app文件
from flask import Flask, request, render_template, jsonify, send_file from uuid import uuid4 import os import asr_test app = Flask(__name__) app.debug = True @app.route('/') def index(): return render_template('index.html') @app.route('/uploader', methods=['POST']) def uploader(): file = request.files.get('reco') file_name = os.path.join('audio', f'{uuid4()}.wav') file.save(file_name) ret_filename = asr_test.my_ai(file_name) print(ret_filename) return jsonify({'filename': ret_filename}) @app.route('/get_audio/<filename>') def get_audio(filename): file = os.path.join('audio', filename) return send_file(file) if __name__ == '__main__': app.run('0.0.0.0', 5000)
调用百度语音识别与语音合成接口，把传来的语言识别成文字，并调用下面的相似度接口，返回回答的文字，然后利用语音合成返回回答
from aip import AipSpeech import os from my_npl import get_score from uuid import uuid4 """ 你的 APPID AK SK """ APP_ID = '******' API_KEY = '******' SECRET_KEY = '******' client = AipSpeech(APP_ID, API_KEY, SECRET_KEY) # 读取文件 def get_file_content(filePath): any2pcm_str = f"ffmpeg -y -i {filePath} -acodec pcm_s16le -f s16le -ac 1 -ar 16000 {filePath}.pcm" os.system(any2pcm_str) with open(f"{filePath}.pcm", 'rb') as fp: return fp.read() # 识别本地文件 def my_ai(file): res = client.asr(get_file_content(file), 'pcm', 16000, { 'dev_pid': 1536, }) print(res.get('result')) print(res) question = res.get('result')[0] req = get_score(question) req = client.synthesis(req, 'zh', 1, { 'vol': 5, 'pit': 5, 'spd': 4, "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(req, dict): ret_filename = f'{uuid4()}.mp3' new_filename = os.path.join("audio", ret_filename) with open(new_filename, 'wb') as f: f.write(req) return ret_filename
调用百度ai自然语言中的短文本相似度接口，使相似的问题得到相同的答案
from aip import AipNlp from mytuling import to_tuling """ 你的 APPID AK SK """ APP_ID = '***' API_KEY = '***' SECRET_KEY = '***' client = AipNlp(APP_ID, API_KEY, SECRET_KEY) def get_score(Q): if client.simnet(Q, '你叫什么名字').get('score') > 0.7: return '我是大名鼎鼎的小王吧' elif client.simnet(Q, '你今年几岁呀').get('score') > 0.7: return '我今年已经1112岁啦' else: return to_tuling(Q)
调用图灵接口完成未设定问答的
import requests tuling_url = 'http://openapi.tuling123.com/openapi/api/v2' data = {"reqType": 0, "perception": { "inputText": { "text": "" } } , "userInfo": { "apiKey": "***", "userId": "***" } } def to_tuling(Q): data["perception"]["inputText"]['text'] = Q a = requests.post(url=tuling_url, json=data) res = a.json() print(res) return res.get("results")[0].get("values").get("text")
简单前端页面
<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Title</title> </head> <body> <audio src="" autoplay controls id="player"></audio> <p> <button onclick="start_reco()">开始录音</button> </p> <p> <button onclick="stop_reco()">停止录音</button> </p> </body> <script src="/static/Recorder.js"></script> <script src="/static/jQuery3.1.1.js"></script> <script type="text/javascript"> var serv = "http://127.0.0.1:5000"; var audio_serv = serv + "/get_audio/"; var audio_context = new AudioContext(); 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); reco = new Recorder(stream_input); } function start_reco() { reco.record(); } function stop_reco() { reco.stop(); get_audio(); reco.clear(); } function get_audio() { reco.exportWAV(function (wav_file) { // wav_file 音频文件 Blob("wav","context") console.log(wav_file); var formdata = new FormData(); formdata.append("reco", wav_file); $.ajax({ url: serv + "/uploader", type: 'post', processData: false, contentType: false, data: formdata, dataType: 'json', success: function (data) { document.getElementById("player").src = audio_serv + data.filename; } } ); }) } </script> </html>