更新记录

1.5.4（2026-06-05）

• 鸿蒙端离线识别模式增加punctuationModel标点恢复模型配置

1.5.3（2026-06-03）

• 安卓端离线识别模式增加punctuationModel标点恢复模型配置

1.5.2（2026-06-03）

• 细节优化

平台兼容性

uni-app(4.87)

uni-app x(4.87)

xwq-sherpa-onnx

开发文档说明

插件功能 （兼容安卓、鸿蒙端、iOS）

• 实时语音识别
• 离线语音识别（wav音频文件转文本识别）
• 支持热词检测
• 支持VAD检测
• 支持sense_voice
• 支持拼音词组替换fst
• 支持关键字配置

ASR模型

• ASR模型下载地址：ASR模型下载
• Transducer模型中英双语模型可以下载 sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20
• Paraformer模型双语模型可以下载（推荐）https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-paraformer-bilingual-zh-en.tar.bz2

KWS关键词模型（唤醒词场景专用）

• KWS 模型下载地址：KWS模型下载
• 推荐使用 sherpa-onnx-kws-zipformer-wenetspeech-3.3M 模型
• keyword 文件格式为：关键词 阈值，阈值范围 0-1，越小越灵敏

鸿蒙端使用特别说明

 * 鸿蒙端使用sherpa_onnx语音识别特别说明： 
 * 将路径：unpackage\dist\dev\app-harmony\entry\build-profile.json5文件拷贝到根目录harmony-configs/entry下（目录没有的需要手动创建）
 * 
 * 在harmony-configs/entry/build-profile.json5中配置参数字段：buildOption下面新增以下内容
 * "sourceOption": {
 *     "workers": [
 *      '../uni_modules/xwq-sherpa-onnx/utssdk/app-harmony/onnxWorkers.ets'
 *    ]
 *  }

