[CF]添加音频分类

音频分类/script.js

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+// 全局变量和模型实例
+let recognizer; // 基础的 SpeechCommands recognizer
+let transferRecognizer; // 用于迁移学习的 recognizer
+const labels = []; // 用户定义的类别标签数组 (包括背景噪音)
+// 将背景噪音定义为第一个类别，其内部名称为 _background_noise_
+const BACKGROUND_NOISE_LABEL = '_background_noise_';
+const BACKGROUND_NOISE_INDEX = 0; // 仅用于本地 labels 数组索引，不直接用于collectExample
+
+let isPredicting = false; // 预测状态标志
+let isRecording = false; // 录音状态标志，防止重复点击
+const recordDuration = 1000; // 每个样本的录音时长 (毫秒)
+let isModelTrainedFlag = false; // 手动维护模型训练状态
+let predictionStopFunction = null; // 存储 transferRecognizer.listen() 返回的停止函数
+
+// UI 元素引用 (保持不变)
+const statusDiv = document.getElementById('status');
+const backgroundNoiseSampleCountSpan = document.getElementById('backgroundNoiseSampleCount');
+const recordBackgroundNoiseBtn = document.getElementById('recordBackgroundNoiseBtn');
+const categoryContainer = document.getElementById('categoryContainer');
+const newCategoryNameInput = document.getElementById('newCategoryName');
+const addCategoryBtn = document.getElementById('addCategoryBtn');
+const trainModelBtn = document.getElementById('trainModelBtn');
+const startPredictingBtn = document.getElementById('startPredictingBtn');
+const stopPredictingBtn = document.getElementById('stopPredictingBtn');
+const predictionResultDiv = document.getElementById('predictionResult');
+
+// ======================= 初始化函数 =======================
+async function init() {
+    statusDiv.innerText = '正在加载 TensorFlow.js 和 Speech Commands 模型...';
+
+    try {
+        recognizer = speechCommands.create(
+            'BROWSER_FFT' // 使用浏览器内置的 FFT 处理，性能更好
+        );
+
+        await recognizer.ensureModelLoaded();
+
+        transferRecognizer = recognizer.createTransfer('my-custom-model');
+        
+        // 只有在 transferRecognizer 创建成功后，才将背景噪音标签加入我们的 local labels 数组
+        labels.push(BACKGROUND_NOISE_LABEL); // 仅用于本地 UI 映射和预测结果查找
+
+        statusDiv.innerText = '模型加载成功！你可以开始录制背景噪音和自定义声音样本了。';
+        recordBackgroundNoiseBtn.disabled = false;
+        addCategoryBtn.disabled = false;
+        
+        trainModelBtn.disabled = true;
+        startPredictingBtn.disabled = true;
+        stopPredictingBtn.disabled = true;
+        isModelTrainedFlag = false; // 重置训练状态
+
+    } catch (error) {
+        statusDiv.innerText = `模型加载失败或麦克风无法访问: ${error.message}. 请检查麦克风权限和网络连接。`;
+        console.error('初始化失败:', error);
+        // 任何失败都禁用所有控制，直到初始化成功
+        recordBackgroundNoiseBtn.disabled = true;
+        addCategoryBtn.disabled = true;
+        trainModelBtn.disabled = true;
+        startPredictingBtn.disabled = true;
+        stopPredictingBtn.disabled = true;
+        isModelTrainedFlag = false; // 重置训练状态
+    }
+}
+
+// ======================= 批量录制样本的通用函数 =======================
+async function recordMultipleExamples(label, sampleCountSpanElement, buttonElement, countToRecord = 5) { // 默认一次录制5个样本
+    if (isRecording) {
+        statusDiv.innerText = '请等待当前录音完成...';
+        return;
+    }
+
+    isRecording = true;
+    buttonElement.disabled = true;
+    buttonElement.innerText = '正在录制...';
+    
+    for (let i = 0; i < countToRecord; i++) {
+        statusDiv.innerText = `正在录制 "${label}" 样本... (第 ${i + 1} 个 / 共 ${countToRecord} 个)`;
+        try {
+            await transferRecognizer.collectExample(
+                label,
+                { amplitudeRequired: true, durationMillis: recordDuration }
+            );
+            const exampleCounts = transferRecognizer.countExamples();
+            sampleCountSpanElement.innerText = exampleCounts[label] || 0;
+            // 在每次录音之间增加短暂延迟，以便更好地分离样本
