diff options
Diffstat (limited to 'source/use_case/asr')
| -rw-r--r-- | source/use_case/asr/include/AsrClassifier.hpp | 62 | ||||
| -rw-r--r-- | source/use_case/asr/include/AsrResult.hpp | 63 | ||||
| -rw-r--r-- | source/use_case/asr/include/OutputDecode.hpp | 40 | ||||
| -rw-r--r-- | source/use_case/asr/include/UseCaseHandler.hpp | 37 | ||||
| -rw-r--r-- | source/use_case/asr/include/Wav2LetterMfcc.hpp | 109 | ||||
| -rw-r--r-- | source/use_case/asr/include/Wav2LetterModel.hpp | 61 | ||||
| -rw-r--r-- | source/use_case/asr/include/Wav2LetterPostprocess.hpp | 109 | ||||
| -rw-r--r-- | source/use_case/asr/include/Wav2LetterPreprocess.hpp | 203 | ||||
| -rw-r--r-- | source/use_case/asr/src/AsrClassifier.cc | 130 | ||||
| -rw-r--r-- | source/use_case/asr/src/MainLoop.cc | 230 | ||||
| -rw-r--r-- | source/use_case/asr/src/OutputDecode.cc | 47 | ||||
| -rw-r--r-- | source/use_case/asr/src/UseCaseHandler.cc | 288 | ||||
| -rw-r--r-- | source/use_case/asr/src/Wav2LetterMfcc.cc | 137 | ||||
| -rw-r--r-- | source/use_case/asr/src/Wav2LetterModel.cc | 56 | ||||
| -rw-r--r-- | source/use_case/asr/src/Wav2LetterPostprocess.cc | 172 | ||||
| -rw-r--r-- | source/use_case/asr/src/Wav2LetterPreprocess.cc | 228 | ||||
| -rw-r--r-- | source/use_case/asr/usecase.cmake | 164 |
17 files changed, 2136 insertions, 0 deletions
diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp new file mode 100644 index 0000000..1a63814 --- /dev/null +++ b/source/use_case/asr/include/AsrClassifier.hpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_CLASSIFIER_HPP +#define ASR_CLASSIFIER_HPP + +#include "Classifier.hpp" + +namespace arm { +namespace app { + + class AsrClassifier : public Classifier { + public: + /** + * @brief Gets the top N classification results from the + * output vector. + * @param[in] outputTensor Inference output tensor from an NN model. + * @param[out] vecResults A vector of classification results + * populated by this function. + * @param[in] labels Labels vector to match classified classes + * @param[in] topNCount Number of top classifications to pick. + * @return true if successful, false otherwise. + **/ + bool GetClassificationResults( + TfLiteTensor* outputTensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, uint32_t topNCount) override; + + private: + /** + * @brief Utility function that gets the top 1 classification results from the + * output tensor (vector of vector). + * @param[in] tensor Inference output tensor from an NN model. + * @param[out] vecResults Vector of classification results populated by this function. + * @param[in] labels Labels vector to match classified classes. + * @param[in] scale Quantization scale. + * @param[in] zeroPoint Quantization zero point. + * @return true if successful, false otherwise. + **/ + template<typename T> + bool _GetTopResults(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, double scale, double zeroPoint); + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_CLASSIFIER_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/AsrResult.hpp b/source/use_case/asr/include/AsrResult.hpp new file mode 100644 index 0000000..b12ed7d --- /dev/null +++ b/source/use_case/asr/include/AsrResult.hpp @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_RESULT_HPP +#define ASR_RESULT_HPP + +#include "ClassificationResult.hpp" + +#include <vector> + +namespace arm { +namespace app { +namespace asr { + + using ResultVec = std::vector < arm::app::ClassificationResult >; + + /* Structure for holding ASR result. */ + class AsrResult { + + public: + ResultVec m_resultVec; /* Container for "thresholded" classification results. */ + float m_timeStamp; /* Audio timestamp for this result. */ + uint32_t m_inferenceNumber; /* Corresponding inference number. */ + float m_threshold; /* Threshold value for `m_resultVec.` */ + + AsrResult() = delete; + AsrResult(ResultVec& resultVec, + const float timestamp, + const uint32_t inferenceIdx, + const float scoreThreshold) { + + this->m_threshold = scoreThreshold; + this->m_timeStamp = timestamp; + this->m_inferenceNumber = inferenceIdx; + + this->m_resultVec = ResultVec(); + for (auto& i : resultVec) { + if (i.m_normalisedVal >= this->m_threshold) { + this->m_resultVec.emplace_back(i); + } + } + } + ~AsrResult() = default; + }; + +} /* namespace asr */ +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_RESULT_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/OutputDecode.hpp b/source/use_case/asr/include/OutputDecode.hpp new file mode 100644 index 0000000..6095531 --- /dev/null +++ b/source/use_case/asr/include/OutputDecode.hpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_OUTPUT_DECODE_HPP +#define ASR_OUTPUT_DECODE_HPP + +#include "AsrClassifier.hpp" + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + /** + * @brief Gets the top N classification results from the + * output vector. + * @param[in] tensor Label output from classifier. + * @return true if successful, false otherwise. + **/ + std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults); + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_OUTPUT_DECODE_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/UseCaseHandler.hpp b/source/use_case/asr/include/UseCaseHandler.hpp new file mode 100644 index 0000000..75052c7 --- /dev/null +++ b/source/use_case/asr/include/UseCaseHandler.hpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_EVT_HANDLER_HPP +#define ASR_EVT_HANDLER_HPP + +#include "AppContext.hpp" + +namespace arm { +namespace app { + + /** + * @brief Handles the inference event. + * @param[in] ctx Pointer to the application context. + * @param[in] clipIndex Index to the audio clip to classify. + * @param[in] runAll Flag to request classification of all the available audio clips. + * @return true or false based on execution success. + **/ + bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll); + +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_EVT_HANDLER_HPP */ diff --git a/source/use_case/asr/include/Wav2LetterMfcc.hpp b/source/use_case/asr/include/Wav2LetterMfcc.hpp new file mode 100644 index 0000000..3cb43b9 --- /dev/null +++ b/source/use_case/asr/include/Wav2LetterMfcc.hpp @@ -0,0 +1,109 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_WAV2LETTER_MFCC_HPP +#define ASR_WAV2LETTER_MFCC_HPP + +#include "Mfcc.hpp" + +namespace arm { +namespace app { +namespace audio { + + /* Class to provide Wav2Letter specific MFCC calculation requirements. */ + class Wav2LetterMFCC : public MFCC { + + public: + static constexpr uint32_t ms_defaultSamplingFreq = 16000; + static constexpr uint32_t ms_defaultNumFbankBins = 128; + static constexpr uint32_t ms_defaultMelLoFreq = 0; + static constexpr