 模型资源需要放在插件目录resources/rawfile/目录下

• 初始化模型参数说明

• 初始化Onnx失败回调函数fail参数说明

• hotwords.txt热词文件内容如下设置

  
  打开豆包
  语音识别
  打开灯光
  关闭灯光
  关闭空调暖气
  频繁
  礼拜二

#### 实时语音识别使用步骤

* `1.初始化模型识别器`

```html
uesSherpaOnnx({
    mode: 'online',
    modelType: "transducer",
    model: '',
    tokens: staticPath + "tokens.txt",
    encoder: staticPath + "encoder-epoch-99-avg-1.int8.onnx",
    decoder: staticPath + "decoder-epoch-99-avg-1.onnx",
    joiner: staticPath + "joiner-epoch-99-avg-1.int8.onnx",
    success: () => {
        console.log('初始化成功')
        uni.hideLoading()
    },
    fail: (res) => {
        console.log(res)
    }
})

• 2.设置识别结果回调监听函数

setListner() {
    setRecognizerResultListner((res, final) => {
        console.log('识别结果====', res)
        console.log('final====', final)
        this.content = res
    })
}

• 3.开始语音识别

start() {
    startRecognizer()
}

离线语音识别（识别wav音频文件）使用步骤

• 1.初始化模型识别器

initOfflineOnnx() {
    uni.showLoading({
        title: '模型加载中...',
    })
    let path = '/static/offlineOnnxModel';
    const staticPath = plus.io.convertLocalFileSystemURL(path);
    console.log('staticPath===', staticPath)
    uesSherpaOnnx({
        mode: 'offline',
        modelType: "zipformer2ctc",
        model: '',
        tokens: staticPath+'/' + "tokens.txt",
        encoder: staticPath+'/' + "encoder-epoch-34-avg-19.int8.onnx",
        decoder: staticPath+'/' + "decoder-epoch-34-avg-19.onnx",
        joiner: staticPath+'/' + "joiner-epoch-34-avg-19.int8.onnx",
        success: () => {
            console.log('初始化成功')
            uni.hideLoading()
        },
        fail: (res) => {
            console.log(res)
        }
    })
}

• 2.开始识别wav音频文件(需要单声道、16kHz、16位 PCM 格式)

recognizerFile() {
    let path = '/static/offlineOnnxModel/';
    const filePath = plus.io.convertLocalFileSystemURL(path) + '1.wav';
    startOfflineRecognizerFile(filePath, (result) => {
        console.log('识别结果===', result)
        this.content = result
    })
}

• 在不使用的时候可以停止识别

stopRecognizer()  //停止实时语音识别
stopOfflineRecognizerFile() //停止音频文件识别

destroyRecognizer()  //页面销毁时调用此方法销毁实例，释放内存占用

KWS唤醒词场景说明

• keyword关键字说明:推荐直接使用下载模型里的keywords文件内容。若手动编写，请按 sherpa-onnx 的拼音 token 粒度书写，不是简单的单字符拆分；例如 zh、ch、sh 这类整体声母保持原样。
• 格式说明:
• 1. 每条关键词通常写成：拼音序列 + 空格 + @显示文本
• 2. 多条关键词通常使用换行分隔
• 3. 例如：y ǎn y uán @演员、zh ī m íng @知名，这里的 zh 不是写错，它本身就是整体声母 token
• 4. 可以直接传关键词文件路径 keyword，也可以直接传关键词字符串内容 keywordsText；keywordsText 的内容格式必须与 keywords 文件原文完全一致

  n ǐ h ǎo j ūn g ē @你好军哥
  d àn g ē d àn g ē @蛋哥蛋哥
  x iǎo ài t óng x ué @小爱同学
  n ǐ h ǎo w èn w èn @你好问问
  x iǎo y ì x iǎo y ì @小艺小艺
  x iǎo m ǐ x iǎo m ǐ @小米小米
  l ín m ěi l ì @林美丽
  n ǐ h ǎo x ī x ī @你好西西
  x iǎo áo t óng x ué @小鳌同学
  n ǐ h ǎo x iǎo áo @你好小鳌
  x iǎo áo x iǎo áo @小鳌小鳌

• 也支持直接传关键词字符串，字符串内容格式与 keywords 文件内容一致，多条关键词使用换行分隔

uesSherpaOnnx({
    mode: 'online',
    modelType: 'transducer',
    encoder: staticPath + 'encoder.onnx',
    decoder: staticPath + 'decoder.onnx',
    joiner: staticPath + 'joiner.onnx',
    tokens: staticPath + 'tokens.txt',
    keywordsText: 'y ǎn y uán @演员\nzh ī m íng @知名',
    success: () => {},
    fail: (err) => {}
})

• 具体用法如下

<template>
    <view>
        <button @click="initOnnx">初始化Onnx</button>
        <button @click="setListner">设置结果监听</button>
        <button @click="removeRecognizerResultListner">移除结果监听</button>
        <button @click="start">开始识别</button>
        <button @click="stop">停止识别</button>
    </view>
</template>

<script>
    import {
        startRecognizer,
        uesSherpaOnnx,
        stopRecognizer,
        setRecognizerResultListner,
        removeOnRecognizerResultListner
    } from "@/uni_modules/xwq-sherpa-onnx";
    export default {
        data() {
            return {
                content: ""
            }
        },
        methods: {
            // 在线识别
            initOnnx() {
                let path = '/static/onlineOnnxModel/kws/';
                const staticPath = plus.io.convertLocalFileSystemURL(path);

                uesSherpaOnnx({
                    mode: 'online',
                    modelType:"zipformerCCC",
                    encoder:  staticPath +"encoder-epoch-12-avg-2-chunk-16-left-64.onnx",
                    decoder:  staticPath +"decoder-epoch-12-avg-2-chunk-16-left-64.onnx",