+            if (i < countToRecord - 1) {
+                await new Promise(resolve => setTimeout(resolve, Math.max(200, recordDuration / 5))); // 至少 200ms 或录音时长的 1/5
+            }
+        } catch (error) {
+            statusDiv.innerText = `录制 "${label}" 样本失败: ${error.message}`;
+            console.error(`录制 ${label} 样本失败:`, error);
+            // 如果某个样本录制失败，则停止当前批次的录制
+            break; 
+        }
+    }
+
+    buttonElement.disabled = false;
+    buttonElement.innerText = '录制样本';
+    isRecording = false;
+    checkTrainingReadiness(); // 录制完成后检查训练就绪状态
+    statusDiv.innerText = `已为 "${label}" 收集了 ${transferRecognizer.countExamples()[label] || 0} 个样本。`;
+}
+
+// ======================= 背景噪音样本收集 =======================
+recordBackgroundNoiseBtn.onclick = async () => {
+    await recordMultipleExamples(BACKGROUND_NOISE_LABEL, backgroundNoiseSampleCountSpan, recordBackgroundNoiseBtn, 5);
+};
+
+
+// ======================= 自定义类别管理和样本收集 =======================
+
+// 添加新类别到 UI 和逻辑 (用于自定义声音)
+function addCustomCategory(categoryName) {
+    if (!categoryName) {
+        alert('类别名称不能为空！');
+        return;
+    }
+    // 检查是否与现有标签重复（包括背景噪音，尽管背景噪音不会由用户输入）
+    if (labels.some(label => label.toLowerCase() === categoryName.toLowerCase())) {
+        alert(`类别 "${categoryName}" 已经存在！`);
+        return;
+    }
+
+    // 将标签添加到本地数组以供 UI 逻辑和后续预测结果查找使用
+    labels.push(categoryName); 
+
+    // 创建类别块 UI
+    const categoryBlock = document.createElement('div');
+    categoryBlock.className = 'category-block';
+    
+    // categoryId 此时仅用于生成唯一的 ID，不直接传给 collectExample
+    const categoryId = labels.indexOf(categoryName); 
+
+    categoryBlock.innerHTML = `
+        <h3>${categoryName}</h3>
+        <p>样本数量: <span id="sampleCount-${categoryId}">0</span></p>
+        <button id="recordBtn-${categoryId}">录制样本</button>
+    `;
+    categoryContainer.appendChild(categoryBlock);
+
+    // 绑定录音按钮事件
+    const recordBtn = document.getElementById(`recordBtn-${categoryId}`);
+    const sampleCountSpan = document.getElementById(`sampleCount-${categoryId}`);
+
+    recordBtn.onclick = async () => {
+        await recordMultipleExamples(categoryName, sampleCountSpan, recordBtn, 5); 
+    };
+
+    newCategoryNameInput.value = ''; // 清空输入框
+    checkTrainingReadiness(); // 添加新类别后检查训练就绪状态
+}
+
+// 添加自定义类别按钮点击事件
+addCategoryBtn.onclick = () => {
+    addCustomCategory(newCategoryNameInput.value.trim());
+};
+
+// ======================= 检查训练就绪状态 =======================
+function checkTrainingReadiness() {
+    const exampleCounts = transferRecognizer.countExamples();
+    
+    let backgroundNoiseReady = (exampleCounts[BACKGROUND_NOISE_LABEL] || 0) > 0;
+    
+    let customCategoriesReady = 0;
+    // 遍历本地 labels 数组，检查每个自定义类别是否有样本
+    for (let i = 1; i < labels.length; i++) { // 从索引 1 开始，因为 0 是背景噪音
+        const customLabel = labels[i];
+        if ((exampleCounts[customLabel] || 0) > 0) {
+            customCategoriesReady++;
+        }
+    }
+
+    // 必须有背景噪音样本，并且至少一个自定义类别有样本
+    if (backgroundNoiseReady && customCategoriesReady >= 1) {
+        trainModelBtn.disabled = false;
+    } else {
+        trainModelBtn.disabled = true;
+    }
+}
+// ======================= 模型训练 =======================
+trainModelBtn.onclick = async () => {
+    const exampleCounts = transferRecognizer.countExamples(); // 确保这里获取到了最新的样本数量
+    console.log('--- DEBUG: 训练开始前，各类别样本数量:', exampleCounts); 
+
+    let totalExamples = 0;
+    let validClasses = 0;
+    const MIN_SAMPLES_PER_CLASS_FOR_TRAINING = 5; 
+    let allClassesHaveEnoughSamples = true; 
+    
+    // 统计所有类别的有效样本数，并检查每个类别是否达到`isTrained`的最低要求
