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人工智能-语音合成和语音识别

 

一：语音合成

 转成语音

相关实验-基于AI百度开放平台

1：登录基于AI百度开放平台

pip install baidu-aip   #安装百度ai　　

2：创建语音合成应用

 

 

 

3：查看官方文档

 

 

上面介绍相关使用

4：写代码

from aip import AipSpeech   #aip是SDK包里的文件

""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)

result  = client.synthesis('你好', 'zh', 1, {
    'vol': 5,
    'spd':3,
    'pit':9,
    'per':5
})

# 识别正确返回语音二进制 错误则返回dict 参照下面错误码
if not isinstance(result, dict):
    with open('audio.mp3', 'wb') as f:
        f.write(result)

 

 

 5：测试

可以看到生成audio.mp3文件

 

 

 

二：语音识别

 

相关实验

1：准备转码工具

语音识别仅支持以下格式 ：pcm（不压缩）、wav（不压缩，pcm编码）、amr（有损压缩格式）；8k/16k 采样率 16bit 位深的单声道。即：

  1. pcm wav amr 格式三选一。 正常情况请使用pcm。其中wav格式需要使用pcm编码。
  2. 采用率二选一 8000 或者 16000。正常情况请使用16000
  3. 单声道

 

先准备转码工具ffmpeg并配置到环境变量中

　　https://pan.baidu.com/s/1jonSAa_TG2XuaJEy3iTmHg

　　密码：w6hk

 

ffmpeg -y  -i 16k.wav  -acodec pcm_s16le -f s16le -ac 1 -ar 16000 16k.pcm

 

2：代码

from aip import AipSpeech
import os

""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)


# 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

# 识别本地文件
res = client.asr(get_file_content('audio.mp3'), 'pcm', 16000, {
    'dev_pid': 1536,
})

print(res.get("result")[0])

 

　　

3：测试

 

 

三：语音识别和语音合成的使用案例

1：学说话

先自己录好音

from aip import AipSpeech
import os
import time

""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)

###############################################
#语音识别
    # 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

def audio2text(filePath):
    # 识别本地文件
    res = client.asr(get_file_content(filePath), 'pcm', 16000, {
        'dev_pid': 1536,
    })

    return res.get("result")[0]

###############################################
#语音合成
def text2audio(text):
    filename = f"{time.time()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        'vol': 5,
        'spd': 3,
        'pit': 9,
        'per': 5
    })

    # 识别正确返回语音二进制 错误则返回dict 参照下面错误码
    if not isinstance(result, dict):
        with open(filename, 'wb') as f:
            f.write(result)
    return filename

##################################################################
text = audio2text('1547553000.3612237.mp3')#语音识别

filename = text2audio(text)  #语音合成
os.system(filename)  #执行语音文件

 

 

2：问答--文本相识度技术（自然语言处理）

我问机器答

from aip import AipSpeech
import os
import time

""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)

###############################################
#语音识别
    # 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

def audio2text(filePath):
    # 识别本地文件
    res = client.asr(get_file_content(filePath), 'pcm', 16000, {
        'dev_pid': 1536,
    })

    return res.get("result")[0]

###############################################
#语音合成
def text2audio(text):
    filename = f"{time.time()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        'vol': 5,
        'spd': 3,
        'pit': 9,
        'per': 5
    })

    # 识别正确返回语音二进制 错误则返回dict 参照下面错误码
    if not isinstance(result, dict):
        with open(filename, 'wb') as f:
            f.write(result)
    return filename


text = audio2text('audio.mp3')#语音识别
print(text)
if text == '你叫什么名字':
    text = '我叫小猪配齐'

filename = text2audio(text)  #语音合成
os.system(filename)  #执行语音文件

 

 

这样是有问题的，我们换一种方式问就不行，所以还有另外一种方式，自然语言处理的文本相识度技术

 

 

 

import os
import time
from aip import AipSpeech
from aip import AipNlp   （nlp:nature language process 自然语言处理）


""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
nlp = AipNlp(APP_ID, API_KEY, SECRET_KEY)
###############################################
#语音识别
    # 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

def audio2text(filePath):
    # 识别本地文件
    res = client.asr(get_file_content(filePath), 'pcm', 16000, {
        'dev_pid': 1536,
    })

    return res.get("result")[0]