                    joiner:  staticPath +"joiner-epoch-12-avg-2-chunk-16-left-64.int8.onnx",
                    tokens:  staticPath +"tokens.txt",
                    keyword:staticPath + "keywords.txt",
                    success: () => {
                        console.log('***初始化成功****')
                    },
                    fail: (res) => {
                        console.log(res)
                    }
                })
            },
            //开始识别
            start() {
                startRecognizer()
            },
            //停止识别
            stop() {
                stopRecognizer()
            },
            //设置监听
            setListner() {
                setRecognizerResultListner((res, final) => {
                    console.log('识别结果====', res)
                    this.content = res
                })
            },
            //移除监听
            removeRecognizerResultListner() {
                removeOnRecognizerResultListner()
            }

        }
    }
</script>

<style>
    .title {
        margin: 20px 0 10px 0;
    }

    .content {
        height: 100%;
        border: 1px solid #ccc;
        background-color: #f2f2f2;
    }
</style>

uniappX项目中完整示例

<template>
    <view>
        <button @click="initOnnx">初始化Onnx</button>
        <button @click="setListner">设置结果监听</button>
        <button @click="removeRecognizerResultListner">移除结果监听</button>
        <button @click="start">开始识别</button>
        <button @click="stop">停止识别</button>
        <button @click="destroy">销毁实例</button>
        <button @click="initOfflineOnnx">初始化离线语音识别器</button>
        <button @click="recognizerFile">开始识别WAV File音频文件</button>
        <button @click="stopRecognizerFile">停止识别WAV File音频文件</button>
        <view style="padding:10px;">
            <view class="title">
                <text>识别结果:</text>
            </view>
            <view class="content">
                <textarea :value="content" disabled style="padding:15px;background-color: #f2f2f2;width: 100%;"></textarea>
                <!-- <text v-for="(i,k) in resultArr" :key="k"></text> -->
            </view>
        </view>
    </view>
</template>

<script setup>
    import {
        startRecognizer,
        uesSherpaOnnx,
        stopRecognizer,
        setRecognizerResultListner,
        removeOnRecognizerResultListner,
        startOfflineRecognizerFile,
        stopOfflineRecognizerFile,
        destroyRecognizer
    } from "@/uni_modules/xwq-sherpa-onnx";
    import { UseSherpaOnnxOptions } from "@/uni_modules/xwq-sherpa-onnx/utssdk/interface.uts";

    const content=ref('');

    // 在线识别
    const initOnnx=()=> {
        uni.showLoading({
            title: '模型加载中...',
        })

        uesSherpaOnnx({
            mode: 'online', //识别方式
            modelType: "transducer",//模型类型，支持'paraformer' | 'transducer' | 'zipformer'
            model: '',
            tokens: "/static/onlineOnnxModel/tokens.txt",
            encoder: "/static/onlineOnnxModel/encoder-epoch-99-avg-1.int8.onnx",
            decoder: "/static/onlineOnnxModel/decoder-epoch-99-avg-1.onnx",
            joiner: "/static/onlineOnnxModel/joiner-epoch-99-avg-1.int8.onnx",
            hotwords:'hotwords.txt', //热词内容文件
            bpeVocab:'bpe.vocab', //热词解码文件（和热词配合使用）
            success: () => {
                console.log('初始化成功')
                uni.hideLoading()
            },
            fail: (res) => {
                console.log(res)
            }
        } as UseSherpaOnnxOptions)
    };
    //离线识别
    const initOfflineOnnx=()=> {
        uni.showLoading({
            title: '模型加载中...',
        })
        uesSherpaOnnx({
            mode: 'offline',
            modelType: "zipformer2ctc",
            model: '',
            tokens: "/static/offlineOnnxModel/tokens.txt",
            encoder: "/static/offlineOnnxModel/encoder-epoch-34-avg-19.int8.onnx",
            decoder: "/static/offlineOnnxModel/decoder-epoch-34-avg-19.onnx",
            joiner: "/static/offlineOnnxModel/joiner-epoch-34-avg-19.int8.onnx",
            success: () => {
                console.log('初始化成功')
                uni.hideLoading()
            },
            fail: (res) => {
                console.log(res)