uint32_t ms_defaultMelHiFreq = 8000; + static constexpr bool ms_defaultUseHtkMethod = false; + + explicit Wav2LetterMFCC(const size_t numFeats, const size_t frameLen) + : MFCC(MfccParams( + ms_defaultSamplingFreq, ms_defaultNumFbankBins, + ms_defaultMelLoFreq, ms_defaultMelHiFreq, + numFeats, frameLen, ms_defaultUseHtkMethod)) + {} + + Wav2LetterMFCC() = delete; + ~Wav2LetterMFCC() = default; + + protected: + + /** + * @brief Overrides base class implementation of this function. + * @param[in] fftVec Vector populated with FFT magnitudes + * @param[in] melFilterBank 2D Vector with filter bank weights + * @param[in] filterBankFilterFirst Vector containing the first indices of filter bank + * to be used for each bin. + * @param[in] filterBankFilterLast Vector containing the last indices of filter bank + * to be used for each bin. + * @param[out] melEnergies Pre-allocated vector of MEL energies to be + * populated. + * @return true if successful, false otherwise + */ + bool ApplyMelFilterBank( + std::vector<float>& fftVec, + std::vector<std::vector<float>>& melFilterBank, + std::vector<int32_t>& filterBankFilterFirst, + std::vector<int32_t>& filterBankFilterLast, + std::vector<float>& melEnergies) override; + + /** + * @brief Override for the base class implementation convert mel + * energies to logarithmic scale. The difference from + * default behaviour is that the power is converted to dB + * and subsequently clamped. + * @param[in,out] melEnergies 1D vector of Mel energies + **/ + void ConvertToLogarithmicScale(std::vector<float>& melEnergies) override; + + /** + * @brief Create a matrix used to calculate Discrete Cosine + * Transform. Override for the base class' default + * implementation as the first and last elements + * use a different normaliser. + * @param[in] inputLength input length of the buffer on which + * DCT will be performed + * @param[in] coefficientCount Total coefficients per input length. + * @return 1D vector with inputLength x coefficientCount elements + * populated with DCT coefficients. + */ + std::vector<float> CreateDCTMatrix(int32_t inputLength, + int32_t coefficientCount) override; + + /** + * @brief Given the low and high Mel values, get the normaliser + * for weights to be applied when populating the filter + * bank. Override for the base class implementation. + * @param[in] leftMel Low Mel frequency value. + * @param[in] rightMel High Mel frequency value. + * @param[in] useHTKMethod bool to signal if HTK method is to be + * used for calculation. + * @return Value to use for normalising. + */ + float GetMelFilterBankNormaliser(const float& leftMel, + const float& rightMel, + bool useHTKMethod) override; + }; + +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_WAV2LETTER_MFCC_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/Wav2LetterModel.hpp b/source/use_case/asr/include/Wav2LetterModel.hpp new file mode 100644 index 0000000..b801e10 --- /dev/null +++ b/source/use_case/asr/include/Wav2LetterModel.hpp @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved.rved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_WAV2LETTER_MODEL_HPP +#define ASR_WAV2LETTER_MODEL_HPP + +#include "Model.hpp" + +extern const int g_FrameLength; +extern const int g_FrameStride; +extern const float g_ScoreThreshold; +extern const int g_ctxLen; + +namespace arm { +namespace app { + + class Wav2LetterModel : public Model { + + public: + /* Indices for the expected model - based on input and output tensor shapes */ + static constexpr uint32_t ms_inputRowsIdx = 1; + static constexpr uint32_t ms_inputColsIdx = 2; + static constexpr uint32_t ms_outputRowsIdx = 2; + static constexpr uint32_t ms_outputColsIdx = 3; + + protected: + /** @brief Gets the reference to op resolver interface class. */ + const tflite::MicroOpResolver& GetOpResolver() override; + + /** @brief Adds operations to the op resolver instance. */ + bool EnlistOperations() override; + + const uint8_t* ModelPointer() override; + + size_t ModelSize() override; + + private: + /* Maximum number of individual operations that can be enlisted. */ + static constexpr int _ms_maxOpCnt = 5; + + /* A mutable op resolver instance. */ + tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver; + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_WAV2LETTER_MODEL_HPP */ diff --git a/source/use_case/asr/include/Wav2LetterPostprocess.hpp b/source/use_case/asr/include/Wav2LetterPostprocess.hpp new file mode 100644 index 0000000..69567a3 --- /dev/null +++ b/source/use_case/asr/include/Wav2LetterPostprocess.hpp @@ -0,0 +1,109 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_WAV2LETTER_POSTPROCESS_HPP +#define ASR_WAV2LETTER_POSTPROCESS_HPP + +#include "TensorFlowLiteMicro.hpp" /* TensorFlow headers. */ +#include "hal.h" /* stdout facility. */ + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + /** + * @brief Helper class to manage tensor post-processing for "wav2letter" + * output. + */ + class Postprocess { + public: + /** + * @brief Constructor + * @param[in] contextLen Left and right context length for + * output tensor. + * @param[in] innerLen This is the length of the section + * between left and right context. + **/ + Postprocess(uint32_t contextLen, + uint32_t innerLen, + uint32_t blankTokenIdx); + + Postprocess() = delete; + ~Postprocess() = default; + + /** + * @brief Erases the required part of the tensor based + * on context lengths set up during initialisation. + * @param[in] tensor Pointer to the tensor. + * @param[in] axisIdx Index of the axis on which erase is + * performed. + * @param[in] lastIteration Flag to signal this is the + * last iteration in which case + * the right context is preserved. + * @return true if successful, false otherwise. + */ + bool Invoke(TfLiteTensor* tensor, + uint32_t axisIdx, + bool lastIteration = false); + + private: + uint32_t _m_contextLen; /* lengths of left and right contexts. */ + uint32_t _m_innerLen; /* Length of inner context. */ + uint32_t _m_totalLen; /* Total length of the required axis. */ + uint32_t _m_countIterations; /* Current number of iterations. */ + uint32_t _m_blankTokenIdx; /* Index of the labels blank token. */ + /** + * @brief Checks if the tensor and axis index are valid + * inputs to the object - based on how it has been + * initialised. + * @return true if valid, false otherwise. + */ + bool _IsInputValid(TfLiteTensor* tensor, + uint32_t axisIdx) const; + + /** + * @brief Gets the tensor data element size in bytes based + * on the tensor type. + * @return Size in bytes, 0 if not supported. + */ + uint32_t _GetTensorElementSize(TfLiteTensor* tensor); + + /** + * @brief Erases sections from the data assuming row-wise + * arrangement along the context axis. + * @return true if successful, false otherwise. + */ + bool _EraseSectionsRowWise(uint8_t* ptrData, + uint32_t strideSzBytes, + bool lastIteration); + + /** + * @brief Erases sections from the data assuming col-wise + * arrangement along the context axis. + * @return true if successful, false otherwise. + */ + bool _EraseSectionsColWise(uint8_t* ptrData, + uint32_t strideSzBytes, + bool lastIteration); + }; + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_WAV2LETTER_POSTPROCESS_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/include/Wav2LetterPreprocess.hpp b/source/use_case/asr/include/Wav2LetterPreprocess.hpp new file mode 100644 index 0000000..8a4e0b7 --- /dev/null +++ b/source/use_case/asr/include/Wav2LetterPreprocess.hpp @@ -0,0 +1,203 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef ASR_WAV2LETTER_PREPROCESS_HPP +#define ASR_WAV2LETTER_PREPROCESS_HPP + +#include "Wav2LetterModel.hpp" +#include "Wav2LetterMfcc.hpp" +#include "AudioUtils.hpp" +#include "DataStructures.hpp" + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + /* Class to facilitate pre-processing calculation for Wav2Letter model + * for ASR. */ + using AudioWindow = SlidingWindow <const int16_t>; + + class Preprocess { + public: + /** + * @brief Constructor. + * @param[in] numMfccFeatures Number of MFCC features per window. + * @param[in] windowLen Number of elements in a window. + * @param[in] windowStride Stride (in number of elements) for + * moving the window. + * @param[in] numMfccVectors Number of MFCC vectors per window. + */ + Preprocess( + uint32_t numMfccFeatures, + uint32_t windowLen, + uint32_t windowStride, + uint32_t numMfccVectors); + Preprocess() = delete; + ~Preprocess() = default; + + /** + * @brief Calculates the features required from audio data. This + * includes MFCC, first and second order deltas, + * normalisation and finally, quantisation. The tensor is + * populated with feature from a given window placed along + * in a single row. + * @param[in] audioData Pointer to the first element of audio data. + * @param[in] audioDataLen Number of elements in the audio data. + * @param[in] tensor Tensor to be populated. + * @return true if successful, false in case of error. + */ + bool Invoke(const int16_t * audioData, + uint32_t audioDataLen, + TfLiteTensor * tensor); + + protected: + /** + * @brief Computes the first and second order deltas for the + * MFCC buffers - they are assumed to be populated. + * + * @param[in] mfcc MFCC buffers. + * @param[out] delta1 Result of the first diff computation. + * @param[out] delta2 Result of the second diff computation. + * @return true if successful, false otherwise. + */ + static bool _ComputeDeltas(Array2d<float>& mfcc, + Array2d<float>& delta1, + Array2d<float>& delta2); + + /** + * @brief Given a 2D vector of floats, computes the mean. + * @param[in] vec Vctor of vector of floats. + * @return Mean value. + */ + static float _GetMean(Array2d<float>& vec); + + /** + * @brief Given a 2D vector of floats, computes the stddev. + * @param[in] vec Vector of vector of floats. + * @param[in] mean Mean value of the vector passed in. + * @return stddev value. + */ + static float _GetStdDev(Array2d<float>& vec, + float mean); + + /** + * @brief Given a 2D vector of floats, normalises it using + * the mean and the stddev. + * @param[in,out] vec Vector of vector of floats. + */ + static void _NormaliseVec(Array2d<float>& vec); + + /** + * @brief Normalises the MFCC and delta buffers. + */ + void _Normalise(); + + /** + * @brief Given the quantisation and data type limits, computes + * the quantised values of a floating point input data. + * @param[in] elem Element to be quantised. + * @param[in] quantScale Scale. + * @param[in] quantOffset Offset. + * @param[in] minVal Numerical limit - minimum. + * @param[in] maxVal Numerical limit - maximum. + * @return Floating point quantised value. + */ + static float _GetQuantElem( + float elem, + float quantScale, + int quantOffset, + float minVal, + float maxVal); + + /** + * @brief Quantises the MFCC and delta buffers, and places them + * in the output buffer. While doing so, it transposes + * the data. Reason: Buffers in this class are arranged + * for "time" axis to be row major. Primary reason for + * this being the convolution speed up (as we can use + * contiguous memory). The output, however, requires the + * time axis to be in column major arrangement. + * @param[in] outputBuf Pointer to the output buffer. + * @param[in] outputBufSz Output buffer's size. + * @param[in] quantScale Quantisation scale. + * @param[in] quantOffset Quantisation offset. + */ + template <typename T> + bool _Quantise( + T * outputBuf, + const uint32_t outputBufSz, + const float quantScale, + const int quantOffset) + { + /* Check the output size will fit everything. */ + if (outputBufSz < (this->_m_mfccBuf.size(0) * 3 * sizeof(T))) { + printf_err("Tensor size too small for features\n"); + return false; + } + + /* Populate. */ + 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(); + const float maxVal = std::numeric_limits<T>::max(); + + /* Need to transpose while copying and concatenating the tensor. */ + for (uint32_t j = 0; j < this->_m_numFeatVectors; ++j) { + for (uint32_t i = 0; i < this->_m_numMfccFeats; ++i) { + *outputBufMfcc++ = static_cast<T>(Preprocess::_GetQuantElem( + this->_m_mfccBuf(i, j), quantScale, + quantOffset, minVal, maxVal)); + *outputBufD1++ = static_cast<T>(Preprocess::_GetQuantElem( + this->_m_delta1Buf(i, j), quantScale, + quantOffset, minVal, maxVal)); + *outputBufD2++ = static_cast<T>(Preprocess::_GetQuantElem( + this->_m_delta2Buf(i, j), quantScale, + quantOffset, minVal, maxVal)); + } + outputBufMfcc += ptrIncr; + outputBufD1 += ptrIncr; + outputBufD2 += ptrIncr; + } + + return true; + } + + private: + Wav2LetterMFCC _m_mfcc; /* MFCC instance. */ + + /* Actual buffers to be populated. */ + Array2d<float> _m_mfccBuf; /* Contiguous buffer 1D: MFCC */ + Array2d<float> _m_delta1Buf; /* Contiguous buffer 1D: Delta 1 */ + Array2d<float> _m_delta2Buf; /* Contiguous buffer 1D: Delta 2 */ + + uint32_t _m_windowLen; /* Window length for MFCC. */ + uint32_t _m_windowStride; /* Window stride len for MFCC. */ + uint32_t _m_numMfccFeats; /* Number of MFCC features per window. */ + uint32_t _m_numFeatVectors; /* Number of _m_numMfccFeats. */ + AudioWindow _m_window; /* Sliding window. */ + + }; + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */ + +#endif /* ASR_WAV2LETTER_PREPROCESS_HPP */
