人工智能 01. 语音合成,语音识别,相似度,图灵机器人,智能对话

一.准备工作(基于百度ai)

　　1.创建应用

①

②

 

③ 

④

　　2.用python时,下载模块  baidu-aip

 

 二.语音合成

　　将 文本 转换成 音频

 　　1.基本框架

from aip import AipSpeech

""" 你的 APPID AK SK """　　　　# 接口信息
APP_ID = '16027159'
API_KEY = 'AZoiGc2udGqk9psiylinOZrU'
SECRET_KEY = '9pa87qO9kkLiCxFjp2vCp9MRvM5LUPgV'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)


result  = client.synthesis('人的一切痛苦,本质都是对自己无能的愤怒', 'zh', 1, {
    'vol': 8,
    "spd": 4,
    "pit": 6,
    "per": 3
})

# 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('auido.mp3', 'wb') as f:    # 将合成数据放入 auido.mp3 文件中
        f.write(result)

 　　2.参数

 

　　3.错误时

 三.语音识别

　　将音频  转换成  文本

　　1.基本框架

from aip import AipSpeech

import os
""" 你的 APPID AK SK """
APP_ID = '16027159'
API_KEY = 'AZoiGc2udGqk9psiylinOZrU'
SECRET_KEY = '9pa87qO9kkLiCxFjp2vCp9MRvM5LUPgV'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)


# 读取文件
def get_file_content(filePath):
    with open(filePath, 'rb') as fp:
        return fp.read()

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

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

　　2.需要转换格式时

　　①安装ffmpeg

　　②配置环境变量

　　③基本结构

import os

from aip import AipSpeech

""" 你的 APPID AK SK """
APP_ID = '16027154'
API_KEY = '5a8u0aLf2SxRGRMX3jbZ2VH0'
SECRET_KEY = 'UAaqS13z6DjD9Qbjd065dAh0HjbqPrzV'

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")
　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　# 格式转换命令
    with open(f"{filePath}.pcm", 'rb') as fp:
        return fp.read()

res = client.asr(get_file_content('name.wav'), 'pcm', 16000, {    #pcm格式进行识别率高
    'dev_pid': 1536,
})

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

 　　3.先识别,再合成

import os

from aip import AipSpeech

""" 你的 APPID AK SK """
APP_ID = '16027159'

API_KEY = 'AZoiGc2udGqk9psiylinOZrU'
SECRET_KEY = '9pa87qO9kkLiCxFjp2vCp9MRvM5LUPgV'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)

def text2audio(text):　　　　　　　　#⑥
    result  = client.synthesis(text, 'zh', 1, {
        'vol': 8,
        "spd": 4,
        "pit": 6,
        "per": 3
    })

    # 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('auido.mp3', 'wb') as f:
            f.write(result)

    return "auido.mp3"

def audio2text(filepath):　　　　　　　　#②
    res = client.asr(get_file_content('name.wav'), 'pcm', 16000, {
        'dev_pid': 1536,
    })

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

def get_file_content(filePath):　　　　　　#③
    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()


text= audio2text('name.wav')　　　　　　　　　　# ①

if text == "你叫什么":　　　　　　　　　　　　#④
    filname = text2audio("我的名字是张三")　　　　#⑤
    os.system(filname)　　　　　　#⑦

 四.相似度

import os

from aip import AipSpeech,AipNlp    #导入模块

""" 你的 APPID AK SK """
APP_ID = '16027159'

API_KEY = 'AZoiGc2udGqk9psiylinOZrU'
SECRET_KEY = '9pa87qO9kkLiCxFjp2vCp9MRvM5LUPgV'

client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)
client_nlp = AipNlp(APP_ID, API_KEY, SECRET_KEY)　　#定义新对象

def text2audio(text):
    result  = client.synthesis(text, 'zh', 1, {
        'vol': 8,
        "spd": 4,
        "pit": 6,
        "per": 3
    })