###############################################
#语音合成
def text2audio(text):
    filename = f"{time.time()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        'vol': 5,
        'spd': 3,
        'pit': 9,
        'per': 5
    })

    # 识别正确返回语音二进制 错误则返回dict 参照下面错误码
    if not isinstance(result, dict):
        with open(filename, 'wb') as f:
            f.write(result)
    return filename


text = audio2text('audio.mp3')#语音识别

if nlp.simnet('你叫什么名字',text).get('score') > 5:
    text = '我叫小猪配齐'
else:
    text = '我不知道你在说什么'

filename = text2audio(text)  #语音合成
os.system(filename)  #执行语音文件

 

 

3：图灵机器人-第三方语聊库

 

（1）先登录并创建机器人

 

可以查看使用文档

 

  

 

（2）使用案例

import requests

args = {
    "reqType":0,
    "perception": {
        "inputText": {
            "text": "北京今天的天气怎么样"
        },
    },
    "userInfo": {
        "apiKey": "8940b5fa71984863b349d66894988538",
        "userId": "111"
    }
}


url = 'http://openapi.tuling123.com/openapi/api/v2'
res = requests.post(url,json=args)

print(res.json().get('results')[0].get('values').get('text'))

 

 

（3）与机器人交流-提前录好问题

import requests
import os
import time
from aip import AipSpeech
from aip import AipNlp


""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
nlp = AipNlp(APP_ID, API_KEY, SECRET_KEY)
###############################################
#语音识别
    # 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

def audio2text(filePath):
    # 识别本地文件
    res = client.asr(get_file_content(filePath), 'pcm', 16000, {
        'dev_pid': 1536,
    })

    return res.get("result")[0]

###############################################
#语音合成
def text2audio(text):
    filename = f"{time.time()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        'vol': 5,
        'spd': 3,
        'pit': 9,
        'per': 5
    })

    # 识别正确返回语音二进制 错误则返回dict 参照下面错误码
    if not isinstance(result, dict):
        with open(filename, 'wb') as f:
            f.write(result)
    return filename

#################################

def to_tuling(text):
    args = {
        "reqType": 0,
        "perception": {
            "inputText": {
                "text": text
            },
        },
        "userInfo": {
            "apiKey": "8940b5fa71984863b349d66894988538",
            "userId": "111"
        }
    }

    url = 'http://openapi.tuling123.com/openapi/api/v2'
    res = requests.post(url, json=args)

    text = res.json().get('results')[0].get('values').get('text')
    return text


text = audio2text('audio.mp3')#语音识别  #问的内容

res = nlp.simnet('你叫什么名字','你是谁')
if nlp.simnet('你叫什么名字',text).get('score') > 5:
    text = '我叫小猪配齐'
else:
    text = to_tuling(text)  #  使用第三方库回答的内容

print(text)
filename = text2audio(text)  #语音合成  #回答
os.system(filename)  #执行语音文件

 

（4）与机器人交流

 

from flask  import Flask,render_template,request,jsonify,send_file
from uuid import uuid4
import requests
import os
import time
from aip import AipSpeech
from aip import AipNlp


""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
nlp = AipNlp(APP_ID, API_KEY, SECRET_KEY)
###############################################
#语音识别
    # 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

def audio2text(filePath):
    # 识别本地文件
    res = client.asr(get_file_content(filePath), 'pcm', 16000, {
        'dev_pid': 1536,
    })

    return res.get("result")[0]

###############################################
#语音合成
def text2audio(text):
    filename = f"{time.time()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        'vol': 5,
        'spd': 3,
        'pit': 9,
        'per': 5
    })

    # 识别正确返回语音二进制 错误则返回dict 参照下面错误码
    if not isinstance(result, dict):
        with open(filename, 'wb') as f:
            f.write(result)
    return filename

#################################

def to_tuling(text):
    args = {
        "reqType": 0,
        "perception": {
            "inputText": {
                "text": text
            },
        },
        "userInfo": {
            "apiKey": "8940b5fa71984863b349d66894988538",
            "userId": "111"
        }
    }

    url = 'http://openapi.tuling123.com/openapi/api/v2'
    res = requests.post(url, json=args)

    text = res.json().get('results')[0].get('values').get('text')
    return text


app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')


