diff options
Diffstat (limited to 'source/use_case/kws/src/UseCaseHandler.cc')
| -rw-r--r-- | source/use_case/kws/src/UseCaseHandler.cc | 46 |
1 files changed, 24 insertions, 22 deletions
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc index e73a2c3..61c6eb6 100644 --- a/source/use_case/kws/src/UseCaseHandler.cc +++ b/source/use_case/kws/src/UseCaseHandler.cc @@ -34,13 +34,12 @@ using KwsClassifier = arm::app::Classifier; namespace arm { namespace app { - /** * @brief Presents KWS inference results. * @param[in] results Vector of KWS classification results to be displayed. * @return true if successful, false otherwise. **/ - static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results); + static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results); /* KWS inference handler. */ bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) @@ -50,6 +49,7 @@ namespace app { const auto mfccFrameLength = ctx.Get<int>("frameLength"); const auto mfccFrameStride = ctx.Get<int>("frameStride"); const auto scoreThreshold = ctx.Get<float>("scoreThreshold"); + /* If the request has a valid size, set the audio index. */ if (clipIndex < NUMBER_OF_FILES) { if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) { @@ -61,16 +61,17 @@ namespace app { constexpr uint32_t dataPsnTxtInfStartX = 20; constexpr uint32_t dataPsnTxtInfStartY = 40; constexpr int minTensorDims = static_cast<int>( - (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)? - arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx); - + (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)? + MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx); if (!model.IsInited()) { printf_err("Model is not initialised! Terminating processing.\n"); return false; } + /* Get Input and Output tensors for pre/post processing. */ TfLiteTensor* inputTensor = model.GetInputTensor(0); + TfLiteTensor* outputTensor = model.GetOutputTensor(0); if (!inputTensor->dims) { printf_err("Invalid input tensor dims\n"); return false; @@ -81,22 +82,23 @@ namespace app { /* Get input shape for feature extraction. */ TfLiteIntArray* inputShape = model.GetInputShape(0); - const uint32_t numMfccFeatures = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx]; + const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx]; + const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx]; /* We expect to be sampling 1 second worth of data at a time. * NOTE: This is only used for time stamp calculation. */ const float secondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq; /* Set up pre and post-processing. */ - KWSPreProcess preprocess = KWSPreProcess(&model, numMfccFeatures, mfccFrameLength, mfccFrameStride); + KwsPreProcess preProcess = KwsPreProcess(inputTensor, numMfccFeatures, numMfccFrames, + mfccFrameLength, mfccFrameStride); std::vector<ClassificationResult> singleInfResult; - KWSPostProcess postprocess = KWSPostProcess(ctx.Get<KwsClassifier &>("classifier"), &model, + KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"), ctx.Get<std::vector<std::string>&>("labels"), singleInfResult); - UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model); - + /* Loop to process audio clips. */ do { hal_lcd_clear(COLOR_BLACK); @@ -106,7 +108,7 @@ namespace app { auto audioDataSlider = audio::SlidingWindow<const int16_t>( get_audio_array(currentIndex), get_audio_array_size(currentIndex), - preprocess.m_audioDataWindowSize, preprocess.m_audioDataStride); + preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride); /* Declare a container to hold results from across the whole audio clip. */ std::vector<kws::KwsResult> finalResults; @@ -123,34 +125,34 @@ namespace app { const int16_t* inferenceWindow = audioDataSlider.Next(); /* The first window does not have cache ready. */ - preprocess.m_audioWindowIndex = audioDataSlider.Index(); + preProcess.m_audioWindowIndex = audioDataSlider.Index(); info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, audioDataSlider.TotalStrides() + 1); /* Run the pre-processing, inference and post-processing. */ - if (!runner.PreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) { + if (!preProcess.DoPreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) { + printf_err("Pre-processing failed."); return false; } - profiler.StartProfiling("Inference"); - if (!runner.RunInference()) { + if (!RunInference(model, profiler)) { + printf_err("Inference failed."); return false; } - profiler.StopProfiling(); - if (!runner.PostProcess()) { + if (!postProcess.DoPostProcess()) { + printf_err("Post-processing failed."); return false; } /* Add results from this window to our final results vector. */ finalResults.emplace_back(kws::KwsResult(singleInfResult, - audioDataSlider.Index() * secondsPerSample * preprocess.m_audioDataStride, + audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride, audioDataSlider.Index(), scoreThreshold)); #if VERIFY_TEST_OUTPUT - TfLiteTensor* outputTensor = model.GetOutputTensor(0); - arm::app::DumpTensor(outputTensor); + DumpTensor(outputTensor); #endif /* VERIFY_TEST_OUTPUT */ } /* while (audioDataSlider.HasNext()) */ @@ -174,7 +176,7 @@ namespace app { return true; } - static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results) + static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results) { constexpr uint32_t dataPsnTxtStartX1 = 20; constexpr uint32_t dataPsnTxtStartY1 = 30; @@ -187,7 +189,7 @@ namespace app { /* Display each result */ uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr; - for (const auto & result : results) { + for (const auto& result : results) { std::string topKeyword{"<none>"}; float score = 0.f; |