            }
        } as UseSherpaOnnxOptions)
    };
    //开始识别
    const start=()=> {
        startRecognizer()
    };
    //停止识别
    const stop=()=> {
        stopRecognizer()
    };
    //在不使用时或页面销毁时需调用此方法销毁实例
    const destroy=()=>{
        destroyRecognizer()
    };
    //设置监听
    const setListner=()=> {
        setRecognizerResultListner((res:string, final:boolean) => {
            console.log('识别结果====', res)
            console.log('final====', final)
            content.value = res
        })
    };
    //移除监听
    const removeRecognizerResultListner=()=> {
        removeOnRecognizerResultListner()
    };

    //离线识别file(wav格式音频)
    const recognizerFile=()=> {
        const filePath = '/static/offlineOnnxModel/1.wav';
        startOfflineRecognizerFile(filePath, (result) => {
            console.log('识别结果===', result)
            content.value = result
        })

    };

    //停止离线识别wav file
    const stopRecognizerFile=()=> {
        stopOfflineRecognizerFile()
    };

</script>

<style>

</style>

uniapp项目中完整示例

<template>
    <view>
        <button @click="initOnnx">初始化Onnx</button>
        <button @click="setListner">设置结果监听</button>
        <button @click="removeRecognizerResultListner">移除结果监听</button>
        <button @click="start">开始识别</button>
        <button @click="stop">停止识别</button>
        <button @click="destroy">销毁实例</button>
        <button @click="initOfflineOnnx">初始化离线语音识别器</button>
        <button @click="recognizerFile">开始识别WAV File音频文件</button>
        <button @click="stopRecognizerFile">停止识别WAV File音频文件</button>
        <view style="padding:10px;">
            <view class="title">
                <text>识别结果:</text>
            </view>
            <view class="content">
                <textarea :value="content" disabled style="padding:15px;"></textarea>
                <!-- <text v-for="(i,k) in resultArr" :key="k"></text> -->
            </view>
        </view>
    </view>
</template>

<script>
    import {
        startRecognizer,
        uesSherpaOnnx,
        stopRecognizer,
        setRecognizerResultListner,
        removeOnRecognizerResultListner,
        startOfflineRecognizerFile,
        stopOfflineRecognizerFile,
        destroyRecognizer
    } from "@/uni_modules/xwq-sherpa-onnx";
    export default {
        data() {
            return {
                resultArr: [],
                content: ""
            }
        },

        mounted() {},
        methods: {
            // 在线识别
            initOnnx() {
                uni.showLoading({
                    title: '模型加载中...',
                })
                let path = '/static/onlineOnnxModel';
                const staticPath = plus.io.convertLocalFileSystemURL(path);
                console.log('staticPath===', staticPath)

                uesSherpaOnnx({
                    mode: 'online',//识别方式
                    modelType: "transducer",//模型类型，支持'paraformer' | 'transducer' | 'zipformer'
                    model: '',
                    tokens: staticPath+'/' + "tokens.txt",
                    encoder: staticPath+'/' + "encoder-epoch-99-avg-1.int8.onnx",
                    decoder: staticPath+'/' + "decoder-epoch-99-avg-1.onnx",
                    joiner: staticPath+'/' + "joiner-epoch-99-avg-1.int8.onnx",
                    hotwords:'hotwords.txt', //热词内容文件
                    bpeVocab:'bpe.vocab', //热词解码文件（和热词配合使用）
                    success: () => {
                        console.log('初始化成功')
                        uni.hideLoading()
                    },
                    fail: (res) => {
                        console.log(res)
                    }
                })
            },
            //离线识别
            initOfflineOnnx() {
                uni.showLoading({
                    title: '模型加载中...',
                })
                let path = '/static/offlineOnnxModel';
                const staticPath = plus.io.convertLocalFileSystemURL(path);
                console.log('staticPath===', staticPath)
                uesSherpaOnnx({
                    mode: 'offline',
                    modelType: "zipformer2ctc",
                    model: '',
                    tokens: staticPath+'/' + "tokens.txt",
