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.Net 使用OpenAI开源语音识别模型Whisper

.Net 使用OpenAI开源语音识别模型 Whisper

前言

Open AI在2022年9月21日开源了号称其英文语音辨识能力已达到人类水准的 Whisper 神经网络，且它亦支持其它98种语言的自动语音辨识。 Whisper系统所提供的自动语音辨识（Automatic Speech Recognition，ASR）模型是被训练来运行语音辨识与翻译任务的，它们能将各种语言的语音变成文本，也能将这些文本翻译成英文。

whisper的核心功能语音识别，对于大部分人来说，可以帮助我们更快捷的将会议、讲座、课堂录音整理成文字稿；对于影视爱好者，可以将无字幕的资源自动生成字幕，不用再苦苦等待各大字幕组的字幕资源；对于外语口语学习者，使用whisper翻译你的发音练习录音，可以很好的检验你的口语发音水平。 当然，各大云平台都提供语音识别服务，但是基本都是联网运行，个人隐私安全总是有隐患，而whisper完全不同，whisper完全在本地运行，无需联网，充分保障了个人隐私，且whisper识别准确率相当高。

Whisper是C++写的，sandrohanea 对其进行了.Net封装。

本文旨在梳理我在.net web 项目中使用开源语音识别模型Whisper的过程，方便下次翻阅，如对您有所帮助不胜荣幸~

.Net Web 项目版本为：.Net 6.0

文章目录

安装Whisper.net包

首先我们在Core项目中安装Whisper.net包。在NuGet包管理器中搜索并安装【Whisper.net】和【Whisper.net.Runtime】包，如下图所示:

注意，我们要找的是【Whisper.net】和【Whisper.net.Runtime】，不是、【WhisperNet】、【Whisper.Runtime】。

image-20230530162444326

下载模型文件

前往Hugging Face下载Whisper的模型文件，一共有 ggml-tiny.bin、ggml-base.bin、ggml-small.bin、ggml-medium.bin、ggml-large.bin 5个模型，文件大小依次变大，识别率也依次变大。此外，【xxx.en.bin】是英文模型，【xxx.bin】支持各国语言。

我们将模型文件放到项目中即可，我这里是放到Web项目的wwwroot下：

image-20230530165740596

新建Whisper帮助类

WhisperHelper.cs

image-20230530170227200

using Whisper.net;
using System.IO;
using System.Collections.Generic;
using Market.Core.Enum;

namespace Market.Core.Util
{
    public class WhisperHelper
    {
        public static List<SegmentData> Segments { get; set; }
        public static WhisperProcessor Processor { get; set; }

        public WhisperHelper(ASRModelType modelType)
        {
            if(Segments == null || Processor == null)
            {
                Segments = new List<SegmentData>();

                var binName = "ggml-large.bin";
                switch (modelType)
                {
                    case ASRModelType.WhisperTiny:
                        binName = "ggml-tiny.bin";
                        break;
                    case ASRModelType.WhisperBase:
                        binName = "ggml-base.bin";
                        break;
                    case ASRModelType.WhisperSmall:
                        binName = "ggml-small.bin";
                        break;
                    case ASRModelType.WhisperMedium:
                        binName = "ggml-medium.bin";
                        break;
                    case ASRModelType.WhisperLarge:
                        binName = "ggml-large.bin";
                        break;
                    default:
                        break;
                }
                var modelFilePath = $"wwwroot/WhisperModel/{binName}";
                var factory = WhisperFactory.FromPath(modelFilePath);
                var builder = factory.CreateBuilder()
                                     .WithLanguage("zh") //中文
                                     .WithSegmentEventHandler(Segments.Add);
                var processor = builder.Build();
                Processor = processor;
            }
        }

        /// <summary>
        /// 完整的语音识别 单例实现
        /// </summary>
        /// <returns></returns>
        public string FullDetection(Stream speechStream)
        {
            Segments.Clear();
            var txtResult = string.Empty;

            //开始识别
            Processor.Process(speechStream);

            //识别结果处理
            foreach (var segment in Segments)
            {
                txtResult += segment.Text + "\n";
            }
            Segments.Clear();
            return txtResult;
        }
    }
}