    # 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('auido.mp3', 'wb') as f:
            f.write(result)

    return "auido.mp3"

def audio2text(filepath):
    res = client.asr(get_file_content('name.wav'), 'pcm', 16000, {
        'dev_pid': 1536,
    })

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

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()


text= audio2text('name.wav')
score = client_nlp.simnet("你的名字是什么",text).get('score')   # 比较俩个对象,获取相似度
print(score)
if score >= 0.58:　　　　　　　　#  0.58  是分界线(是不是一个意思)
    filname = text2audio("我的名字是张三")
    os.system(filname)

else:
    print('结束')

五.图灵机器人

 　　1.创建机器人

　　http://www.turingapi.com/

①

②

 

③

 　　2.请求示例

{
    "reqType":0,
    "perception": {
        "inputText": {
            "text": "附近的酒店"
        },
        "inputImage": {
            "url": "imageUrl"
        },
        "selfInfo": {
            "location": {
                "city": "北京",
                "province": "北京",
                "street": "信息路"
            }
        }
    },
    "userInfo": {
        "apiKey": "",
        "userId": ""
    }
}

 　　3.参数说明

①必填

 ②至少有一个

③  可不写

④ 必填

 　　4.示例

URL = "http://openapi.tuling123.com/openapi/api/v2"
import requests

data = {
    "perception": {
        "inputText": {
            "text": "你叫什么"
        }
    },
    "userInfo": {
        "apiKey": "2acbb36ddfcc41b5ae0deb65428eea84",
        "userId": "123"
    }
}

res = requests.post("http://openapi.tuling123.com/openapi/api/v2",json=data)

# print(res.content)
# print(res.text)
print(res.json())

 六. 识别+合成+图灵

import os

from aip import AipSpeech,AipNlp

""" 你的 APPID AK SK """
APP_ID = '16027159'

API_KEY = 'AZoiGc2udGqk9psiylinOZrU'
SECRET_KEY = '9pa87qO9kkLiCxFjp2vCp9MRvM5LUPgV'

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

def text2audio(text):
    result  = client.synthesis(text, 'zh', 1, {
        'vol': 8,
        "spd": 4,
        "pit": 6,
        "per": 3
    })

    # 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('auido.mp3', 'wb') as f:
            f.write(result)

    return "auido.mp3"

def audio2text(filepath):
    res = client.asr(get_file_content('name.wav'), 'pcm', 16000, {
        'dev_pid': 1536,
    })

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

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 goto_tl(text,uid):
    URL = "http://openapi.tuling123.com/openapi/api/v2"
    import requests
    data = {
        "perception": {
            "inputText": {
                "text": "天气怎样"
            }
        },
        "userInfo": {
            "apiKey": "2acbb36ddfcc41b5ae0deb65428eea84",
            "userId": "123"
        }
    }

    data["perception"]["inputText"]["text"] = text    # 替换请求信息
    data["userInfo"]["userId"] = uid　　　　　　# 替换标识
    res = requests.post(URL, json=data)

    # print(res.content)
    # print(res.text)
    print(res.json())
    return res.json().get("results")[0].get("values").get("text")


text= audio2text('name.wav')
score = client_nlp.simnet("今天天气怎样",text).get('score')
print(score)
if score >= 0.58:
    filname = text2audio("我的名字是张三")
    os.system(filname)

answer = goto_tl(text,"qiaoxiaoqiang")  # 相似度小于0.58 时走图灵机器人
filename = text2audio(answer)
os.system(filename)

七.智能对话

　　①导入三个文件

 　　② webtoy.html 文件中

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>我是玩具</title>
</head>
<body>
<audio controls id="player" autoplay></audio>
<p></p>
<button onclick="start_reco()">录音</button>
<button onclick="stop_reco()">发送录音</button>