+    for (const labelName of labels) { // 遍历所有标签（包括背景噪音）
+        if (exampleCounts[labelName] && exampleCounts[labelName] > 0) {
+            totalExamples += exampleCounts[labelName];
+            validClasses++;
+            if (exampleCounts[labelName] < MIN_SAMPLES_PER_CLASS_FOR_TRAINING) {
+                allClassesHaveEnoughSamples = false; 
+            }
+        }
+    }
+
+    // 更明确的样本数量检查提示
+    if (validClasses < 2) { 
+        alert(`训练需要至少 "背景噪音" (已存在) 和另一个自定义类别 (您需要添加并录制样本)。\n\n当前只有 ${validClasses} 个有效类别。`);
+        return;
+    }
+    if (!allClassesHaveEnoughSamples) {
+        alert(`请确保每个类别至少收集了 ${MIN_SAMPLES_PER_CLASS_FOR_TRAINING} 个样本。\n(当前某些类别样本不足，请检查！)\n\n建议每个类别多收集一些（例如 5-10 个）以获得更好的模型效果。`);
+        return;
+    }
+     if (totalExamples === 0) { // 额外的安全检查，理论上会被上面的validClasses捕捉
+        alert('没有收集到任何训练样本！请先录制样本。');
+        return;
+    }
+
+
+    statusDiv.innerText = '模型训练中...请稍候。';
+    trainModelBtn.disabled = true;
+    startPredictingBtn.disabled = true;
+    stopPredictingBtn.disabled = true;
+
+    const trainingConfig = {
+        epochs: 50, 
+        batchSize: 16, 
+        validationSplit: 0.1, 
+        shuffle: true, 
+        yieldEvery: 'epoch', 
+        callbacks: {
+            onEpochEnd: (epoch, logs) => {
+                statusDiv.innerText = `训练 Epoch ${epoch + 1}/${trainingConfig.epochs}, Loss: ${logs.loss ? logs.loss.toFixed(4) : 'N/A'}, Accuracy: ${logs.acc ? logs.acc.toFixed(4) : 'N/A'}`;
+            }
+        }
+    };
+
+    try {
+        await transferRecognizer.train(trainingConfig);
+        statusDiv.innerText = '模型训练完成！你可以开始识别了。';
+        predictionResultDiv.innerText = '训练完成，等待识别...';
+        startPredictingBtn.disabled = false;
+        
+        // 训练成功后，手动设置状态标志
+        isModelTrainedFlag = true; 
+        console.log('--- DEBUG: 训练成功完成，此时 transferRecognizer.isTrained 为:', transferRecognizer.isTrained); 
+    } catch (error) {
+        statusDiv.innerText = `模型训练失败: ${error.message}. 这通常是由于样本数量过少，类别不均，或录音质量问题导致。请确保每个类别至少有 ${MIN_SAMPLES_PER_CLASS_FOR_TRAINING} 个样本，并且多录制一些（例如 5-10 个）！`;
+        console.error('训练失败:', error);
+        // 训练失败时重置状态
+        isModelTrainedFlag = false; 
+    } finally {
+        trainModelBtn.disabled = false; 
+    }
+};
+
+// ======================= 实时预测 =======================
+startPredictingBtn.onclick = async () => { // 确保此函数是 async
+    console.log('--- DEBUG: 点击开始识别时， isModelTrainedFlag 为:', isModelTrainedFlag); 
+    if (isPredicting) {
+        statusDiv.innerText = '识别已经在进行中...';
+        return;
+    }
+    
+    // 使用自定义标志进行判断
+    if (!isModelTrainedFlag) { 
+        alert('模型尚未训练完成，请先训练模型！');
+        return;
+    }
+
+    isPredicting = true;
+    startPredictingBtn.disabled = true;
+    stopPredictingBtn.disabled = false;
+    trainModelBtn.disabled = true; 
+    recordBackgroundNoiseBtn.disabled = true; 
+    addCategoryBtn.disabled = true; 
+    // 禁用所有录制按钮 (确保在预测时不能添加新样本)
+    document.querySelectorAll('.category-block button').forEach(btn => btn.disabled = true);
+    
+    statusDiv.innerText = '正在开始识别... 请发出你训练过的声音。';
+    predictionResultDiv.innerText = '等待识别结果...';
+
+    // <<< 核心修正：捕获 transferRecognizer.listen() 返回的停止函数时使用 await
+    predictionStopFunction = await transferRecognizer.listen(result => { // ！！！这里加上了 await ！！！
+        if (!isPredicting) return; 
+
+        // `transferRecognizer.wordLabels()` 会返回 transferRecognizer 内部按顺序排列的所有标签名称。
+        // `result.scores` 的索引会与 `transferRecognizer.wordLabels()` 的索引对应。
+        const classLabels = transferRecognizer.wordLabels(); 
+
+        const scores = result.scores; 
+        const maxScore = Math.max(...scores);
