MLECO-3174: Minor refactoring to implemented use case APIS
Looks large but it is mainly just many small adjustments
Removed the inference runner code as it wasn't used
Fixes to doc strings
Consistent naming e.g. Asr/Kws instead of ASR/KWS
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I43b620b5c51d7910a29a63b509ac4d8a82c3a8fc
diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp
index 67a200e..a07a721 100644
--- a/source/use_case/asr/include/AsrClassifier.hpp
+++ b/source/use_case/asr/include/AsrClassifier.hpp
@@ -35,10 +35,10 @@
* @param[in] use_softmax Whether softmax scaling should be applied to model output.
* @return true if successful, false otherwise.
**/
- bool GetClassificationResults(
- TfLiteTensor* outputTensor,
- std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax = false) override;
+ bool GetClassificationResults(TfLiteTensor* outputTensor,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector<std::string>& labels,
+ uint32_t topNCount, bool use_softmax = false) override;
private:
/**
@@ -54,7 +54,7 @@
template<typename T>
bool GetTopResults(TfLiteTensor* tensor,
std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, double scale, double zeroPoint);
+ const std::vector<std::string>& labels, double scale, double zeroPoint);
};
} /* namespace app */
diff --git a/source/use_case/asr/include/Wav2LetterModel.hpp b/source/use_case/asr/include/Wav2LetterModel.hpp
index 895df2b..0078e44 100644
--- a/source/use_case/asr/include/Wav2LetterModel.hpp
+++ b/source/use_case/asr/include/Wav2LetterModel.hpp
@@ -36,6 +36,7 @@
static constexpr uint32_t ms_outputRowsIdx = 2;
static constexpr uint32_t ms_outputColsIdx = 3;
+ /* Model specific constants. */
static constexpr uint32_t ms_blankTokenIdx = 28;
static constexpr uint32_t ms_numMfccFeatures = 13;
diff --git a/source/use_case/asr/include/Wav2LetterPostprocess.hpp b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
index 45defa5..446014d 100644
--- a/source/use_case/asr/include/Wav2LetterPostprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
@@ -30,23 +30,24 @@
* @brief Helper class to manage tensor post-processing for "wav2letter"
* output.
*/
- class ASRPostProcess : public BasePostProcess {
+ class AsrPostProcess : public BasePostProcess {
public:
bool m_lastIteration = false; /* Flag to set if processing the last set of data for a clip. */
/**
* @brief Constructor
- * @param[in] outputTensor Pointer to the output Tensor.
+ * @param[in] outputTensor Pointer to the TFLite Micro output Tensor.
+ * @param[in] classifier Object used to get top N results from classification.
* @param[in] labels Vector of string labels to identify each output of the model.
- * @param[in/out] result Vector of classification results to store decoded outputs.
+ * @param[in/out] result Vector of classification results to store decoded outputs.
* @param[in] outputContextLen Left/right context length for output tensor.
* @param[in] blankTokenIdx Index in the labels that the "Blank token" takes.
* @param[in] reductionAxis The axis that the logits of each time step is on.
**/
- ASRPostProcess(AsrClassifier& classifier, TfLiteTensor* outputTensor,
- const std::vector<std::string>& labels, asr::ResultVec& result,
- uint32_t outputContextLen,
- uint32_t blankTokenIdx, uint32_t reductionAxis);
+ AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier,
+ const std::vector<std::string>& labels, asr::ResultVec& result,
+ uint32_t outputContextLen,
+ uint32_t blankTokenIdx, uint32_t reductionAxis);
/**
* @brief Should perform post-processing of the result of inference then
diff --git a/source/use_case/asr/include/Wav2LetterPreprocess.hpp b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
index 8c12b3d..dc9a415 100644
--- a/source/use_case/asr/include/Wav2LetterPreprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
@@ -31,22 +31,22 @@
* for ASR. */
using AudioWindow = audio::SlidingWindow<const int16_t>;
- class ASRPreProcess : public BasePreProcess {
+ class AsrPreProcess : public BasePreProcess {
public:
/**
* @brief Constructor.
* @param[in] inputTensor Pointer to the TFLite Micro input Tensor.
* @param[in] numMfccFeatures Number of MFCC features per window.
+ * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated
+ * for an inference.
* @param[in] mfccWindowLen Number of audio elements to calculate MFCC features per window.
* @param[in] mfccWindowStride Stride (in number of elements) for moving the MFCC window.
- * @param[in] mfccWindowStride Number of MFCC vectors that need to be calculated
- * for an inference.
*/
- ASRPreProcess(TfLiteTensor* inputTensor,
- uint32_t numMfccFeatures,
- uint32_t audioWindowLen,
- uint32_t mfccWindowLen,
- uint32_t mfccWindowStride);
+ AsrPreProcess(TfLiteTensor* inputTensor,
+ uint32_t numMfccFeatures,
+ uint32_t numFeatureFrames,
+ uint32_t mfccWindowLen,
+ uint32_t mfccWindowStride);
/**
* @brief Calculates the features required from audio data. This
@@ -130,9 +130,9 @@
}
/* Populate. */
- T * outputBufMfcc = outputBuf;
- T * outputBufD1 = outputBuf + this->m_numMfccFeats;
- T * outputBufD2 = outputBufD1 + this->m_numMfccFeats;
+ T* outputBufMfcc = outputBuf;
+ T* outputBufD1 = outputBuf + this->m_numMfccFeats;
+ T* outputBufD2 = outputBufD1 + this->m_numMfccFeats;
const uint32_t ptrIncr = this->m_numMfccFeats * 2; /* (3 vectors - 1 vector) */
const float minVal = std::numeric_limits<T>::min();
@@ -141,13 +141,13 @@
/* Need to transpose while copying and concatenating the tensor. */
for (uint32_t j = 0; j < this->m_numFeatureFrames; ++j) {
for (uint32_t i = 0; i < this->m_numMfccFeats; ++i) {
- *outputBufMfcc++ = static_cast<T>(ASRPreProcess::GetQuantElem(
+ *outputBufMfcc++ = static_cast<T>(AsrPreProcess::GetQuantElem(
this->m_mfccBuf(i, j), quantScale,
quantOffset, minVal, maxVal));
- *outputBufD1++ = static_cast<T>(ASRPreProcess::GetQuantElem(
+ *outputBufD1++ = static_cast<T>(AsrPreProcess::GetQuantElem(
this->m_delta1Buf(i, j), quantScale,
quantOffset, minVal, maxVal));
- *outputBufD2++ = static_cast<T>(ASRPreProcess::GetQuantElem(
+ *outputBufD2++ = static_cast<T>(AsrPreProcess::GetQuantElem(
this->m_delta2Buf(i, j), quantScale,
quantOffset, minVal, maxVal));
}