</body>
<script type="text/javascript" src="/static/Recorder.js"></script>
<script type="text/javascript" src="/static/jquery-3.3.1.min.js"></script>
<script type="text/javascript">
    var serv = "http://127.0.0.1:9527";

    var reco = null;
    var audio_context = new AudioContext();//音频内容对象
    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) { // audio 麦克风和扬声器
        var stream_input = audio_context.createMediaStreamSource(user_media);//媒体流容器
        reco = new Recorder(stream_input);
    }


    function start_reco() {
        reco.record();
    }

    function stop_reco() {
        reco.stop();
        reco.exportWAV(function (wav_file) {
            console.log(wav_file); // Blob 对象
            var formdata = new FormData(); // form 表单 {key:value}
            formdata.append("reco",wav_file); // form - input type="file" name="reco"
            formdata.append("username","Alexander.DSB.Li");
            // form input type="text / password 编辑框" name="username" value = "Alexander.DSB.Li"
            $.ajax({
            url: serv + "/uploader",
            type: 'post',
            processData: false,
            contentType: false,
            data: formdata,
            dataType: 'json',
            success: function (data) {
                console.log(data);
                document.getElementById("player").src = serv + "/getaudio/" +data.filename;
            }
            })
        });//异步

        reco.clear();
    }
    


</script>
</html>

 　　③调用文件  app.py

import os

from flask import Flask,render_template,jsonify,request,send_file
from uuid import uuid4
# from flask_cors import CORS
from FAQ import audio2text, client_nlp, text2audio, goto_tl

app = Flask(__name__)
# CORS(app)

@app.route("/")
def webtoy():
    return render_template("WebToy.html")

@app.route("/uploader",methods=["POST","GET"])
def uploader():
    filename = f"{uuid4()}.wav"
    file = request.files.get("reco")
    file.save(filename)

    text = audio2text(filename)
    # 自然语言处理 LowB
    score = client_nlp.simnet("你叫什么名字", text).get("score")
    print(score)
    if score >= 0.75:
        filename = text2audio("我的名字叫银角大王八")
    else:
        answer = goto_tl(text, "qiaoxiaoqiang")
        filename = text2audio(answer)

    return jsonify({"code":0,"msg":"文件上传成功","filename":filename})


@app.route("/getaudio/<filename>")
def getaudio(filename):
    return send_file(filename)

if __name__ == '__main__':
    app.run("127.0.0.1",9527,debug=True)

 　　④ 调用文件用到的函数存在 FAQ.py文件中

import os
from uuid import uuid4

from aip import AipSpeech,AipNlp

""" 你的 APPID AK SK """
APP_ID = '16027154'
API_KEY = '5a8u0aLf2SxRGRMX3jbZ2VH0'
SECRET_KEY = 'UAaqS13z6DjD9Qbjd065dAh0HjbqPrzV'

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

# res = client_nlp.simnet("你叫什么名字","你的名字是什么")
# print(res)


def text2audio(text):
    filename = f"{uuid4()}.mp3"
    result = client.synthesis(text, 'zh', 1, {
        "spd": 4,
        'vol': 5,
        "pit": 8,
        "per": 4
    })
    # 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 audio2text(filepath):
    res = client.asr(get_file_content(filepath), 'pcm', 16000, {
        'dev_pid': 1536,
    })
    print(res)
    return res.get("result")[0]

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 goto_tl(text,uid):
    URL = "http://openapi.tuling123.com/openapi/api/v2"
    import requests
    data = {
        "perception": {
            "inputText": {
                "text": "你叫什么名字"
            }
        },
        "userInfo": {
            "apiKey": "be41cf8596a24aec95b0e86be895cfa9",
            "userId": "123"
        }
    }

    data["perception"]["inputText"]["text"] = text
    data["userInfo"]["userId"] = uid
    res = requests.post(URL, json=data)

    # print(res.content)
    # print(res.text)
    print(res.json())
    return res.json().get("results")[0].get("values").get("text")