\ No newline at end of file diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc new file mode 100644 index 0000000..7377d30 --- /dev/null +++ b/source/use_case/asr/src/AsrClassifier.cc @@ -0,0 +1,130 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "AsrClassifier.hpp" + +#include "hal.h" +#include "TensorFlowLiteMicro.hpp" +#include "Wav2LetterModel.hpp" + +template<typename T> +bool arm::app::AsrClassifier::_GetTopResults(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, double scale, double zeroPoint) +{ + const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx]; + const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]; + + /* NOTE: tensor's size verification against labels should be + * checked by the calling/public function. */ + if (nLetters < 1) { + return false; + } + + /* Final results' container. */ + vecResults = std::vector<ClassificationResult>(nElems); + + T* tensorData = tflite::GetTensorData<T>(tensor); + + /* Get the top 1 results. */ + for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) { + std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0); + + for (uint32_t j = 1; j < nLetters; ++j) { + if (top_1.first < tensorData[row + j]) { + top_1.first = tensorData[row + j]; + top_1.second = j; + } + } + + double score = static_cast<int> (top_1.first); + vecResults[i].m_normalisedVal = scale * (score - zeroPoint); + vecResults[i].m_label = labels[top_1.second]; + vecResults[i].m_labelIdx = top_1.second; + } + + return true; +} +template bool arm::app::AsrClassifier::_GetTopResults<uint8_t>(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, double scale, double zeroPoint); +template bool arm::app::AsrClassifier::_GetTopResults<int8_t>(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, double scale, double zeroPoint); + +bool arm::app::AsrClassifier::GetClassificationResults( + TfLiteTensor* outputTensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, uint32_t topNCount) +{ + vecResults.clear(); + + constexpr int minTensorDims = static_cast<int>( + (arm::app::Wav2LetterModel::ms_outputRowsIdx > arm::app::Wav2LetterModel::ms_outputColsIdx)? + arm::app::Wav2LetterModel::ms_outputRowsIdx : arm::app::Wav2LetterModel::ms_outputColsIdx); + + constexpr uint32_t outColsIdx = arm::app::Wav2LetterModel::ms_outputColsIdx; + + /* Sanity checks. */ + if (outputTensor == nullptr) { + printf_err("Output vector is null pointer.\n"); + return false; + } else if (outputTensor->dims->size < minTensorDims) { + printf_err("Output tensor expected to be %dD\n", minTensorDims); + return false; + } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) { + printf_err("Output vectors are smaller than %u\n", topNCount); + return false; + } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) { + printf("Output size doesn't match the labels' size\n"); + return false; + } + + if (topNCount != 1) { + warn("TopNCount value ignored in this implementation\n"); + } + + /* To return the floating point values, we need quantization parameters. */ + QuantParams quantParams = GetTensorQuantParams(outputTensor); + + bool resultState; + + switch (outputTensor->type) { + case kTfLiteUInt8: + resultState = this->_GetTopResults<uint8_t>( + outputTensor, vecResults, + labels, quantParams.scale, + quantParams.offset); + break; + case kTfLiteInt8: + resultState = this->_GetTopResults<int8_t>( + outputTensor, vecResults, + labels, quantParams.scale, + quantParams.offset); + break; + default: + printf_err("Tensor type %s not supported by classifier\n", + TfLiteTypeGetName(outputTensor->type)); + return false; + } + + if (!resultState) { + printf_err("Failed to get sorted set\n"); + return false; + } + + return true; +}
\ No newline at end of file diff --git a/source/use_case/asr/src/MainLoop.cc b/source/use_case/asr/src/MainLoop.cc new file mode 100644 index 0000000..ca777be --- /dev/null +++ b/source/use_case/asr/src/MainLoop.cc @@ -0,0 +1,230 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "hal.h" /* Brings in platform definitions. */ +#include "Labels.hpp" /* For label strings. */ +#include "UseCaseHandler.hpp" /* Handlers for different user options. */ +#include "Wav2LetterModel.hpp" /* Model class for running inference. */ +#include "UseCaseCommonUtils.hpp" /* Utils functions. */ +#include "AsrClassifier.hpp" /* Classifier. */ +#include "InputFiles.hpp" /* Generated audio clip header. */ +#include "Wav2LetterPreprocess.hpp" /* Pre-processing class. */ +#include "Wav2LetterPostprocess.hpp" /* Post-processing class. */ + +enum opcodes +{ + MENU_OPT_RUN_INF_NEXT = 1, /* Run on next vector. */ + MENU_OPT_RUN_INF_CHOSEN, /* Run on a user provided vector index. */ + MENU_OPT_RUN_INF_ALL, /* Run inference on all. */ + MENU_OPT_SHOW_MODEL_INFO, /* Show model info. */ + MENU_OPT_LIST_AUDIO_CLIPS /* List the current baked audio clips. */ +}; + +static void DisplayMenu() +{ + printf("\n\nUser input required\n"); + printf("Enter option number from:\n\n"); + printf(" %u. Classify next audio clip\n", MENU_OPT_RUN_INF_NEXT); + printf(" %u. Classify audio clip at chosen index\n", MENU_OPT_RUN_INF_CHOSEN); + printf(" %u. Run classification on all audio clips\n", MENU_OPT_RUN_INF_ALL); + printf(" %u. Show NN model info\n", MENU_OPT_SHOW_MODEL_INFO); + printf(" %u. List audio clips\n\n", MENU_OPT_LIST_AUDIO_CLIPS); + printf(" Choice: "); +} + +/** @brief Verify input and output tensor are of certain min dimensions. */ +static bool VerifyTensorDimensions(const arm::app::Model& model); + +/** @brief Gets the number of MFCC features for a single window. */ +static uint32_t GetNumMfccFeatures(const arm::app::Model& model); + +/** @brief Gets the number of MFCC feature vectors to be computed. */ +static uint32_t GetNumMfccFeatureVectors(const arm::app::Model& model); + +/** @brief Gets the output context length (left and right) for post-processing. */ +static uint32_t GetOutputContextLen(const arm::app::Model& model, + uint32_t inputCtxLen); + +/** @brief Gets the output inner length for post-processing. */ +static uint32_t GetOutputInnerLen(const arm::app::Model& model, + uint32_t outputCtxLen); + +void main_loop(hal_platform& platform) +{ + arm::app::Wav2LetterModel model; /* Model wrapper object. */ + + /* Load the model. */ + if (!model.Init()) { + printf_err("Failed to initialise model\n"); + return; + } else if (!VerifyTensorDimensions(model)) { + printf_err("Model's input or output dimension verification failed\n"); + return; + } + + /* Initialise pre-processing. */ + arm::app::audio::asr::Preprocess prep( + GetNumMfccFeatures(model), + g_FrameLength, + g_FrameStride, + GetNumMfccFeatureVectors(model)); + + /* Initialise post-processing. */ + const uint32_t outputCtxLen = GetOutputContextLen(model, g_ctxLen); + const uint32_t blankTokenIdx = 28; + arm::app::audio::asr::Postprocess