feat: 扩展音频格式支持与推理管线优化

- 新增 MP3/FLAC 格式解码 (dr_mp3/dr_flac)
- 修复 Mel 频谱图使用 magnitude² 替代 magnitude 的问题
- 推理管线增加音频重采样 (非 16kHz 自动转换)
- 更新 README 项目状态

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

README.md

@@ -125,8 +125,9 @@ ctest
 - [x] 音频采集/解码框架 (PortAudio/dr_libs)
 - [x] 三个 GUI 页面 (实时识别 / 文件转写 / 配置)
 - [x] 日志系统 (控制台 + 文件输出)
+- [x] 批量文件转写 (支持 WAV/MP3/FLAC)
+- [x] 音频重采样 (非 16kHz 音频自动重采样)
 - [ ] 完整 Whisper 推理 (自回归解码 + 流式识别)
-- [ ] 批量文件转写
 - [ ] 跨平台打包
 
 ## License

src/audio/audio_decoder.cpp

@@ -1,6 +1,8 @@
 #include "audio_decoder.h"
 #include "utils/logger.h"
 
+#include <cstdint>
+
 #ifdef HAVE_DR_LIBS
 #define DR_WAV_IMPLEMENTATION
 #define DR_MP3_IMPLEMENTATION
@@ -23,7 +25,7 @@ AudioDecoder::AudioDecoder(QObject* parent)
 AudioDecoder::~AudioDecoder() = default;
 
 QStringList AudioDecoder::supportedFormats() {
-    return {"wav", "mp3", "flac", "ogg", "aac"};
+    return {"wav", "mp3", "flac"};
 }
 
 bool AudioDecoder::decode(const QString& filePath) {
@@ -34,13 +36,37 @@ bool AudioDecoder::decode(const QString& filePath) {
     sampleRate_ = 0;
     channels_ = 0;
 
-    if (ext != "wav") {
+#ifdef HAVE_DR_LIBS
+    bool success = false;
+
+    if (ext == "wav") {
+        success = decodeWav(filePath);
+    } else if (ext == "mp3") {
+        success = decodeMp3(filePath);
+    } else if (ext == "flac") {
+        success = decodeFlac(filePath);
+    } else {
         LOG_ERROR(kTag, QString("暂不支持格式: %1").arg(ext));
         emit error(QString("暂不支持格式: %1").arg(ext));
         return false;
     }
 
+    if (success) {
+        emit progress(1.0);
+        emit decoded(filePath);
+        LOG_INFO(kTag, QString("文件解码完成: %1 (%2 样本, %3Hz, %4声道)")
+            .arg(filePath).arg(samples_.size()).arg(sampleRate_).arg(channels_));
+    }
+    return success;
+#else
+    LOG_ERROR(kTag, "dr_libs 未编译启用");
+    emit error("音频解码库未启用");
+    return false;
+#endif
+}
+
 #ifdef HAVE_DR_LIBS
+bool AudioDecoder::decodeWav(const QString& filePath) {
     drwav wav;
     if (!drwav_init_file(&wav, filePath.toUtf8().constData(), nullptr)) {
         LOG_ERROR(kTag, QString("无法打开 WAV 文件: %1").arg(filePath));
@@ -55,33 +81,80 @@ bool AudioDecoder::decode(const QString& filePath) {
     drwav_read_pcm_frames_s16(&wav, wav.totalPCMFrameCount, pcm16.data());
     drwav_uninit(&wav);
 
