package main

import (
    "encoding/binary"
    "fmt"
    "os"
    "io"
    "math"
)

// WAV文件头结构 (增强版)
type WAVHeader struct {
    RIFF            [4]byte
    FileSize        uint32
    WAVE            [4]byte
    FMT             [4]byte
    ChunkSize       uint32
    AudioFormat     uint16
    NumChannels     uint16
    SampleRate      uint32
    ByteRate        uint32
    BlockAlign      uint16
    BitsPerSample   uint16
    ExtensionSize   uint16
    ValidBits       uint16
    ChannelMask     uint32
    SubFormat       [16]byte
}

// AudioData 优化的音频数据结构
type AudioData struct {
    Header      WAVHeader
    Samples     []float32  // float32 for AI processing
    SampleRate  int
    Duration    float64
}

// ReadWAV 读取WAV文件
func ReadWAV(filename string) (*AudioData, error) {
    file, err := os.Open(filename)
    if err != nil {
        return nil, err
    }
    defer file.Close()

    header := WAVHeader{}
    err = binary.Read(file, binary.LittleEndian, &header)
    if err != nil {
        return nil, err
    }

    // AI优化：使用float32提高计算效率
    samples := []float32{}
    bytesPerSample := int(header.BitsPerSample / 8)
    buffer := make([]byte, bytesPerSample)

    for {
        chunkHeader := [4]byte{}
        _, err = io.ReadFull(file, chunkHeader[:])
        if err == io.EOF {
            break
        }

        chunkData := [4]byte{}
        binary.Read(file, binary.LittleEndian, &chunkData)
        chunkSize := uint32(chunkData[0]) | 
                    uint32(chunkData[1])<<8 | 
                    uint32(chunkData[2])<<16 | 
                    uint32(chunkData[3])<<24

        chunkName := string(chunkHeader[:])

        if chunkName == "data" {
            numSamples := int(chunkSize) / bytesPerSample
            samples = make([]float32, numSamples)

            for i := 0; i < numSamples; i++ {
                _, err = io.ReadFull(file, buffer)
                if err != nil {
                    break
                }

                var sample float32
                switch header.BitsPerSample {
                case 16:
                    sample = float32(int16(buffer[0]) | 
                              int16(buffer[1])<<8) / 32768.0
                case 24:
                    sample = float32(int32(buffer[0]) | 
                              int32(buffer[1])<<8 | 
                              int32(buffer[2])<<16) / 8388608.0
                case 32:
                    if header.AudioFormat == 3 {  // IEEE Float
                        sample = float32(math.Float32frombits(
                            uint32(buffer[0]) | 
                            uint32(buffer[1])<<8 | 
                            uint32(buffer[2])<<16 | 
                            uint32(buffer[3])<<24))
                    }
                }
                samples[i] = sample
            }
        } else {
            file.Seek(int64(chunkSize), 1)
        }
    }

    duration := float64(len(samples)) / float64(header.SampleRate)

    return &AudioData{
        Header:     header,
        Samples:    samples,
        SampleRate: int(header.SampleRate),
        Duration:   duration,
    }, nil
}

// Normalize 归一化音频数据（AI预处理）
func (a *AudioData) Normalize() {
    maxAmp := float32(0)
    for _, sample := range a.Samples {
        if abs := float32(math.Abs(float64(sample))); abs > maxAmp {
            maxAmp = abs
        }
    }

    if maxAmp > 0 {
        gain := 0.95 / maxAmp
        for i := range a.Samples {
            a.Samples[i] *= gain
        }
    }
}

// ToMFCC 转换为MFCC特征（用于AI处理）
func (a *AudioData) ToMFCC(numCoeffs int) ([]float64, error) {
    // 简化的MFCC计算
    nFFT := 512
    hopLength := 160
    
    ">// 计算短时傅里叶变换
    stft := a.computeSTFT(nFFT, hopLength)
    
    // 应用梅尔滤波器组
    melFilter := a.createMelFilterBank(nFFT, numCoeffs)
    
    // 计算MFCC
    mfcc := make([]float64, numCoeffs)
    for i := 0; i < numCoeffs; i++ {
        var sum float64
        for j := 0; j < len(stft); j++ {
            sum += melFilter[i][j] * stft[j]
        }
        mfcc[i] = math.Log(sum + 1e-10)
    }
    
    return mfcc, nil
}

// computeSTFT 计算短时傅里叶变换
func (a *AudioData) computeSTFT(nFFT, hopLength int) []float64 {
    // 简化实现
    numFrames := (len(a.Samples) - nFFT) / hopLength
    spectrum := make([]float64, nFFT/2+1)
    