+        const predictedIndex = scores.indexOf(maxScore);
+        
+        let predictedLabel = classLabels[predictedIndex]; // 从 transferRecognizer 的内部标签列表中获取
+        // 如果预测结果是内部的背景噪音标签，转换成用户友好的显示
+        if (predictedLabel === BACKGROUND_NOISE_LABEL) {
+            predictedLabel = '背景噪音'; 
+        }
+        
+        predictionResultDiv.innerText = `预测结果：${predictedLabel} (置信度: ${(maxScore * 100).toFixed(2)}%)`;
+
+    }, {
+        includeEmbedding: true, 
+        probabilityThreshold: 0.75, 
+        suppressionTimeMillis: 300, 
+        overlapFactor: 0.50, 
+    });
+
+    // 可以在这里添加一个调试日志，确认 predictionStopFunction 确实是一个函数
+    console.log('--- DEBUG: predictionStopFunction 赋值后:', predictionStopFunction);
+    console.log('--- DEBUG: typeof predictionStopFunction 赋值后:', typeof predictionStopFunction);
+
+};
+
+stopPredictingBtn.onclick = () => {
+    if (isPredicting) {
+        // 增加一个额外的类型检查，确保它确实是一个函数
+        if (typeof predictionStopFunction === 'function') { // 确保是函数才调用
+            predictionStopFunction(); // 调用停止识别的函数
+            predictionStopFunction = null; // 清除引用，避免内存泄漏，也防止二次调用
+        } else {
+            console.warn('--- WARN: predictionStopFunction 不是一个函数，无法停止监听。');
+        }
+        
+        isPredicting = false;
+        startPredictingBtn.disabled = false;
+        stopPredictingBtn.disabled = true;
+        trainModelBtn.disabled = false; 
+        recordBackgroundNoiseBtn.disabled = false; 
+        addCategoryBtn.disabled = false; 
+
+        // 重新启用所有录制按钮 (只有在不是正在录音状态时才启用)
+        document.querySelectorAll('.category-block button').forEach(btn => {
+            if (!isRecording) { 
+                 btn.disabled = false;
+            }
+        });
+
+        statusDiv.innerText = '已停止识别。';
+        predictionResultDiv.innerText = '停止识别。';
+    }
+};
+
+// ======================= 页面加载时执行 =======================
+window.onload = init;

音频分类/voice.html

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+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>音频分类器 (背景噪音分离版)</title>
+    <style>
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            margin: 20px;
+            background-color: #f4f7f6;
+            color: #333;
+        }
+        .container {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 20px;
+            margin-top: 20px;
+        }
+        .category-block {
+            background-color: #fff;
+            border: 1px solid #e0e0e0;
+            border-radius: 8px;
+            padding: 20px;
+            width: 280px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+            display: flex;
+            flex-direction: column;
+            justify-content: space-between;
+        }
+        .category-block h3 {
+            margin-top: 0;
+            color: #007bff;
+            border-bottom: 1px solid #eee;
+            padding-bottom: 10px;
+            margin-bottom: 15px;
+        }
+        /* 特殊样式给背景噪音 */
+        #backgroundNoiseBlock h3 {
+            color: #dc3545; /* 红色 */
+        }
+        #backgroundNoiseBlock button {
+            background-color: #dc3545; /* 红色 */
+        }
+        #backgroundNoiseBlock button:hover:not(:disabled) {
+            background-color: #c82333;
+        }
+
+        .category-block button {
+            background-color: #007bff;
+            color: white;
+            border: none;
+            padding: 10px 15px;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 1em;
+            transition: background-color 0.2s ease;
+            margin-top: 10px;
+        }
+        .category-block button:hover:not(:disabled) {
+            background-color: #0056b3;
+        }