@app.route('/ai',methods=['GET','POST'])
def ai():
    # 1.保存录音文件
    audio = request.files.get('record')
    filename = f'{uuid4()}.wav'
    audio.save(filename)
    # 2.将录音转换为PCM发送百度AI进行语音识别
    q_text = audio2text(filename)
    # 3.将识别的问题交给图灵机器人处理并获取答案
    a_text = to_tuling(q_text)
    # 4.将答案发送给百度AI进行语音合成
    a_file = text2audio(a_text)
    # 5.将音频文件发送给前端播放
    print(a_file)
    return jsonify({'filename':a_file})

#前端读取音频文件
@app.route('/get_audio/<filename>')
def get_audio(filename):
    return send_file(filename)



if __name__ == '__main__':
    app.run('0.0.0.0',9527,debug=True)

 

 

template/index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Title</title>
</head>
<body>
<audio src="" controls autoplay id="player"></audio>


<p>
    <button onclick="start_reco()" style="background-color: yellow">录制语音指令</button>
</p>
<p>
    <button onclick="stop_reco_audio()" style="background-color: blue">发送语音指令</button>
</p>

</body>
<script type="text/javascript" src="/static/jQuery3.1.1.js"></script>
<script type="text/javascript" src="/static/Recorder.js"></script>
<script type="text/javascript">
    var reco = null;
    //浏览器音频播放内容
    var audio_context = new AudioContext();
    //用户的媒体信息
    navigator.getUserMedia = (navigator.getUserMedia ||
        navigator.webkitGetUserMedia ||
        navigator.mozGetUserMedia ||
        navigator.msGetUserMedia);

    navigator.getUserMedia({audio: true}, create_stream, function (err) {
        console.log(err)
    });

    function create_stream(user_media) {
        var stream_input = audio_context.createMediaStreamSource(user_media);//创建流媒体
        reco = new Recorder(stream_input);//装流，盛放录音内容
    }

    function start_reco() {//开始录音
        reco.record();
    }


    function stop_reco_audio() {
        reco.stop();
        send_audio();
        reco.clear();
    }


    function send_audio() {
        reco.exportWAV(function (wav_file) { //发文件
            var formdata = new FormData();
            formdata.append('record',wav_file);
            console.log(formdata);
            $.ajax({
                url: "http://192.168.13.142:9527/ai",
                type: 'post',
                processData: false,
                contentType: false,
                data: formdata,
                dataType: 'json',
                success: function (data) {
                    document.getElementById('player').src = 'http://192.168.13.142:9527/get_audio/' +data.filename;
                }
            });

        })
    }

    function get_audio() {
        reco.exportWAV(function (wav_file) {
            // wav_file = Blob对象
            // ws.send(wav_file);
            var formdata = new FormData();
            formdata.append("record", wav_file);
            formdata.append("sender", toy_id);
            formdata.append("to_user", document.getElementById("from_user").innerText);
            console.log(formdata);
            $.ajax({
                url: serv + "/toy_uploader",
                type: 'post',
                processData: false,
                contentType: false,
                data: formdata,
                dataType: 'json',
                success: function (data) {
                    if (data.code == 0) {
                        var send_str = JSON.stringify({"to_user": document.getElementById("from_user").innerText});
                        ws.send(send_str)
                    }
                }
            });

        })
    }

</script>
</html>
前端页面

 

 

static/

(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.Recorder = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
"use strict";

module.exports = require("./recorder").Recorder;

},{"./recorder":2}],2:[function(require,module,exports){
'use strict';

var _createClass = (function () {
    function defineProperties(target, props) {
        for (var i = 0; i < props.length; i++) {
            var descriptor = props[i];descriptor.enumerable = descriptor.enumerable || false;descriptor.configurable = true;if ("value" in descriptor) descriptor.writable = true;Object.defineProperty(target, descriptor.key, descriptor);
        }
    }return function (Constructor, protoProps, staticProps) {
        if (protoProps) defineProperties(Constructor.prototype, protoProps);if (staticProps) defineProperties(Constructor, staticProps);return Constructor;
    };
})();