                    encoder: staticPath+'/' + "encoder-epoch-34-avg-19.int8.onnx",
                    decoder: staticPath+'/' + "decoder-epoch-34-avg-19.onnx",
                    joiner: staticPath+'/' + "joiner-epoch-34-avg-19.int8.onnx",
                    success: () => {
                        console.log('初始化成功11')
                        uni.hideLoading()
                    },
                    fail: (res) => {
                        console.log(res)
                    }
                })
            },
            //开始识别
            start() {
                startRecognizer()
            },
            //停止识别
            stop() {
                stopRecognizer()
            },
            //在不使用时或页面销毁时需调用此方法销毁实例
            destroy(){
                destroyRecognizer()
            },
            //设置监听
            setListner() {
                setRecognizerResultListner((res, final) => {
                    console.log('识别结果====', res)
                    console.log('final====', final)
                    this.content = res
                })
            },
            //移除监听
            removeRecognizerResultListner() {
                removeOnRecognizerResultListner()
            },

            //离线识别file(wav格式音频)
            recognizerFile() {
                let path = '/static/offlineOnnxModel/';
                const filePath = plus.io.convertLocalFileSystemURL(path) + '1.wav';
                startOfflineRecognizerFile(filePath, (result) => {
                    console.log('识别结果===', result)
                    this.content = result
                })

            },

            //停止离线识别wav file
            stopRecognizerFile() {
                stopOfflineRecognizerFile()
            }

        }
    }
</script>

<style>
    .title {
        margin: 20px 0 10px 0;
    }

    .content {
        height: 100%;
        border: 1px solid #ccc;
        background-color: #f2f2f2;
    }
</style>

VAD语音检测+paraformer模型用例

<template>
    <view>
        <button @click="initOnnx">初始化Onnx</button>
        <button @click="setListner">设置结果监听</button>
        <button @click="removeRecognizerResultListner">移除结果监听</button>
        <button @click="start">开始识别</button>
        <button @click="stop">停止识别</button>
        <button @click="destroy">销毁实例</button>
        <view style="padding:10px;">
            <view class="title">
                <text>识别结果:</text>
            </view>
            <view class="content">
                <textarea :value="content" disabled style="padding:15px;"></textarea>
            </view>
        </view>
    </view>
</template>

<script>
    import {
        startRecognizer,
        uesSherpaOnnx,
        stopRecognizer,
        setRecognizerResultListner,
        removeOnRecognizerResultListner,
        destroyRecognizer
    } from "@/uni_modules/xwq-sherpa-onnx";
    export default {
        data() {
            return {
                content: "", // textarea 最终展示文本
                finalLines: [], // 已确认结束的句子列表
                currentPartial: '', // 当前流式输出中的临时句子
                nextLineNumber: 1, // 下一条最终句子的显示序号
            }
        },
        methods: {
            resetDisplayState() {
                this.content = ''
                this.finalLines = []
                this.currentPartial = ''
                this.nextLineNumber = 1
            },
            renderContent() {
                // 已完成句子和当前 partial 分开渲染，避免流式结果重复叠加。
                const lines = this.finalLines.slice()
                if (this.currentPartial) {
                    lines.push(`${this.nextLineNumber}. ${this.currentPartial}`)
                }
                this.content = lines.join('\n')
                if (!this.currentPartial && this.finalLines.length > 0) {
                    this.content += '\n'
                }
            },
            // 在线识别
            initOnnx() {
                let path = '/static/paraformer';
                const staticPath = plus.io.convertLocalFileSystemURL(path);

                uesSherpaOnnx({
                    mode: 'online',
                    modelType: "paraformer",
                    tokens: staticPath+'/' + "tokens.txt",
                    encoder: staticPath+'/' + "encoder.int8.onnx",