postp( + outputCtxLen, + GetOutputInnerLen(model, outputCtxLen), + blankTokenIdx); + + /* Instantiate application context. */ + arm::app::ApplicationContext caseContext; + std::vector <std::string> labels; + GetLabelsVector(labels); + arm::app::AsrClassifier classifier; /* Classifier wrapper object. */ + + caseContext.Set<hal_platform&>("platform", platform); + caseContext.Set<arm::app::Model&>("model", model); + caseContext.Set<uint32_t>("clipIndex", 0); + caseContext.Set<uint32_t>("frameLength", g_FrameLength); + caseContext.Set<uint32_t>("frameStride", g_FrameStride); + caseContext.Set<float>("scoreThreshold", g_ScoreThreshold); /* Score threshold. */ + caseContext.Set<uint32_t>("ctxLen", g_ctxLen); /* Left and right context length (MFCC feat vectors). */ + caseContext.Set<const std::vector <std::string>&>("labels", labels); + caseContext.Set<arm::app::AsrClassifier&>("classifier", classifier); + caseContext.Set<arm::app::audio::asr::Preprocess&>("preprocess", prep); + caseContext.Set<arm::app::audio::asr::Postprocess&>("postprocess", postp); + + bool executionSuccessful = true; + constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false; + + /* Loop. */ + do { + int menuOption = MENU_OPT_RUN_INF_NEXT; + if (bUseMenu) { + DisplayMenu(); + menuOption = arm::app::ReadUserInputAsInt(platform); + printf("\n"); + } + switch (menuOption) { + case MENU_OPT_RUN_INF_NEXT: + executionSuccessful = ClassifyAudioHandler( + caseContext, + caseContext.Get<uint32_t>("clipIndex"), + false); + break; + case MENU_OPT_RUN_INF_CHOSEN: { + printf(" Enter the audio clip index [0, %d]: ", + NUMBER_OF_FILES-1); + auto clipIndex = static_cast<uint32_t>( + arm::app::ReadUserInputAsInt(platform)); + executionSuccessful = ClassifyAudioHandler(caseContext, + clipIndex, + false); + break; + } + case MENU_OPT_RUN_INF_ALL: + executionSuccessful = ClassifyAudioHandler( + caseContext, + caseContext.Get<uint32_t>("clipIndex"), + true); + break; + case MENU_OPT_SHOW_MODEL_INFO: + executionSuccessful = model.ShowModelInfoHandler(); + break; + case MENU_OPT_LIST_AUDIO_CLIPS: + executionSuccessful = ListFilesHandler(caseContext); + break; + default: + printf("Incorrect choice, try again."); + break; + } + } while (executionSuccessful && bUseMenu); + info("Main loop terminated.\n"); +} + +static bool VerifyTensorDimensions(const arm::app::Model& model) +{ + /* Populate tensor related parameters. */ + TfLiteTensor* inputTensor = model.GetInputTensor(0); + if (!inputTensor->dims) { + printf_err("Invalid input tensor dims\n"); + return false; + } else if (inputTensor->dims->size < 3) { + printf_err("Input tensor dimension should be >= 3\n"); + return false; + } + + TfLiteTensor* outputTensor = model.GetOutputTensor(0); + if (!outputTensor->dims) { + printf_err("Invalid output tensor dims\n"); + return false; + } else if (outputTensor->dims->size < 3) { + printf_err("Output tensor dimension should be >= 3\n"); + return false; + } + + return true; +} + +static uint32_t GetNumMfccFeatures(const arm::app::Model& model) +{ + TfLiteTensor* inputTensor = model.GetInputTensor(0); + const int inputCols = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputColsIdx]; + if (0 != inputCols % 3) { + printf_err("Number of input columns is not a multiple of 3\n"); + } + return std::max(inputCols/3, 0); +} + +static uint32_t GetNumMfccFeatureVectors(const arm::app::Model& model) +{ + TfLiteTensor* inputTensor = model.GetInputTensor(0); + const int inputRows = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx]; + return std::max(inputRows, 0); +} + +static uint32_t GetOutputContextLen(const arm::app::Model& model, const uint32_t inputCtxLen) +{ + const uint32_t inputRows = GetNumMfccFeatureVectors(model); + const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen); + constexpr uint32_t ms_outputRowsIdx = arm::app::Wav2LetterModel::ms_outputRowsIdx; + + /* Check to make sure that the input tensor supports the above + * context and inner lengths. */ + if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) { + printf_err("Input rows not compatible with ctx of %u\n", + inputCtxLen); + return 0; + } + + TfLiteTensor* outputTensor = model.GetOutputTensor(0); + const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); + + const float tensorColRatio = static_cast<float>(inputRows)/ + static_cast<float>(outputRows); + + return std::round(static_cast<float>(inputCtxLen)/tensorColRatio); +} + +static uint32_t GetOutputInnerLen(const arm::app::Model& model, + const uint32_t outputCtxLen) +{ + constexpr uint32_t ms_outputRowsIdx = arm::app::Wav2LetterModel::ms_outputRowsIdx; + TfLiteTensor* outputTensor = model.GetOutputTensor(0); + const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); + return (outputRows - (2 * outputCtxLen)); +} diff --git a/source/use_case/asr/src/OutputDecode.cc b/source/use_case/asr/src/OutputDecode.cc new file mode 100644 index 0000000..41fbe07 --- /dev/null +++ b/source/use_case/asr/src/OutputDecode.cc @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "OutputDecode.hpp" + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults) + { + std::string CleanOutputBuffer; + + for (size_t i = 0; i < vecResults.size(); ++i) /* For all elements in vector. */ + { + while (i+1 < vecResults.size() && + vecResults[i].m_label == vecResults[i+1].m_label) /* While the current element is equal to the next, ignore it and move on. */ + { + ++i; + } + if (vecResults[i].m_label != "$") /* $ is a character used to represent unknown and double characters so should not be in output. */ + { + CleanOutputBuffer += vecResults[i].m_label; /* If the element is different to the next, it will be appended to CleanOutputBuffer. */ + } + } + + return CleanOutputBuffer; /* Return string type containing clean output. */ + } + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */ diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc new file mode 100644 index 0000000..e706eb8 --- /dev/null +++ b/source/use_case/asr/src/UseCaseHandler.cc @@ -0,0 +1,288 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "UseCaseHandler.hpp" + +#include "InputFiles.hpp" +#include "AsrClassifier.hpp" +#include "Wav2LetterModel.hpp" +#include "hal.h" +#include "Wav2LetterMfcc.hpp" +#include "AudioUtils.hpp" +#include "UseCaseCommonUtils.hpp" +#include "AsrResult.hpp" +#include "Wav2LetterPreprocess.hpp" +#include "Wav2LetterPostprocess.hpp" +#include "OutputDecode.hpp" + +namespace arm { +namespace app { + + /** + * @brief Helper function to increment current audio clip index. + * @param[in,out] ctx Pointer to the application context object. + **/ + static void _IncrementAppCtxClipIdx(ApplicationContext& ctx); + + /** + * @brief Helper function to set