Object.defineProperty(exports, "__esModule", {
    value: true
});
exports.Recorder = undefined;

var _inlineWorker = require('inline-worker');

var _inlineWorker2 = _interopRequireDefault(_inlineWorker);

function _interopRequireDefault(obj) {
    return obj && obj.__esModule ? obj : { default: obj };
}

function _classCallCheck(instance, Constructor) {
    if (!(instance instanceof Constructor)) {
        throw new TypeError("Cannot call a class as a function");
    }
}

var Recorder = exports.Recorder = (function () {
    function Recorder(source, cfg) {
        var _this = this;

        _classCallCheck(this, Recorder);

        this.config = {
            bufferLen: 4096,
            numChannels: 2,
            mimeType: 'audio_pcm/wav'
        };
        this.recording = false;
        this.callbacks = {
            getBuffer: [],
            exportWAV: []
        };

        Object.assign(this.config, cfg);
        this.context = source.context;
        this.node = (this.context.createScriptProcessor || this.context.createJavaScriptNode).call(this.context, this.config.bufferLen, this.config.numChannels, this.config.numChannels);

        this.node.onaudioprocess = function (e) {
            if (!_this.recording) return;

            var buffer = [];
            for (var channel = 0; channel < _this.config.numChannels; channel++) {
                buffer.push(e.inputBuffer.getChannelData(channel));
            }
            _this.worker.postMessage({
                command: 'record',
                buffer: buffer
            });
        };

        source.connect(this.node);
        this.node.connect(this.context.destination); //this should not be necessary

        var self = {};
        this.worker = new _inlineWorker2.default(function () {
            var recLength = 0,
                recBuffers = [],
                sampleRate = undefined,
                numChannels = undefined;

            self.onmessage = function (e) {
                switch (e.data.command) {
                    case 'init':
                        init(e.data.config);
                        break;
                    case 'record':
                        record(e.data.buffer);
                        break;
                    case 'exportWAV':
                        exportWAV(e.data.type);
                        break;
                    case 'getBuffer':
                        getBuffer();
                        break;
                    case 'clear':
                        clear();
                        break;
                }
            };

            function init(config) {
                sampleRate = config.sampleRate;
                numChannels = config.numChannels;
                initBuffers();
            }

            function record(inputBuffer) {
                for (var channel = 0; channel < numChannels; channel++) {
                    recBuffers[channel].push(inputBuffer[channel]);
                }
                recLength += inputBuffer[0].length;
            }

            function exportWAV(type) {
                var buffers = [];
                for (var channel = 0; channel < numChannels; channel++) {
                    buffers.push(mergeBuffers(recBuffers[channel], recLength));
                }
                var interleaved = undefined;
                if (numChannels === 2) {
                    interleaved = interleave(buffers[0], buffers[1]);
                } else {
                    interleaved = buffers[0];
                }
                var dataview = encodeWAV(interleaved);
                var audioBlob = new Blob([dataview], { type: type });

                self.postMessage({ command: 'exportWAV', data: audioBlob });
            }

            function getBuffer() {
                var buffers = [];
                for (var channel = 0; channel < numChannels; channel++) {
                    buffers.push(mergeBuffers(recBuffers[channel], recLength));
                }
                self.postMessage({ command: 'getBuffer', data: buffers });
            }

            function clear() {
                recLength = 0;
                recBuffers = [];
                initBuffers();
            }

            function initBuffers() {
                for (var channel = 0; channel < numChannels; channel++) {
                    recBuffers[channel] = [];
                }
            }

            function mergeBuffers(recBuffers, recLength) {
                var result = new Float32Array(recLength);
                var offset = 0;
                for (var i = 0; i < recBuffers.length; i++) {
                    result.set(recBuffers[i], offset);
                    offset += recBuffers[i].length;
                }
                return result;
            }

            function interleave(inputL, inputR) {
                var length = inputL.length + inputR.length;
                var result = new Float32Array(length);

                var index = 0,
                    inputIndex = 0;

                while (index < length) {
                    result[index++] = inputL[inputIndex];
                    result[index++] = inputR[inputIndex];
                    inputIndex++;
                }
                return result;
            }

            function floatTo16BitPCM(output, offset, input) {
                for (var i = 0; i < input.length; i++, offset += 2) {
                    var s = Math.max(-1, Math.min(1, input[i]));
                    output.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7FFF, true);
                }
            }

            function writeString(view, offset, string) {
                for (var i = 0; i < string.length; i++) {
                    view.setUint8(offset + i, string.charCodeAt(i));
                }
            }

            function encodeWAV(samples) {
                var buffer = new ArrayBuffer(44 + samples.length * 2);
                var view = new DataView(buffer);