                    decoder: staticPath+'/' + "decoder.int8.onnx",
                    joiner: "",
                    vad: staticPath+'/' + "silero_vad.onnx",
                    numThreads:2,
                    success: () => {
                        uni.hideLoading()
                    },
                    fail: (res) => {
                        console.log("初始化失败===",res)
                    }
                })
            },

            //开始识别
            start() {
                this.resetDisplayState()
                startRecognizer()
            },
            //停止识别
            stop() {
                stopRecognizer()
            },
            //销毁实例
            destroy(){
                this.resetDisplayState()
                destroyRecognizer()
            },
            //设置监听
            setListner() {
                // res: 当前回调文本；final: 是否为当前句的最终结果。
                setRecognizerResultListner((res, final) => {
                    console.log('识别结果====', res)
                    console.log('final====', final)
                    const text = (res || '').trim()
                    if (!text) {
                        return
                    }

                    if (!final) {
                        // partial 只替换当前行，不直接追加到历史内容。
                        this.currentPartial = text
                        this.renderContent()
                        return
                    }

                    const finalLine = `${this.nextLineNumber}. ${text}`
                    if (this.finalLines[this.finalLines.length - 1] !== finalLine) {
                        // final 才真正落行并递增序号。
                        this.finalLines.push(finalLine)
                        this.nextLineNumber += 1
                    }
                    this.currentPartial = ''
                    this.renderContent()

                })
            },
            //移除监听
            removeRecognizerResultListner() {
                removeOnRecognizerResultListner()
            },

        }
    }
</script>

<style>
    .title {
        margin: 20px 0 10px 0;
    }

    .content {
        height: 100%;
        border: 1px solid #ccc;
        background-color: #f2f2f2;
    }
</style>

实时语音识别+VAD语音检测+hotwords用法（针对语音唤醒场景）

hotwords.txt 文件内容说明：
格式：内容+空格+冒号+分数
例如：小度小度 :10
注意：后面的分数越高越容易触发，但太高也容易导致其他词被屏蔽无法触发，请设置合理值使用

silero_vad.onnx下载地址：https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx

<template>
    <view>
        <button @click="initOnnx">初始化Onnx</button>
        <button @click="setListner">设置结果监听</button>
        <button @click="removeRecognizerResultListner">移除结果监听</button>
        <button @click="start">开始识别</button>
        <button @click="stop">停止识别</button>
        <view style="padding:10px;">
            <view class="title">
                <text>识别结果:</text>
            </view>
            <view class="content">
                <textarea :value="content" :disabled="true" :auto-height="true" style="padding:15px;"></textarea>
            </view>
        </view>
    </view>
</template>

<script>
    import {
        startRecognizer,
        uesSherpaOnnx,
        stopRecognizer,
        setRecognizerResultListner,
        removeOnRecognizerResultListner
    } from "@/uni_modules/xwq-sherpa-onnx";
    export default {
        data() {
            return {
                content: ""
            }
        },

        methods: {
            // 在线识别
            initOnnx() {
                let path = '/static/onlineOnnxModel/transducer/';
                uesSherpaOnnx({
                    mode: 'online',
                    modelType: "transducer",
                    hotwords:path+'hotwords.txt', //热词内容文件
                    bpeVocab:path +'bpe.vocab', //热词解码文件（transducer模型自带）
                    encoder: path + "encoder-epoch-99-avg-1.int8.onnx",//transducer模型文件
                    decoder: path + "decoder-epoch-99-avg-1.onnx",//transducer模型文件
                    joiner: path + "joiner-epoch-99-avg-1.int8.onnx",//transducer模型文件
                    tokens: path + "tokens99.txt", //transducer模型tokens
                    vad:path + "silero_vad.onnx",//VAD模型文件
                    isCopyFile:false,
                    success: () => {