    // 返回功率谱
    return spectrum
}

// createMelFilterBank 创建梅尔滤波器组
func (a *AudioData) createMelFilterBank(nFFT, numFilters int) [][]float64 {
    filterBank := make([][]float64, numFilters)
    for i := range filterBank {
        filterBank[i] = make([]float64, nFFT/2+1)
    }
    return filterBank
}

// 示例使用
func main() {
    wav, err := ReadWAV("audio.wav")
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }

    fmt.Printf("Sample Rate: %d Hz\n", wav.SampleRate)
    fmt.Printf("Duration: %.2f seconds\n", wav.Duration)
    fmt.Printf("Samples: %d\n", len(wav.Samples))
    
    // AI预处理
    wav.Normalize()
    
    // 提取MFCC特征
    mfcc, _ := wav.ToMFCC(13)
    fmt.Printf("MFCC coefficients: %v\n", mfcc)

package main

import (
    "fmt"
    "os"
    
    onnxruntime "github.com/yourbasic/onnxruntime"
)

// ONNXInference ONNX模型推理引擎 
type ONNXInference struct {
    session    *onnxruntime.Session
    inputName  string
    outputName string
}

func NewONNXInference(modelPath string, providers []string) (*ONNXInference, error) {
    // 加载ONNX模型
    session, err := onnxruntime.NewSession(modelPath, &onnxruntime.SessionOptions{
        Providers: providers,  // ["CUDA", "CPU"] or ["CoreML", "NPU"]
    })
    if err != nil {
        return nil, fmt.Errorf("failed to load model: %w", err)
    }

    // 获取输入输出名称
    inputName := session.GetInputNames()[0]
    outputName := session.GetOutputNames()[0]

    return &ONNXInference{
        session:    session,
        inputName:  inputName,
        outputName: outputName,
    }, nil
}

// Inference 执行推理
func (o *ONNXInference) Inference(input []float32) ([]float32, error) {
    // 准备输入
    inputTensor := onnxruntime.NewTensor(input, []int64{1, 16000})

    // 运行推理
    outputs, err := o.session.Run([]onnxruntime.Tensor{inputTensor})
    if err != nil {
        return nil, err
    }

    // 获取输出
    output := outputs[0].(*onnxruntime.Tensor).Data().([]float32)

    return output, nil
}

// AudioClassifier 音频分类器 
type AudioClassifier struct {
    inference *ONNXInference
    classes   []string
}

func NewAudioClassifier(modelPath string, classesPath string) (*AudioClassifier, error) {
    inference, err := NewONNXInference(modelPath, []string{
        "CUDAExecutionProvider",
        "CPUExecutionProvider",
    })
    if err != nil {
        return nil, err
    }

    // 加载类别标签
    classes, err := os.ReadFile(classesPath)
    if err != nil {
        return nil, err
    }

    return &AudioClassifier{
        inference: inference,
        classes:   strings.Fields(string(classes)),
    }, nil
}

// Predict 预测音频类别
func (c *AudioClassifier) Predict(audioFeatures []float32) (string, float32) {
    output, err := c.inference.Inference(audioFeatures)
    if err != nil {
        return "error", 0
    }

    // 找到最大概率的类别
    maxIdx := 0
    maxProb := float32(0)
    for i, prob := range output {
        if prob > maxProb {
            maxProb = prob
            maxIdx = i
        }
    }

    return c.classes[maxIdx], maxProb
}

// 使用示例
func main() {
    // 创建分类器
    classifier, err := NewAudioClassifier(
        "audio_classifier.onnx",
        "classes.txt",
    )
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }

    // 准备特征（示例：MFCC特征）
    features := make([]float32, 16000)
    // 填充特征...

    // 预测
    label, confidence := classifier.Predict(features)
    fmt.Printf("Predicted: %s (confidence: %.2f%%)\n", label, confidence*100)

package main

import (
    "context"
    "fmt"
    "sync"
    "time"
    
    "golang.org/x/sync/errgroup"
)

// AudioProcessor 实时音频处理器 
type AudioProcessor struct {
    sampleRate     int
    chunkSize      int
    numWorkers     int
    inferenceEngine *ONNXInference
    audioBuffer    *AudioBuffer
    wg             sync.WaitGroup
    ctx            context.Context
    cancel         context.CancelFunc
}

func NewAudioProcessor(sampleRate, chunkSize, numWorkers int) *AudioProcessor {
    ctx, cancel := context.WithCancel(context.Background())
    
    return &AudioProcessor{
        sampleRate:     sampleRate,
        chunkSize:      chunkSize,
        numWorkers:     numWorkers,
        audioBuffer:    NewAudioBuffer(chunkSize * 10),  // 10块缓存
        wg:             sync.WaitGroup{},
        ctx:            ctx,
        cancel:         cancel,
    }
}

