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

4 files changed, 28 insertions, 26 deletions

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 @@ namespace app {
          * @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 @@ namespace app {
         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 @@ namespace app {
         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 @@ namespace app {
      * @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 @@ namespace app {
      * 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 @@ namespace app {
             }
 
             /* 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 @@ namespace app {
             /* 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));
                 }