+        .category-block button:disabled {
+            background-color: #cccccc;
+            cursor: not-allowed;
+        }
+        #controls button {
+            background-color: #28a745;
+            color: white;
+            border: none;
+            padding: 12px 20px;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 1.1em;
+            margin-right: 15px;
+            transition: background-color 0.2s ease;
+        }
+        #controls button:hover:not(:disabled) {
+            background-color: #218838;
+        }
+        #controls button:disabled {
+            background-color: #cccccc;
+            cursor: not-allowed;
+        }
+        #status {
+            margin-top: 15px;
+            font-size: 1.1em;
+            color: #616161;
+        }
+        #predictionResult {
+            margin-top: 30px;
+            font-size: 1.8em;
+            font-weight: bold;
+            color: #28a745;
+            padding: 15px;
+            border: 2px dashed #28a745;
+            background-color: #e6ffed;
+            border-radius: 8px;
+        }
+        .add-category-section {
+            background-color: #e9f5ff;
+            border: 1px solid #b3d9ff;
+            border-radius: 8px;
+            padding: 20px;
+            margin-bottom: 30px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+        }
+        .add-category-section input[type="text"] {
+            padding: 10px;
+            border: 1px solid #ccc;
+            border-radius: 4px;
+            width: 250px;
+            margin-right: 10px;
+        }
+        .add-category-section button {
+            background-color: #17a2b8;
+            color: white;
+            border: none;
+            padding: 10px 15px;
+            border-radius: 5px;
+            cursor: pointer;
+        }
+        .add-category-section button:hover {
+            background-color: #138496;
+        }
+        .sample-count {
+            font-size: 0.9em;
+            color: #6a6a6a;
+            margin-top: 5px;
+        }
+    </style>
+</head>
+<body>
+    <h1>浏览器音频分类器 (背景噪音分离增强版)</h1>
+    <p>这个工具通过分离背景噪音和目标声音的录制，来提高分类准确性。</p>
+
+    <div id="status">正在初始化模型和音频设备... 请稍候。</div>
+
+    <h2>🤫 1. 录制背景噪音</h2>
+    <div id="backgroundNoiseBlock" class="category-block">
+        <h3>背景噪音 (Background Noise)</h3>
+        <p>样本数量: <span id="backgroundNoiseSampleCount">0</span></p>
+        <button id="recordBackgroundNoiseBtn">录制样本</button>
+        <p style="font-size: 0.85em; color: #6a6a6a; margin-top: 10px;">
+            请录制您所处环境的<b>无特定声音</b>的噪音，帮助模型区分目标声音与环境杂音。建议多录制一些。
+        </p>
+    </div>
+
+    <h2>🗣️ 2. 录制您要分类的声音</h2>
+    <div class="add-category-section">
+        <h3>🎉 添加新类别</h3>
+        <input type="text" id="newCategoryName" placeholder="输入类别名称 (例如: 拍手, 响指, 警告音)">
+        <button id="addCategoryBtn">添加类别</button>
+    </div>
+
+    <div id="categoryContainer" class="container">
+        <!-- 动态添加的类别块会在这里显示 -->
+    </div>
+
+    <div id="controls" style="margin-top: 30px;">
+        <button id="trainModelBtn" disabled>🚀 3. 训练模型</button>
+        <button id="startPredictingBtn" disabled>👂 4. 开始识别</button>
+        <button id="stopPredictingBtn" disabled>⏸️ 停止识别</button>
+    </div>
+
+    <h2>🧠 识别结果</h2>
+    <div id="predictionResult">
+        等待模型训练完成并开始识别...
+    </div>
+
+    <!-- 引入 TensorFlow.js 库 -->
+    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
+    <!-- 引入 Speech Commands 模型库 -->
+    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/speech-commands@0.5.4/dist/speech-commands.min.js"></script>
+
+    <!-- 你的 JavaScript 代码 -->
+    <script src="script.js"></script>
+</body>
+</html>