// SetInferenceEngine 设置AI推理引擎
func (p *AudioProcessor) SetInferenceEngine(engine *ONNXInference) {
    p.inferenceEngine = engine
}

// AudioBuffer 线程安全的音频缓冲区
type AudioBuffer struct {
    data     []float32
    writePos int
    readPos  int
    mu       sync.RWMutex
    cond     *sync.Cond
}

func NewAudioBuffer(size int) *AudioBuffer {
    buf := &AudioBuffer{
        data: make([]float32, size),
    }
    buf.cond = sync.NewCond(buf.mu.RLocker())
    return buf
}

// Write 写入音频数据
func (b *AudioBuffer) Write(data []float32) {
    b.mu.Lock()
    defer b.mu.Unlock()

    for _, sample := range data {
        b.data[b.writePos] = sample
        b.writePos = (b.writePos + 1) % len(b.data)
        if b.writePos == b.readPos {
            // 缓冲区满，跳过最旧的数据
            b.readPos = (b.readPos + 1) % len(b.data)
        }
    }
    b.cond.Broadcast()
}

// Read 读取音频数据
func (b *AudioBuffer) Read(size int) []float32 {
    b.mu.RLock()
    defer b.mu.RUnlock()

    for b.available() < size {
        b.cond.Wait()
    }

    result := make([]float32, size)
    for i := 0; i < size; i++ {
        result[i] = b.data[b.readPos]
        b.readPos = (b.readPos + 1) % len(b.data)
    }
    return result
}

// available 返回可用样本数
func (b *AudioBuffer) available() int {
    if b.writePos >= b.readPos {
        return b.writePos - b.readPos
    }
    return len(b.data) - b.readPos + b.writePos
}

// ProcessorWorker 处理工作协程
func (p *AudioProcessor) ProcessorWorker(id int) {
    defer p.wg.Done()

    for {
        select {
        case <-p.ctx.Done():
            return
        default:
            // 读取音频块
            chunk := p.audioBuffer.Read(p.chunkSize)

            // 应用AI处理
            if p.inferenceEngine != nil {
                result, err := p.inferenceEngine.Inference(chunk)
                if err != nil {
                    // 处理错误
                    continue
                }
                // 输出处理结果
                _ = result
            }
        }
    }
}

// Start 启动处理器
func (p *AudioProcessor) Start() {
    for i := 0; i < p.numWorkers; i++ {
        p.wg.Add(1)
        go p.ProcessorWorker(i)
    }
}

// Stop 停止处理器
func (p *AudioProcessor) Stop() {
    p.cancel()
    p.wg.Wait()
}

// Benchmark 性能测试
func (p *AudioProcessor) Benchmark(duration time.Duration) {
    p.Start()
    
    start := time.Now()
    count := 0
    
    go func() {
        for time.Since(start) < duration {
            // 生成测试数据
            testData := make([]float32, p.chunkSize)
            for i := range testData {
                testData[i] = float32(i%256) / 256.0
            }
            p.audioBuffer.Write(testData)
            count++
            time.Sleep(time.Millisecond)
        }
    }()
    
    select {
    case <-time.After(duration + time.Second):
    }
    
    p.Stop()
    
    throughput := float64(count) / duration.Seconds()
    fmt.Printf("Processed %.2f chunks/second\n", throughput)
}

// 使用示例
func main() {
    // 创建处理器 (16kHz采样率, 160样本/块, 4协程)
    processor := NewAudioProcessor(16000, 160, 4)

    // 加载AI模型
    inference, err := NewONNXInference("denoiser.onnx", []string{
        "CUDAExecutionProvider",
        "CPUExecutionProvider",
    })
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }
    processor.SetInferenceEngine(inference)

    // 性能测试
    processor.Benchmark(10 * time.Second)

package main

import (
    "fmt"
    "math"
)

// AIEffectsProcessor AI音频效果处理器 
type AIEffectsProcessor struct {
    sampleRate    int
    effects       []AudioEffect
}

// AudioEffect 音频效果接口
type AudioEffect interface {
    Process(samples []float32) []float32
    Reset()
}

// AIReverb AI增强混响效果
type AIReverb struct {
    sampleRate    int
    wetMix        float32
    dryMix        float32
    decay         float32
    combFilters   []CombFilter
    allpassFilters []AllpassFilter
}

// CombFilter 梳状滤波器
type CombFilter struct {
    delayLine []float32
    index     int
    gain      float32
}

func NewCombFilter(delaySamples int, decay float32) *CombFilter {
    return &CombFilter{
        delayLine: make([]float32, delaySamples),
        gain:      decay,
    }
}

func (c *CombFilter) Process(input float32) float32 {
    output := c.delayLine[c.index]
    c.delayLine[c.index] = input + output*c.gain
    c.index = (c.index + 1) % len(c.delayLine)
    return output
}