-    // 多声道混合为单声道
+    convertToMono(pcm16, wav.totalPCMFrameCount);
+    return true;
+}
+
+bool AudioDecoder::decodeMp3(const QString& filePath) {
+    drmp3 mp3;
+    if (!drmp3_init_file(&mp3, filePath.toUtf8().constData(), nullptr)) {
+        LOG_ERROR(kTag, QString("无法打开 MP3 文件: %1").arg(filePath));
+        emit error("无法打开音频文件");
+        return false;
+    }
+
+    channels_ = mp3.channels;
+    sampleRate_ = mp3.sampleRate;
+
+    drmp3_uint64 totalFrames = drmp3_get_pcm_frame_count(&mp3);
+    std::vector<float> rawPcm(totalFrames * channels_);
+    drmp3_read_pcm_frames_f32(&mp3, totalFrames, rawPcm.data());
+    drmp3_uninit(&mp3);
+
+    // MP3 解码器直接输出归一化浮点数据
     if (channels_ == 1) {
-        samples_ = std::vector<float>(pcm16.begin(), pcm16.end());
+        samples_ = std::move(rawPcm);
-        // 归一化
-        for (auto& s : samples_) s /= 32768.0f;
     } else {
-        samples_.resize(wav.totalPCMFrameCount);
+        samples_.resize(totalFrames);
-        for (size_t i = 0; i < wav.totalPCMFrameCount; ++i) {
+        for (drmp3_uint64 i = 0; i < totalFrames; ++i) {
             float sum = 0.0f;
             for (int ch = 0; ch < channels_; ++ch) {
-                sum += pcm16[static_cast<size_t>(i) * static_cast<size_t>(channels_) + static_cast<size_t>(ch)];
+                sum += rawPcm[i * channels_ + ch];
+            }
+            samples_[i] = sum / channels_;
+        }
+    }
+    return true;
+}
+
+bool AudioDecoder::decodeFlac(const QString& filePath) {
+    drflac* flac = drflac_open_file(filePath.toUtf8().constData(), nullptr);
+    if (!flac) {
+        LOG_ERROR(kTag, QString("无法打开 FLAC 文件: %1").arg(filePath));
+        emit error("无法打开音频文件");
+        return false;
+    }
+
+    channels_ = flac->channels;
+    sampleRate_ = flac->sampleRate;
+
+    drflac_uint64 totalFrames = flac->totalPCMFrameCount;
+    std::vector<short> pcm16(totalFrames * channels_);
+    drflac_read_pcm_frames_s16(flac, totalFrames, pcm16.data());
+    drflac_close(flac);
+
+    convertToMono(pcm16, totalFrames);
+    return true;
+}
+
+void AudioDecoder::convertToMono(const std::vector<short>& pcm16, uint64_t frameCount) {
+    if (channels_ == 1) {
+        samples_.resize(frameCount);
+        for (uint64_t i = 0; i < frameCount; ++i) {
+            samples_[i] = pcm16[i] / 32768.0f;
+        }
+    } else {
+        samples_.resize(frameCount);
+        for (uint64_t i = 0; i < frameCount; ++i) {
+            float sum = 0.0f;
+            for (int ch = 0; ch < channels_; ++ch) {
+                sum += pcm16[i * channels_ + ch];
             }
             samples_[i] = sum / (channels_ * 32768.0f);
         }
     }
-
-    emit progress(1.0);
-    emit decoded(filePath);
-    LOG_INFO(kTag, QString("文件解码完成: %1 (%2 样本, %3Hz)")
-        .arg(filePath).arg(samples_.size()).arg(sampleRate_));
-    return true;
-#else
-    LOG_ERROR(kTag, "dr_libs 未编译启用");
-    emit error("音频解码库未启用");
-    return false;
-#endif
 }
+#endif
 
 double AudioDecoder::duration() const {
     if (sampleRate_ == 0) return 0.0;

src/audio/audio_decoder.h

@@ -5,6 +5,7 @@
 #include <QStringList>
 #include <vector>
 #include <memory>
+#include <cstdint>
 
 namespace impress {
 
@@ -20,7 +21,7 @@ public:
     explicit AudioDecoder(QObject* parent = nullptr);
     ~AudioDecoder() override;
 
-    /** @brief 支持的格式 */
+    /** @brief 支持的格式 (WAV, MP3, FLAC) */
     static QStringList supportedFormats();
 
     /** @brief 解码音频文件 */
@@ -49,6 +50,13 @@ signals:
     void error(const QString& message);
 
 private:
+#ifdef HAVE_DR_LIBS
+    bool decodeWav(const QString& filePath);
+    bool decodeMp3(const QString& filePath);
+    bool decodeFlac(const QString& filePath);
+    void convertToMono(const std::vector<short>& pcm16, uint64_t frameCount);
+#endif
+
     std::vector<float> samples_;
     int sampleRate_ = 0;
     int channels_ = 0;

src/core/mel_spectrogram.cpp

@@ -161,9 +161,9 @@ std::vector<float> MelSpectrogram::stft(const std::vector<float>& samples, int f
     // 执行 FFT
     fft(fftInput);
 
-    // 计算幅度谱
+    // 计算幅度谱 (Whisper 使用 magnitude 而非 magnitude²)
     for (int k = 0; k < nFreq; k++) {
-        magnitude[k] = fftInput[k].magnitudeSq();
+        magnitude[k] = std::sqrt(fftInput[k].magnitudeSq());
     }
 
     return magnitude;

src/core/stt_engine.cpp

@@ -1,6 +1,7 @@
 #include "stt_engine.h"
 #include "mel_spectrogram.h"
 #include "whisper_tokenizer.h"
+#include "audio_processor.h"
 #include "utils/logger.h"
 #include "utils/timer.h"
 
@@ -237,7 +238,8 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
     RecognitionResult result;
 
     QString lang = language.isEmpty() ? "zh" : language;
-    LOG_DEBUG(kTag, QString("推理语言: %1").arg(lang));
+    LOG_DEBUG(kTag, QString("推理语言: %1 (采样率: %2Hz, 样本数: %3)")
+        .arg(lang).arg(sampleRate).arg(samples.size()));
 