                /* RIFF identifier */
                writeString(view, 0, 'RIFF');
                /* RIFF chunk length */
                view.setUint32(4, 36 + samples.length * 2, true);
                /* RIFF type */
                writeString(view, 8, 'WAVE');
                /* format chunk identifier */
                writeString(view, 12, 'fmt ');
                /* format chunk length */
                view.setUint32(16, 16, true);
                /* sample format (raw) */
                view.setUint16(20, 1, true);
                /* channel count */
                view.setUint16(22, numChannels, true);
                /* sample rate */
                view.setUint32(24, sampleRate, true);
                /* byte rate (sample rate * block align) */
                view.setUint32(28, sampleRate * 4, true);
                /* block align (channel count * bytes per sample) */
                view.setUint16(32, numChannels * 2, true);
                /* bits per sample */
                view.setUint16(34, 16, true);
                /* data chunk identifier */
                writeString(view, 36, 'data');
                /* data chunk length */
                view.setUint32(40, samples.length * 2, true);

                floatTo16BitPCM(view, 44, samples);

                return view;
            }
        }, self);

        this.worker.postMessage({
            command: 'init',
            config: {
                sampleRate: this.context.sampleRate,
                numChannels: this.config.numChannels
            }
        });

        this.worker.onmessage = function (e) {
            var cb = _this.callbacks[e.data.command].pop();
            if (typeof cb == 'function') {
                cb(e.data.data);
            }
        };
    }

    _createClass(Recorder, [{
        key: 'record',
        value: function record() {
            this.recording = true;
        }
    }, {
        key: 'stop',
        value: function stop() {
            this.recording = false;
        }
    }, {
        key: 'clear',
        value: function clear() {
            this.worker.postMessage({ command: 'clear' });
        }
    }, {
        key: 'getBuffer',
        value: function getBuffer(cb) {
            cb = cb || this.config.callback;
            if (!cb) throw new Error('Callback not set');

            this.callbacks.getBuffer.push(cb);

            this.worker.postMessage({ command: 'getBuffer' });
        }
    }, {
        key: 'exportWAV',
        value: function exportWAV(cb, mimeType) {
            mimeType = mimeType || this.config.mimeType;
            cb = cb || this.config.callback;
            if (!cb) throw new Error('Callback not set');

            this.callbacks.exportWAV.push(cb);

            this.worker.postMessage({
                command: 'exportWAV',
                type: mimeType
            });
        }
    }], [{
        key: 'forceDownload',
        value: function forceDownload(blob, filename) {
            var url = (window.URL || window.webkitURL).createObjectURL(blob);
            var link = window.document.createElement('a');
            link.href = url;
            link.download = filename || 'output.wav';
            var click = document.createEvent("Event");
            click.initEvent("click", true, true);
            link.dispatchEvent(click);
        }
    }]);

    return Recorder;
})();

exports.default = Recorder;

},{"inline-worker":3}],3:[function(require,module,exports){
"use strict";

module.exports = require("./inline-worker");
},{"./inline-worker":4}],4:[function(require,module,exports){
(function (global){
"use strict";

var _createClass = (function () { function defineProperties(target, props) { for (var key in props) { var prop = props[key]; prop.configurable = true; if (prop.value) prop.writable = true; } Object.defineProperties(target, props); } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; })();

var _classCallCheck = function (instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } };

var WORKER_ENABLED = !!(global === global.window && global.URL && global.Blob && global.Worker);

var InlineWorker = (function () {
  function InlineWorker(func, self) {
    var _this = this;

