) self._set_response() #buf = ' \n \n
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