// AllpassFilter 全通滤波器
type AllpassFilter struct {
    delayLine []float32
    index     int
    gain      float32
}

func NewAllpassFilter(delaySamples int, gain float32) *AllpassFilter {
    return &AllpassFilter{
        delayLine: make([]float32, delaySamples),
        gain:      gain,
    }
}

func (a *AllpassFilter) Process(input float32) float32 {
    delayed := a.delayLine[a.index]
    output := delayed + a.gain*input
    a.delayLine[a.index] = input + delayed*a.gain
    a.index = (a.index + 1) % len(a.delayLine)
    return output
}

func NewAIReverb(sampleRate int) *AIReverb {
    // 优化的梳状滤波器参数
    combDelays := []int{1116, 1188, 1277, 1356, 1492, 1617}
    combGains := []float32{0.7, 0.65, 0.6, 0.55, 0.5, 0.45}
    
    combFilters := make([]CombFilter, len(combDelays))
    for i := 0; i < len(combDelays); i++ {
        combFilters[i] = *NewCombFilter(combDelays[i], combGains[i])
    }
    
    // 全通滤波器
    allpassFilters := make([]AllpassFilter, 2)
    allpassFilters[0] = *NewAllpassFilter(556, 0.7)
    allpassFilters[1] = *NewAllpassFilter(441, 0.5)

    return &AIReverb{
        sampleRate:     sampleRate,
        wetMix:        0.3,
        dryMix:        0.7,
        decay:         0.98,
        combFilters:   combFilters,
        allpassFilters: allpassFilters,
    }
}

func (r *AIReverb) Process(samples []float32) []float32 {
    output := make([]float32, len(samples))

    for i := 0; i < len(samples); i++ {
        dry := samples[i] * r.dryMix

        // 梳状滤波器
        var wet float32
        for j := 0; j < len(r.combFilters); j++ {
            wet += r.combFilters[j].Process(samples[i])
        }
        wet /= float32(len(r.combFilters))

        // 全通滤波器
        for j := 0; j < len(r.allpassFilters); j++ {
            wet = r.allpassFilters[j].Process(wet)
        }

        wet *= r.wetMix * r.decay
        output[i] = dry + wet
    }

    return output
}

func (r *AIReverb) Reset() {
    for i := 0; i < len(r.combFilters); i++ {
        r.combFilters[i].delayLine = make([]float32, len(r.combFilters[i].delayLine))
        r.combFilters[i].index = 0
    }
    for i := 0; i < len(r.allpassFilters); i++ {
        r.allpassFilters[i].delayLine = make([]float32, len(r.allpassFilters[i].delayLine))
        r.allpassFilters[i].index = 0
    }
}

// AIDelay AI智能延迟效果
type AIDelay struct {
    sampleRate    int
    delayTime     float32
    feedback      float32
    wetMix        float32
    dryMix        float32
    delayLine     []float32
    index         int
}

func NewAIDelay(sampleRate int, delayTime, feedback float32) *AIDelay {
    delaySamples := int(delayTime * float32(sampleRate))
    
    return &AIDelay{
        sampleRate:  sampleRate,
        delayTime:   delayTime,
        feedback:    feedback,
        wetMix:      0.4,
        dryMix:      0.6,
        delayLine:   make([]float32, delaySamples+1),
    }
}

func (d *AIDelay) Process(samples []float32) []float32 {
    output := make([]float32, len(samples))

    for i := 0; i < len(samples); i++ {
        delayed := d.delayLine[d.index]
        output[i] = samples[i]*d.dryMix + delayed*d.wetMix
        d.delayLine[d.index] = samples[i] + delayed*d.feedback
        d.index = (d.index + 1) % len(d.delayLine)
    }

    return output
}

// 使用示例
func main() {
    sampleRate := 44100

    // 创建AI混响
    reverb := NewAIReverb(sampleRate)

    // 创建AI延迟
    delay := NewAIDelay(sampleRate, 0.3, 0.5)

    // 生成测试信号
    testSignal := make([]float32, sampleRate)  // 1秒
    for i := 0; i < len(testSignal); i++ {
        t := float32(i) / float32(sampleRate)
        testSignal[i] = 0.5*float32(math.Sin(2*math.Pi*440*t)) +
                        0.3*float32(math.Sin(2*math.Pi*880*t))
    }

    // 应用效果
    reverbOutput := reverb.Process(testSignal)
    delayOutput := delay.Process(testSignal)

    fmt.Printf("Original: %d samples\n", len(testSignal))
    fmt.Printf("Reverb: %d samples\n", len(reverbOutput))
    fmt.Printf("Delay: %d samples\n", len(delayOutput))
}