ModelType.cs

不同的模型名字不一样，需要用一个枚举类作区分：

image-20230530170534542

using System.ComponentModel;

namespace Market.Core.Enum
{
    /// <summary>
    /// ASR模型类型
    /// </summary>
    [Description("ASR模型类型")]
    public enum ASRModelType
    {
        /// <summary>
        /// ASRT
        /// </summary>
        [Description("ASRT")]
        ASRT = 0,

        /// <summary>
        /// WhisperTiny
        /// </summary>
        [Description("WhisperTiny")]
        WhisperTiny = 100,

        /// <summary>
        /// WhisperBase
        /// </summary>
        [Description("WhisperBase")]
        WhisperBase = 110,

        /// <summary>
        /// WhisperSmall
        /// </summary>
        [Description("WhisperSmall")]
        WhisperSmall = 120,

        /// <summary>
        /// WhisperMedium
        /// </summary>
        [Description("WhisperMedium")]
        WhisperMedium = 130,

        /// <summary>
        /// WhisperLarge
        /// </summary>
        [Description("WhisperLarge")]
        WhisperLarge = 140,

        /// <summary>
        /// PaddleSpeech
        /// </summary>
        [Description("PaddleSpeech")]
        PaddleSpeech = 200,
    }
}

后端接受音频并识别

后端接口接受音频二进制字节码，并使用Whisper帮助类进行语音识别。

image-20230530171221152

关键代码如下：

public class ASRModel
{
        public string samples { get; set; }
}

/// <summary>
/// 语音识别
/// </summary>
[HttpPost]
[Route("/auth/speechRecogize")]
public async Task<IActionResult> SpeechRecogizeAsync([FromBody] ASRModel model)
{
    ResultDto result = new ResultDto();
    byte[] wavData = Convert.FromBase64String(model.samples);
    model.samples = null;   //内存回收
    // 使用Whisper模型进行语音识别
    var speechStream = new MemoryStream(wavData);
    var whisperManager = new WhisperHelper(model.ModelType);
    var textResult = whisperManager.FullDetection(speechStream);
    speechStream.Dispose();//内存回收
    speechStream = null;
    wavData = null; //内存回收
    result.Data = textResult;
    return Json(result.OK());
}