the audio clip index. + * @param[in,out] ctx Pointer to the application context object. + * @param[in] idx Value to be set. + * @return true if index is set, false otherwise. + **/ + static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx); + + /** + * @brief Presents inference results using the data presentation + * object. + * @param[in] platform Reference to the hal platform object. + * @param[in] results Vector of classification results to be displayed. + * @param[in] infTimeMs Inference time in milliseconds, if available + * otherwise, this can be passed in as 0. + * @return true if successful, false otherwise. + **/ + static bool _PresentInferenceResult( + hal_platform& platform, + const std::vector<arm::app::asr::AsrResult>& results); + + /* Audio inference classification handler. */ + bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) + { + constexpr uint32_t dataPsnTxtInfStartX = 20; + constexpr uint32_t dataPsnTxtInfStartY = 40; + + auto& platform = ctx.Get<hal_platform&>("platform"); + platform.data_psn->clear(COLOR_BLACK); + + /* If the request has a valid size, set the audio index. */ + if (clipIndex < NUMBER_OF_FILES) { + if (!_SetAppCtxClipIdx(ctx, clipIndex)) { + return false; + } + } + + /* Get model reference. */ + auto& model = ctx.Get<Model&>("model"); + if (!model.IsInited()) { + printf_err("Model is not initialised! Terminating processing.\n"); + return false; + } + + /* Get score threshold to be applied for the classifier (post-inference). */ + auto scoreThreshold = ctx.Get<float>("scoreThreshold"); + + /* Get tensors. Dimensions of the tensor should have been verified by + * the callee. */ + TfLiteTensor* inputTensor = model.GetInputTensor(0); + TfLiteTensor* outputTensor = model.GetOutputTensor(0); + const uint32_t inputRows = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx]; + + /* Populate MFCC related parameters. */ + auto mfccParamsWinLen = ctx.Get<uint32_t>("frameLength"); + auto mfccParamsWinStride = ctx.Get<uint32_t>("frameStride"); + + /* Populate ASR inference context and inner lengths for input. */ + auto inputCtxLen = ctx.Get<uint32_t>("ctxLen"); + const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen); + + /* Audio data stride corresponds to inputInnerLen feature vectors. */ + const uint32_t audioParamsWinLen = (inputRows - 1) * mfccParamsWinStride + (mfccParamsWinLen); + const uint32_t audioParamsWinStride = inputInnerLen * mfccParamsWinStride; + const float audioParamsSecondsPerSample = (1.0/audio::Wav2LetterMFCC::ms_defaultSamplingFreq); + + /* Get pre/post-processing objects. */ + auto& prep = ctx.Get<audio::asr::Preprocess&>("preprocess"); + auto& postp = ctx.Get<audio::asr::Postprocess&>("postprocess"); + + /* Set default reduction axis for post-processing. */ + const uint32_t reductionAxis = arm::app::Wav2LetterModel::ms_outputRowsIdx; + + /* Audio clip start index. */ + auto startClipIdx = ctx.Get<uint32_t>("clipIndex"); + + /* Loop to process audio clips. */ + do { + /* Get current audio clip index. */ + auto currentIndex = ctx.Get<uint32_t>("clipIndex"); + + /* Get the current audio buffer and respective size. */ + const int16_t* audioArr = get_audio_array(currentIndex); + const uint32_t audioArrSize = get_audio_array_size(currentIndex); + + if (!audioArr) { + printf_err("Invalid audio array pointer\n"); + return false; + } + + /* Audio clip must have enough samples to produce 1 MFCC feature. */ + if (audioArrSize < mfccParamsWinLen) { + printf_err("Not enough audio samples, minimum needed is %u\n", mfccParamsWinLen); + return false; + } + + /* Initialise an audio slider. */ + auto audioDataSlider = audio::ASRSlidingWindow<const int16_t>( + audioArr, + audioArrSize, + audioParamsWinLen, + audioParamsWinStride); + + /* Declare a container for results. */ + std::vector<arm::app::asr::AsrResult> results; + + /* Display message on the LCD - inference running. */ + std::string str_inf{"Running inference... "}; + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + info("Running inference on audio clip %u => %s\n", currentIndex, + get_filename(currentIndex)); + + size_t inferenceWindowLen = audioParamsWinLen; + + /* Start sliding through audio clip. */ + while (audioDataSlider.HasNext()) { + + /* If not enough audio see how much can be sent for processing. */ + size_t nextStartIndex = audioDataSlider.NextWindowStartIndex(); + if (nextStartIndex + audioParamsWinLen > audioArrSize) { + inferenceWindowLen = audioArrSize - nextStartIndex; + } + + const int16_t* inferenceWindow = audioDataSlider.Next(); + + info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, + static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1))); + + Profiler prepProfiler{&platform, "pre-processing"}; + prepProfiler.StartProfiling(); + + /* Calculate MFCCs, deltas and populate the input tensor. */ + prep.Invoke(inferenceWindow, inferenceWindowLen, inputTensor); + + prepProfiler.StopProfiling(); + std::string prepProfileResults = prepProfiler.GetResultsAndReset(); + info("%s\n", prepProfileResults.c_str()); + + /* Run inference over this audio clip sliding window. */ + arm::app::RunInference(platform, model); + + /* Post-process. */ + postp.Invoke(outputTensor, reductionAxis, !audioDataSlider.HasNext()); + + /* Get results. */ + std::vector<ClassificationResult> classificationResult; + auto& classifier = ctx.Get<AsrClassifier&>("classifier"); + classifier.GetClassificationResults( + outputTensor, classificationResult, + ctx.Get<std::vector<std::string>&>("labels"), 1); + + results.emplace_back(asr::AsrResult(classificationResult, + (audioDataSlider.Index() * + audioParamsSecondsPerSample * + audioParamsWinStride), + audioDataSlider.Index(), scoreThreshold)); + +#if VERIFY_TEST_OUTPUT + arm::app::DumpTensor(outputTensor, + outputTensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]); +#endif /* VERIFY_TEST_OUTPUT */ + + } + + /* Erase. */ + str_inf = std::string(str_inf.size(), ' '); + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + ctx.Set<std::vector<arm::app::asr::AsrResult>>("results", results); + + if (!