基于python实现百度语音识别和图灵对话

图例如下

基于python实现百度语音识别和图灵对话_第1张图片

https://github.com/Dongvdong/python_Smartvoice

  • 上电后,只要周围声音超过 2000,开始录音5S
  • 录音上传百度识别,并返回结果文字输出
  • 继续等待,周围声音是否超过2000,没有就等待。
  • 点用电脑API语音交互

代码如下

# -*- coding: utf-8 -*-
# 树莓派
from pyaudio import PyAudio, paInt16
import numpy as np
from datetime import datetime
import wave
import time
import requests#导入requests库
import urllib, urllib.request, pycurl
import base64
import json
import os
import sys
from imp import reload
 
# 调用电脑API生成语音交互
import speech
import win32api
import os
import sys
import time
import win32con
 
 
reload(sys)
 
#sys.setdefaultencoding( "utf-8" )
#一些全局变量
save_count = 0
save_buffer = []
t = 0
sum = 0
time_flag = 0
flag_num = 0
filename = ''
duihua = '1'
def getHtml(url):
  html= requests.get(url)
  # html.encoding = 'utf-8'#防止中文乱码
  
  return html.text
def get_token():
  apiKey = "AxXDYEN27Ks9XHocsGmCEdPm"
  secretKey = "61cd52759f4d704d91c155a22ff7183d"
  auth_url = "https://openapi.baidu.com/oauth/2.0/token?grant_type=client_credentials&client_id=" + apiKey + "&client_secret=" + secretKey;
  res = requests.get(auth_url)
  #res.encoding = 'utf-8'#防止中文乱码
  #print (res.text)
  return json.loads(res.text)['access_token']
def dump_res(buf):#输出百度语音识别的结果
  global duihua
  #print ("字符串类型")
  #print (buf)
  a = eval(buf)
  #print (type(a))
  if a['err_msg']=='success.':
    #print (a['result'][0])#终于搞定了,在这里可以输出,返回的语句
    duihua = a['result'][0]
    print ("我:"+duihua)
def use_cloud(token):#进行合成
  fp = wave.open(filename, 'rb')
  nf = fp.getnframes()
  f_len = nf * 2
  audio_data = fp.readframes(nf)
  cuid = "9120612" #产品id
  srv_url = 'http://vop.baidu.com/server_api' + '?cuid=' + cuid + '&token=' + token
  http_header = [
    'Content-Type: audio/pcm; rate=8000',
    'Content-Length: %d' % f_len
  ]
  c = pycurl.Curl()
  c.setopt(pycurl.URL, str(srv_url)) #curl doesn't support unicode
  #c.setopt(c.RETURNTRANSFER, 1)
  c.setopt(c.HTTPHEADER, http_header)  #must be list, not dict
  c.setopt(c.POST, 1)
  c.setopt(c.CONNECTTIMEOUT, 30)
  c.setopt(c.TIMEOUT, 30)
  c.setopt(c.WRITEFUNCTION, dump_res)
  c.setopt(c.POSTFIELDS, audio_data)
  c.setopt(c.POSTFIELDSIZE, f_len)
  c.perform() #pycurl.perform() has no return val
# 将data中的数据保存到名为filename的WAV文件中
def save_wave_file(filename, data):
  wf = wave.open(filename, 'wb')
  wf.setnchannels(1)
  wf.setsampwidth(2)
  wf.setframerate(SAMPLING_RATE)
  wf.writeframes(b"".join(data))
  wf.close()
NUM_SAMPLES = 2000    # pyAudio内部缓存的块的大小
SAMPLING_RATE = 8000  # 取样频率
LEVEL = 1500      # 声音保存的阈值
COUNT_NUM = 20     # NUM_SAMPLES个取样之内出现COUNT_NUM个大于LEVEL的取样则记录声音
SAVE_LENGTH = 8     # 声音记录的最小长度:SAVE_LENGTH * NUM_SAMPLES 个取样
exception_on_overflow=False
# 开启声音输入pyaudio对象
pa = PyAudio()
stream = pa.open(format=paInt16, channels=1, rate=SAMPLING_RATE, input=True,
        frames_per_buffer=NUM_SAMPLES)
token = get_token()#获取token
key = '35ff2856b55e4a7f9eeb86e3437e23fe'
api = 'http://www.tuling123.com/openapi/api?key=' + key + '&info='
while(True):
  # 读入NUM_SAMPLES个取样
  string_audio_data = stream.read(NUM_SAMPLES,False);
  # 将读入的数据转换为数组
  audio_data = np.fromstring(string_audio_data, dtype=np.short)
  # 计算大于LEVEL的取样的个数
  large_sample_count = np.sum( audio_data > LEVEL )
  temp = np.max(audio_data)
  if temp > 2000 and t == 0:
    t = 1#开启录音
    print ("---------主人我在听你说!(5S)----------")
    begin = time.time()
    # print (temp)
  if t:
    #print (np.max(audio_data))
    if np.max(audio_data)<1000:
      sum += 1
      # print (sum)
    end = time.time()
    if end-begin>5:
      time_flag = 1
      # print ("五秒到了,准备结束")
    # 如果个数大于COUNT_NUM,则至少保存SAVE_LENGTH个块
    if large_sample_count > COUNT_NUM:
      save_count = SAVE_LENGTH
    else:
      save_count -= 1
    if save_count < 0:
      save_count = 0
    if save_count > 0:
      # 将要保存的数据存放到save_buffer中
      save_buffer.append(string_audio_data )
    else:
      # 将save_buffer中的数据写入WAV文件,WAV文件的文件名是保存的时刻
      #if time_flag:
      if len(save_buffer) > 0 or time_flag:
        #filename = datetime.now().strftime("%Y-%m-%d_%H_%M_%S") + ".wav"#原本是用时间做名字
        filename = str(flag_num)+".wav"
        flag_num += 1
        save_wave_file(filename, save_buffer)
        save_buffer = []
        t = 0
        sum =0
        time_flag = 0
       # print (filename, "保存成功正在进行语音识别")
        use_cloud(token)
       #  print (duihua)
        info = duihua
        duihua = ""
        
        request = api + str(info)
        response = getHtml(request)
       # print ( "-----1-----")
        dic_json = json.loads(response)
       
        a = dic_json['text']
       
        unicodestring = a
        # 将Unicode转化为普通Python字符串:"encode"
        utf8string = unicodestring.encode("utf-8")
       
        print ("科塔娜:"+str(a))
         
        # 电脑说话
        speech.say(str(a))
         
        url = "http://tsn.baidu.com/text2audio?tex="+dic_json['text']+"&lan=zh&per=0&pit=1&spd=7&cuid=7519663&ctp=1&tok=25.41bf315625c68b3e947c49b90788532d.315360000.1798261651.282335-9120612"
        os.system('mpg123 "%s"'%(url))

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。

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