                        console.log('初始化成功')
                    },
                    fail: (res) => {
                        console.log(res)
                    }
                })
            },

            //开始识别
            start() {
                startRecognizer()
            },
            //停止识别
            stop() {
                stopRecognizer()
            },
            //设置监听
            setListner() {
                setRecognizerResultListner((res, final) => {
                    console.log('识别结果====', res)
                    console.log('final====', final)
                    this.content = res
                })
            },
            //移除监听
            removeRecognizerResultListner() {
                removeOnRecognizerResultListner()
            }
        }
    }
</script>

<style>
    .title {
        margin: 20px 0 10px 0;
    }

    .content {
        height: 100%;
        border: 1px solid #ccc;
        background-color: #f2f2f2;
    }
</style>

sense_voice模型+vad语音检测使用方式

silero_vad模型下载地址：https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
silero_voice模型下载地址：https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-int8-2024-07-17.tar.bz2

<template>
    <view>
        <button @click="initOnnx">初始化Onnx</button>
        <button @click="setListner">设置结果监听</button>
        <button @click="removeRecognizerResultListner">移除结果监听</button>
        <button @click="start">开始识别</button>
        <button @click="stop">停止识别</button>
        <view style="padding:10px;">
            <view class="title">
                <text>识别结果:</text>
            </view>
            <view class="content">
                <textarea :value="content" :disabled="true" :auto-height="true" style="padding:15px;"></textarea>
            </view>
        </view>
    </view>
</template>

<script>
    import {
        startRecognizer,
        uesSherpaOnnx,
        stopRecognizer,
        setRecognizerResultListner,
        removeOnRecognizerResultListner
    } from "@/uni_modules/xwq-sherpa-onnx";
    export default {
        data() {
            return {
                content: ""
            }
        },

        methods: {
            // 在线识别
            initOnnx() {
                let path = '/static/onlineOnnxModel/transducer/';
                uesSherpaOnnx({
                    mode: 'online',
                    modelType: "sense_voice",
                    tokens: path + "tokens.txt",
                    model: path + "model.int8.onnx",// silero_voice模型模型文件
                    vad:path + "silero_vad.onnx",//silero_vad模型模型
                    isCopyFile:false,
                    success: () => {
                        console.log('初始化成功')
                        // uni.hideLoading()
                    },
                    fail: (res) => {
                        console.log(res)
                    }
                })
            },

            //开始识别
            start() {
                startRecognizer()
            },
            //停止识别
            stop() {
                stopRecognizer()
            },
            //设置监听
            setListner() {
                setRecognizerResultListner((res, final) => {
                    console.log('识别结果====', res)
                    console.log('final====', final)
                    this.content = res
                })
            },
            //移除监听
            removeRecognizerResultListner() {
                removeOnRecognizerResultListner()
            }
        }
    }
</script>

<style>
    .title {
        margin: 20px 0 10px 0;
    }

    .content {
        height: 100%;
        border: 1px solid #ccc;
        background-color: #f2f2f2;
    }
</style>

其他插件预览

• （兼容安卓、IOS）UTS 车牌识别插件

• (兼容IOS、安卓、鸿蒙)二维码、条码识别，mlkit扫码插件 毫秒级识别效率，支持多码识别，连续扫

• TTS（安卓、ios、鸿蒙、web）将文字转成语音播报

• （兼容安卓、IOS端）离线语音识别插件、支持离线语音

• （UNIAPPX原生播放器）仿抖音视频刷刷刷 刷视频 自动滑动视频，短剧刷视频必备神器

• (安卓、IOS) PDF预览插件

• (兼容安卓、鸿蒙端、iOS)onnx 大模型实时语音识别 wav音频文件转文本

• （兼容安卓、鸿蒙）录音插件，支持pcm实时回调，wav转换，后台录音

• （nvue）app端仿抖音短视频 丝滑切换视频效果，无限数据加载

• 兼容安卓、IOS端支持全局消息弹窗插件

• (支持安卓和IOS端)原生UTS组件svg插件

• （兼容IOS和安卓Web三端）SVGA 动画组件 支持自定义文本、图片，图层隐藏功能

• 系统权限验证、获取、跳转

• （兼容安卓、IOS、鸿蒙）二维码 qrcode 自定义logo图片 颜色可自定义

• 仿抖音短视频微信小程序 丝滑切换视频效果，无限数据加载不卡顿，咨询进IM交流群