_PresentInferenceResult(platform, results)) { + return false; + } + + _IncrementAppCtxClipIdx(ctx); + + } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx); + + return true; + } + + static void _IncrementAppCtxClipIdx(ApplicationContext& ctx) + { + auto curAudioIdx = ctx.Get<uint32_t>("clipIndex"); + + if (curAudioIdx + 1 >= NUMBER_OF_FILES) { + ctx.Set<uint32_t>("clipIndex", 0); + return; + } + ++curAudioIdx; + ctx.Set<uint32_t>("clipIndex", curAudioIdx); + } + + static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx) + { + if (idx >= NUMBER_OF_FILES) { + printf_err("Invalid idx %u (expected less than %u)\n", + idx, NUMBER_OF_FILES); + return false; + } + + ctx.Set<uint32_t>("clipIndex", idx); + return true; + } + + static bool _PresentInferenceResult(hal_platform& platform, + const std::vector<arm::app::asr::AsrResult>& results) + { + constexpr uint32_t dataPsnTxtStartX1 = 20; + constexpr uint32_t dataPsnTxtStartY1 = 60; + constexpr bool allow_multiple_lines = true; + + platform.data_psn->set_text_color(COLOR_GREEN); + + /* Results from multiple inferences should be combined before processing. */ + std::vector<arm::app::ClassificationResult> combinedResults; + for (auto& result : results) { + combinedResults.insert(combinedResults.end(), + result.m_resultVec.begin(), + result.m_resultVec.end()); + } + + /* Get each inference result string using the decoder. */ + for (const auto & result : results) { + std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec); + + info("Result for inf %u: %s\n", result.m_inferenceNumber, + infResultStr.c_str()); + } + + /* Get the decoded result for the combined result. */ + std::string finalResultStr = audio::asr::DecodeOutput(combinedResults); + + platform.data_psn->present_data_text( + finalResultStr.c_str(), finalResultStr.size(), + dataPsnTxtStartX1, dataPsnTxtStartY1, + allow_multiple_lines); + + info("Final result: %s\n", finalResultStr.c_str()); + return true; + } + +} /* namespace app */ +} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/asr/src/Wav2LetterMfcc.cc b/source/use_case/asr/src/Wav2LetterMfcc.cc new file mode 100644 index 0000000..92c91bc --- /dev/null +++ b/source/use_case/asr/src/Wav2LetterMfcc.cc @@ -0,0 +1,137 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "Wav2LetterMfcc.hpp" + +#include "PlatformMath.hpp" + +#include <cfloat> + +namespace arm { +namespace app { +namespace audio { + + bool Wav2LetterMFCC::ApplyMelFilterBank( + std::vector<float>& fftVec, + std::vector<std::vector<float>>& melFilterBank, + std::vector<int32_t>& filterBankFilterFirst, + std::vector<int32_t>& filterBankFilterLast, + std::vector<float>& melEnergies) + { + const size_t numBanks = melEnergies.size(); + + if (numBanks != filterBankFilterFirst.size() || + numBanks != filterBankFilterLast.size()) { + printf_err("Unexpected filter bank lengths\n"); + return false; + } + + for (size_t bin = 0; bin < numBanks; ++bin) { + auto filterBankIter = melFilterBank[bin].begin(); + float melEnergy = 1e-10; /* Avoid log of zero at later stages, same value used in librosa. */ + const int32_t firstIndex = filterBankFilterFirst[bin]; + const int32_t lastIndex = filterBankFilterLast[bin]; + + for (int32_t i = firstIndex; i <= lastIndex; ++i) { + melEnergy += (*filterBankIter++ * fftVec[i]); + } + + melEnergies[bin] = melEnergy; + } + + return true; + } + + void Wav2LetterMFCC::ConvertToLogarithmicScale( + std::vector<float>& melEnergies) + { + float maxMelEnergy = -FLT_MAX; + + /* Container for natural logarithms of mel energies. */ + std::vector <float> vecLogEnergies(melEnergies.size(), 0.f); + + /* Because we are taking natural logs, we need to multiply by log10(e). + * Also, for wav2letter model, we scale our log10 values by 10. */ + constexpr float multiplier = 10.0 * /* Default scalar. */ + 0.4342944819032518; /* log10f(std::exp(1.0)) */ + + /* Take log of the whole vector. */ + math::MathUtils::VecLogarithmF32(melEnergies, vecLogEnergies); + + /* Scale the log values and get the max. */ + for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin(); + iterM != melEnergies.end(); ++iterM, ++iterL) { + + *iterM = *iterL * multiplier; + + /* Save the max mel energy. */ + if (*iterM > maxMelEnergy) { + maxMelEnergy = *iterM; + } + } + + /* Clamp the mel energies. */ + constexpr float maxDb = 80.0; + const float clampLevelLowdB = maxMelEnergy - maxDb; + for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter) { + *iter = std::max(*iter, clampLevelLowdB); + } + } + + std::vector<float> Wav2LetterMFCC::CreateDCTMatrix( + const int32_t inputLength, + const int32_t coefficientCount) + { + std::vector<float> dctMatix(inputLength * coefficientCount); + + /* Orthonormal normalization. */ + const float normalizerK0 = 2 * math::MathUtils::SqrtF32(1.0f / + static_cast<float>(4*inputLength)); + const float normalizer = 2 * math::MathUtils::SqrtF32(1.0f / + static_cast<float>(2*inputLength)); + + const float angleIncr = M_PI / inputLength; + float angle = angleIncr; /* We start using it at k = 1 loop. */ + + /* First row of DCT will use normalizer K0. */ + for (int32_t n = 0; n < inputLength; ++n) { + dctMatix[n] = normalizerK0 /* cos(0) = 1 */; + } + + /* Second row (index = 1) onwards, we use standard normalizer. */ + for (int32_t k = 1, m = inputLength; k < coefficientCount; ++k, m += inputLength) { + for (int32_t n = 0; n < inputLength; ++n) { + dctMatix[m+n] = normalizer * + math::MathUtils::CosineF32((n + 0.5f) * angle); + } + angle += angleIncr; + } + return dctMatix; + } + + float Wav2LetterMFCC::GetMelFilterBankNormaliser( + const float& leftMel, + const float& rightMel, + const bool useHTKMethod) + { + /* Slaney normalization for mel weights. */ + return (2.0f / (MFCC::InverseMelScale(rightMel, useHTKMethod) - + MFCC::InverseMelScale(leftMel, useHTKMethod))); + } + +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */ diff --git a/source/use_case/asr/src/Wav2LetterModel.cc b/source/use_case/asr/src/Wav2LetterModel.cc new file mode 100644 index 0000000..5aefecd --- /dev/null +++ b/source/use_case/asr/src/Wav2LetterModel.cc @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "Wav2LetterModel.hpp" + +#include "hal.h" + +const tflite::MicroOpResolver& arm::app::Wav2LetterModel::GetOpResolver() +{ + return this->_m_opResolver; +} + +bool arm::app::Wav2LetterModel::EnlistOperations() +{ + this->_m_opResolver.AddConv2D(); + this->_m_opResolver.AddMul(); + this->_m_opResolver.AddMaximum(); + this->_m_opResolver.AddReshape(); + +#if defined(ARM_NPU) + if (kTfLiteOk == this->_m_opResolver.AddEthosU()) { + info("Added %s support to op resolver\n", + tflite::GetString_ETHOSU()); + } else { + printf_err("Failed to add Arm NPU support to op resolver."); + return false; + } +#endif /* ARM_NPU */ + + return true; +} + +extern uint8_t* GetModelPointer(); +const uint8_t* arm::app::Wav2LetterModel::ModelPointer() +{ + return GetModelPointer(); +} + +extern size_t GetModelLen(); +size_t arm::app::Wav2LetterModel::ModelSize() +{ + return GetModelLen(); +}