前端页面上传音频

前端主要做一个音频采集的工作，然后将音频文件转化成二进制编码传输到后端Api接口中

前端页面如下：

image-20230530134802045

页面代码如下：

@{
    Layout = null;
}
@using Karambolo.AspNetCore.Bundling.ViewHelpers
@addTagHelper *, Karambolo.AspNetCore.Bundling
@addTagHelper *, Microsoft.AspNetCore.Mvc.TagHelpers
<!DOCTYPEhtml><html><head><metacharset="utf-8"/><title>语音录制</title><metaname="viewport"content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0"><environmentnames="Development"><linkhref="~/content/plugins/element-ui/index.css"rel="stylesheet"/><scriptsrc="~/content/plugins/jquery/jquery-3.4.1.min.js"></script><scriptsrc="~/content/js/matomo.js"></script><scriptsrc="~/content/js/slick.min.js"></script><scriptsrc="~/content/js/masonry.js"></script><scriptsrc="~/content/js/instafeed.min.js"></script><scriptsrc="~/content/js/headroom.js"></script><scriptsrc="~/content/js/readingTime.min.js"></script><scriptsrc="~/content/js/script.js"></script><scriptsrc="~/content/js/prism.js"></script><scriptsrc="~/content/js/recorder-core.js"></script><scriptsrc="~/content/js/wav.js"></script><scriptsrc="~/content/js/waveview.js"></script><scriptsrc="~/content/js/vue.js"></script><scriptsrc="~/content/plugins/element-ui/index.js"></script><scriptsrc="~/content/js/request.js"></script></environment><environmentnames="Stage,Production">
        @await Styles.RenderAsync("~/bundles/login.css")
        @await Scripts.RenderAsync("~/bundles/login.js")
    </environment><style>html,
        body{margin: 0;height: 100%;}body{padding: 20px;box-sizing: border-box;}audio{display:block;}audio + audio{margin-top: 20px;}.el-textarea .el-textarea__inner{color: #000 !important;font-size: 18px;font-weight: 600;}#app{height: 100%;}.content{height:calc(100% - 130px);overflow: auto;}.content > div{margin: 10px 0 20px;}.press{height: 40px;line-height: 40px;border-radius: 5px;border: 1px solid #dcdfe6;cursor: pointer;width: 100%;text-align: center;background: #fff;}</style></head><body><divid="app"><divstyle="display: flex;justify-content: space-between;align-items: center;"><center>{{isPC? '我是电脑版' : '我是手机版'}}</center><centerstyle="margin: 10px 0"><el-radio-groupv-model="modelType"><el-radio:label="0">ASRT</el-radio><el-radio:label="100">WhisperTiny</el-radio><el-radio:label="110">WhisperBase</el-radio><el-radio:label="120">WhisperSmall</el-radio><el-radio:label="130">WhisperMedium</el-radio><el-radio:label="140">WhisperLarge</el-radio><el-radio:label="200">PaddleSpeech</el-radio></el-radio-group></center><el-buttontype="primary"size="small"onclick="window.location.href ='/'">返回</el-button></div><divclass="content"id="wav_pannel">
            @*{{textarea}}*@
        </div><divstyle="margin-top: 20px"></div><centerstyle="height: 40px;"><h4id="msgbox"v-if="messageSatuts">{{message}}</h4></center><buttonclass="press"v-on:touchstart="start"v-on:touchend="end"v-if="!isPC">
            按住 说话
        </button><buttonclass="press"v-on:mousedown="start"v-on:mouseup="end"v-else>
            按住 说话
        </button></div></body></html><script>var blob_wav_current;var rec;varrecOpen=function(success){
        rec =Recorder({type:"wav",sampleRate:16000,bitRate:16,onProcess:(buffers, powerLevel, bufferDuration, bufferSampleRate, newBufferIdx, asyncEnd)=>{}});
        rec.open(()=>{
            success &&success();},(msg, isUserNotAllow)=>{
            app.textarea =(isUserNotAllow ?"UserNotAllow，":"")+"无法录音:"+ msg;});};var app =newVue({el:'#app',data:{textarea:'',message:'',messageSatuts:false,modelType:0,},computed:{isPC(){var userAgentInfo = navigator.userAgent;var Agents =["Android","iPhone","SymbianOS","Windows Phone","iPod","iPad"];var flag =true;for(var i =0; i < Agents.length; i++){if(userAgentInfo.indexOf(Agents[i])>0){
                        flag =false;break;}}return flag;}},methods:{start(){
                app.message ="正在录音...";
                app.messageSatuts =true;recOpen(function(){
                    app.recStart();});},end(){if(rec){
                    rec.stop(function(blob, duration){
                        app.messageSatuts =false;
                        rec.close();
                        rec =null;
                        blob_wav_current = blob;var audio = document.createElement("audio");
                        audio.controls =true;var dom = document.getElementById("wav_pannel");
                        dom.appendChild(audio);
                        audio.src =(window.URL|| webkitURL).createObjectURL(blob);//audio.play();
                        app.messageSatuts =false;
                        app.upload();},function(msg){
                        console.log("录音失败:"+ msg);
                        rec.close();
                        rec =null;});
                    app.message ="录音停止";}},upload(){
                app.message ="正在上传识别...";
                app.messageSatuts =true;var blob = blob_wav_current;var reader =newFileReader();
                reader.onloadend=function(){var data ={samples:(/.+;\s*base64\s*,\s*(.+)$/i.exec(reader.result)||[])[1],sample_rate:16000,channels:1,byte_width:2,modelType: app.modelType
                    }
                    $.post('/auth/speechRecogize', data,function(res){if(res.data && res.data.statusCode ==200000){
                            app.messageSatuts =false;
                            app.textarea = res.data.text ==''?'暂未识别出来，请重新试试': res.data.text;}else{
                            app.textarea ="识别失败";}var dom = document.getElementById("wav_pannel");var div = document.createElement("div");
                        div.innerHTML = app.textarea;
                        dom.appendChild(div);$('#wav_pannel').animate({scrollTop:$('#wav_pannel')[0].scrollHeight -$('#wav_pannel')[0].offsetHeight });})};
                reader.readAsDataURL(blob);},recStart(){
                rec.start();},}})</script>

引用

whisper官网

测试离线音频转文本模型Whisper.net的基本用法

whisper.cpp的github

whisper.net的github

whisper模型下载

标签： 语音识别 人工智能 whisper
本文转载自: https://blog.csdn.net/guigenyi/article/details/130955947
版权归原作者 切糕师学AI 所有， 如有侵权，请联系我们删除。
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