 #ifdef HAVE_ONNXRUNTIME
     if (!loaded_) {
@@ -247,15 +249,29 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
     }
 
     try {
-        // 1. 计算 Mel 频谱图
+        // 1. 重采样到 Whisper 所需的 16kHz
-        Timer melTimer;
+        Timer preprocessTimer;
-        MelSpectrogram melExtractor(kMelBins, 400, 160, sampleRate);
+        std::vector<float> processedSamples = samples;
-        std::vector<float> melSpec = melExtractor.compute(samples);
+        int currentSampleRate = sampleRate;
-        int nFrames = melExtractor.nFrames(static_cast<int>(samples.size()));
-        if (nFrames <= 0) nFrames = 1;
-        LOG_DEBUG(kTag, QString("Mel 计算: %1 ms (%2 帧)").arg(melTimer.elapsedMs(), 0, 'f', 1).arg(nFrames));
 
-        // 2. 运行 ONNX 推理
+        if (sampleRate != 16000) {
+            AudioProcessor processor(16000);
+            processedSamples = processor.resample(samples, sampleRate);
+            currentSampleRate = 16000;
+            LOG_DEBUG(kTag, QString("重采样: %1Hz -> %2Hz (%3 -> %4 样本)")
+                .arg(sampleRate).arg(currentSampleRate)
+                .arg(samples.size()).arg(processedSamples.size()));
+        }
+
+        // 2. Mel 频谱图提取
+        MelSpectrogram melExtractor(kMelBins, 400, 160, currentSampleRate);
+        std::vector<float> melSpec = melExtractor.compute(processedSamples);
+        int nFrames = melExtractor.nFrames(static_cast<int>(processedSamples.size()));
+        if (nFrames <= 0) nFrames = 1;
+        LOG_DEBUG(kTag, QString("Mel 计算: %1 ms (%2 帧)")
+            .arg(preprocessTimer.elapsedMs(), 0, 'f', 1).arg(nFrames));
+
+        // 3. 运行 ONNX 推理
         Timer inferTimer;
         QMutexLocker locker(&impl_->mutex);
 
@@ -277,7 +293,7 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
 
         LOG_DEBUG(kTag, QString("ONNX 推理: %1 ms").arg(inferTimer.elapsedMs(), 0, 'f', 1));
 
-        // 3. 解析输出
+        // 4. 解析输出
         auto& outputTensor = outputTensors[0];
         auto shape = outputTensor.GetTensorTypeAndShapeInfo().GetShape();
         const float* outputData = outputTensor.GetTensorMutableData<float>();
@@ -292,11 +308,12 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
 
         if (shape.size() == 2 && shape[1] == vocabSize) {
             // [1, vocab_size] - 直接输出
-            int bestToken = argmax(outputData, 0, std::min(vocabSize, 50256));
+            int searchEnd = std::min(vocabSize, 50256);
+            int bestToken = argmax(outputData, 0, searchEnd);
             if (!WhisperTokenizer::isSpecialToken(bestToken)) {
                 tokens.push_back(bestToken);
             }
-            auto probs = softmax(outputData, 0, std::min(vocabSize, 50256));
+            auto probs = softmax(outputData, 0, searchEnd);
             float maxProb = probs[0];
             for (size_t i = 1; i < probs.size(); i++) {
                 if (probs[i] > maxProb) maxProb = probs[i];
@@ -310,7 +327,8 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
 
             for (int t = 0; t < seqLen && static_cast<int>(tokens.size()) < kMaxTokens; t++) {
                 int offset = t * vocabSize;
-                int bestToken = argmax(outputData, offset, offset + vocabSize);
+                int searchEnd = std::min(offset + vocabSize, offset + 50256);
+                int bestToken = argmax(outputData, offset, searchEnd);
                 if (WhisperTokenizer::isSpecialToken(bestToken)) break;
                 if (!tokens.empty() && tokens.back() == bestToken) continue;
                 tokens.push_back(bestToken);
@@ -320,8 +338,8 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
                 float avgConf = 0.0f;
                 for (int t = 0; t < seqLen && t < static_cast<int>(tokens.size()); t++) {
                     int offset = t * vocabSize;
-                    int bestToken = argmax(outputData, offset, offset + vocabSize);
                     auto probs = softmax(outputData, offset, offset + vocabSize);
+                    int bestToken = argmax(outputData, offset, offset + vocabSize);
                     avgConf += probs[bestToken - offset];
                 }
                 result.confidence = avgConf / tokens.size();
@@ -332,7 +350,7 @@ RecognitionResult STTEngine::infer(const std::vector<float>& samples,
             return result;
         }
 
-        // 4. 解码 token 为文本
+        // 5. 解码 token 为文本
         if (tokens.empty()) {
             result.text = "";
         } else if (impl_->tokenizer.isLoaded()) {