    _classCallCheck(this, InlineWorker);

    if (WORKER_ENABLED) {
      var functionBody = func.toString().trim().match(/^function\s*\w*\s*\([\w\s,]*\)\s*{([\w\W]*?)}$/)[1];
      var url = global.URL.createObjectURL(new global.Blob([functionBody], { type: "text/javascript" }));

      return new global.Worker(url);
    }

    this.self = self;
    this.self.postMessage = function (data) {
      setTimeout(function () {
        _this.onmessage({ data: data });
      }, 0);
    };

    setTimeout(function () {
      func.call(self);
    }, 0);
  }

  _createClass(InlineWorker, {
    postMessage: {
      value: function postMessage(data) {
        var _this = this;

        setTimeout(function () {
          _this.self.onmessage({ data: data });
        }, 0);
      }
    }
  });

  return InlineWorker;
})();

module.exports = InlineWorker;
}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
},{}]},{},[1])(1)
});
Recorder.js

 

以及文件jQuery3.1.1.js

请下载

https://blog-static.cnblogs.com/files/lilz/jQuery3.1.1.js

　　

测试：

 

（5）与机器人交流--交流数据存放到MongoDB数据库中

请先学习：　　

Mongdb-介绍与使用

python常用模块-pymongo

 

from flask  import Flask,render_template,request,jsonify,send_file
from uuid import uuid4
import requests
import os
import time
from aip import AipSpeech
from aip import AipNlp
import  pymongo
import json
from bson import ObjectId

""" 你的 APPID AK SK """
APP_ID = '15421077'
API_KEY = 'D8qPp3uMDcjvEfkFzSKWUlji'
SECRET_KEY = 'gvHXZFpOaZrkhDSxPiiyQvN3ZUjb4ECU'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
nlp = AipNlp(APP_ID, API_KEY, SECRET_KEY)

##################连接本地数据库##############
mongoclient = pymongo.MongoClient(host='127.0.0.1',port=27017)
MongoDB = mongoclient['locals']  #连接具体数据库
###############################################
#语音识别
    # 读取文件
def get_file_content(filePath):
    os.system(f"ffmpeg -y  -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm")
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

def audio2text(filePath):
    # 识别本地文件
    res = client.asr(get_file_content(filePath), 'pcm', 16000, {
        'dev_pid': 1536,
    })

    return res.get("result")[0]

###############################################
#语音合成
def text2audio(text):
    filename = f"{time.time()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        'vol': 5,
        'spd': 3,
        'pit': 9,
        'per': 5
    })

    # 识别正确返回语音二进制 错误则返回dict 参照下面错误码
    if not isinstance(result, dict):
        with open(filename, 'wb') as f:
            f.write(result)
    return filename

#################################

def to_tuling(text):
    args = {
        "reqType": 0,
        "perception": {
            "inputText": {
                "text": text
            },
        },
        "userInfo": {
            "apiKey": "8940b5fa71984863b349d66894988538",
            "userId": "111"
        }
    }

    url = 'http://openapi.tuling123.com/openapi/api/v2'
    res = requests.post(url, json=args)

    text = res.json().get('results')[0].get('values').get('text')
    return text


app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')


@app.route('/ai',methods=['GET','POST'])
def ai():
    # 1.保存录音文件
    audio = request.files.get('record')
    filename = f'{uuid4()}.wav'
    audio.save(filename)
    # 2.将录音转换为PCM发送百度AI进行语音识别
    q_text = audio2text(filename)

    # 3.将识别的问题交给图灵机器人处理并获取答案
    a_text = to_tuling(q_text)
    # 4.将答案发送给百度AI进行语音合成
    a_file = text2audio(a_text)
    res = MongoDB.users.insert_one({'chat_list': [{'u': q_text},{'a':a_text}]})
    # 5.将音频文件发送给前端播放
    print(a_file)
    return jsonify({'filename':a_file})

#前端读取音频文件
@app.route('/get_audio/<filename>')
def get_audio(filename):
    return send_file(filename)



if __name__ == '__main__':
    app.run('0.0.0.0',9527,debug=True)

 

 

 测试

 

posted on 2019-01-15 15:06  诚意  阅读(1532)  评论(0)  编辑  收藏  举报
 
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