\ No newline at end of file diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc new file mode 100644 index 0000000..60ee51e --- /dev/null +++ b/source/use_case/asr/src/Wav2LetterPostprocess.cc @@ -0,0 +1,172 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "Wav2LetterPostprocess.hpp" + +#include "Wav2LetterModel.hpp" + + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + Postprocess::Postprocess(const uint32_t contextLen, + const uint32_t innerLen, + const uint32_t blankTokenIdx) + : _m_contextLen(contextLen), + _m_innerLen(innerLen), + _m_totalLen(2 * this->_m_contextLen + this->_m_innerLen), + _m_countIterations(0), + _m_blankTokenIdx(blankTokenIdx) + {} + + bool Postprocess::Invoke(TfLiteTensor* tensor, + const uint32_t axisIdx, + const bool lastIteration) + { + /* Basic checks. */ + if (!this->_IsInputValid(tensor, axisIdx)) { + return false; + } + + /* Irrespective of tensor type, we use unsigned "byte" */ + uint8_t* ptrData = tflite::GetTensorData<uint8_t>(tensor); + const uint32_t elemSz = this->_GetTensorElementSize(tensor); + + /* Other sanity checks. */ + if (0 == elemSz) { + printf_err("Tensor type not supported for post processing\n"); + return false; + } else if (elemSz * this->_m_totalLen > tensor->bytes) { + printf_err("Insufficient number of tensor bytes\n"); + return false; + } + + /* Which axis do we need to process? */ + switch (axisIdx) { + case arm::app::Wav2LetterModel::ms_outputRowsIdx: + return this->_EraseSectionsRowWise(ptrData, + elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx], + lastIteration); + case arm::app::Wav2LetterModel::ms_outputColsIdx: + return this->_EraseSectionsColWise(ptrData, + elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx], + lastIteration); + default: + printf_err("Unsupported axis index: %u\n", axisIdx); + } + + return false; + } + + bool Postprocess::_IsInputValid(TfLiteTensor* tensor, + const uint32_t axisIdx) const + { + if (nullptr == tensor) { + return false; + } + + if (static_cast<int>(axisIdx) >= tensor->dims->size) { + printf_err("Invalid axis index: %u; Max: %d\n", + axisIdx, tensor->dims->size); + return false; + } + + if (static_cast<int>(this->_m_totalLen) != + tensor->dims->data[axisIdx]) { + printf_err("Unexpected tensor dimension for axis %d, \n", + tensor->dims->data[axisIdx]); + return false; + } + + return true; + } + + uint32_t Postprocess::_GetTensorElementSize(TfLiteTensor* tensor) + { + switch(tensor->type) { + case kTfLiteUInt8: + return 1; + case kTfLiteInt8: + return 1; + case kTfLiteInt16: + return 2; + case kTfLiteInt32: + return 4; + case kTfLiteFloat32: + return 4; + default: + printf_err("Unsupported tensor type %s\n", + TfLiteTypeGetName(tensor->type)); + } + + return 0; + } + + bool Postprocess::_EraseSectionsRowWise( + uint8_t* ptrData, + const uint32_t strideSzBytes, + const bool lastIteration) + { + /* In this case, the "zero-ing" is quite simple as the region + * to be zeroed sits in contiguous memory (row-major). */ + const uint32_t eraseLen = strideSzBytes * this->_m_contextLen; + + /* Erase left context? */ + if (this->_m_countIterations > 0) { + /* Set output of each classification window to the blank token. */ + std::memset(ptrData, 0, eraseLen); + for (size_t windowIdx = 0; windowIdx < this->_m_contextLen; windowIdx++) { + ptrData[windowIdx*strideSzBytes + this->_m_blankTokenIdx] = 1; + } + } + + /* Erase right context? */ + if (false == lastIteration) { + uint8_t * rightCtxPtr = ptrData + (strideSzBytes * (this->_m_contextLen + this->_m_innerLen)); + /* Set output of each classification window to the blank token. */ + std::memset(rightCtxPtr, 0, eraseLen); + for (size_t windowIdx = 0; windowIdx < this->_m_contextLen; windowIdx++) { + rightCtxPtr[windowIdx*strideSzBytes + this->_m_blankTokenIdx] = 1; + } + } + + if (lastIteration) { + this->_m_countIterations = 0; + } else { + ++this->_m_countIterations; + } + + return true; + } + + bool Postprocess::_EraseSectionsColWise( + uint8_t* ptrData, + const uint32_t strideSzBytes, + const bool lastIteration) + { + /* Not implemented. */ + UNUSED(ptrData); + UNUSED(strideSzBytes); + UNUSED(lastIteration); + return false; + } + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc new file mode 100644 index 0000000..e46cca3 --- /dev/null +++ b/source/use_case/asr/src/Wav2LetterPreprocess.cc @@ -0,0 +1,228 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "Wav2LetterPreprocess.hpp" + +#include "PlatformMath.hpp" +#include "TensorFlowLiteMicro.hpp" + +#include <algorithm> +#include <cmath> + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + Preprocess::Preprocess( + const uint32_t numMfccFeatures, + const uint32_t windowLen, + const uint32_t windowStride, + const uint32_t numMfccVectors): + _m_mfcc(numMfccFeatures, windowLen), + _m_mfccBuf(numMfccFeatures, numMfccVectors), + _m_delta1Buf(numMfccFeatures, numMfccVectors), + _m_delta2Buf(numMfccFeatures, numMfccVectors), + _m_windowLen(windowLen), + _m_windowStride(windowStride), + _m_numMfccFeats(numMfccFeatures), + _m_numFeatVectors(numMfccVectors), + _m_window() + { + if (numMfccFeatures > 0 && windowLen > 0) { + this->_m_mfcc.Init(); + } + } + + bool Preprocess::Invoke( + const int16_t* audioData, + const uint32_t audioDataLen, + TfLiteTensor* tensor) + { + this->_m_window = SlidingWindow<const int16_t>( + audioData, audioDataLen, + this->_m_windowLen, this->_m_windowStride); + + uint32_t mfccBufIdx = 0; + + std::fill(_m_mfccBuf.begin(), _m_mfccBuf.end(), 0.f); + std::fill(_m_delta1Buf.begin(), _m_delta1Buf.end(), 0.f); + std::fill(_m_delta2Buf.begin(), _m_delta2Buf.end(), 0.f); + + /* While we can slide over the window. */ + while (this->_m_window.HasNext()) { + const int16_t* mfccWindow = this->_m_window.Next(); + auto mfccAudioData = std::vector<int16_t>( + mfccWindow, + mfccWindow + this->_m_windowLen); + auto mfcc = this->_m_mfcc.MfccCompute(mfccAudioData); + for (size_t i = 0; i < this->_m_mfccBuf.size(0); ++i) { + this->_m_mfccBuf(i, mfccBufIdx) = mfcc[i]; + } + ++mfccBufIdx; + } + + /* Pad MFCC if needed by adding MFCC for zeros. */ + if (mfccBufIdx != this->_m_numFeatVectors) { + std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->_m_windowLen, 0); + std::vector<float> mfccZeros = this->_m_mfcc.MfccCompute(zerosWindow); + + while (mfccBufIdx != this->_m_numFeatVectors) { + memcpy(&this->_m_mfccBuf(0, mfccBufIdx), + mfccZeros.data(), sizeof(float) * _m_numMfccFeats); + ++mfccBufIdx; + } + } + + /* Compute first and second order deltas from MFCCs. */ + this->_ComputeDeltas(this->_m_mfccBuf, + this->_m_delta1Buf, + this->_m_delta2Buf); + + /* Normalise. */ + this->_Normalise(); + + /* Quantise. */ + QuantParams quantParams = GetTensorQuantParams(tensor); + + if (0 == quantParams.scale) { + printf_err("Quantisation scale can't be 0\n"); + return false; + } + + switch(tensor->type) { + case kTfLiteUInt8: + return this->_Quantise<uint8_t>( + tflite::GetTensorData<uint8_t>(tensor), tensor->bytes, + quantParams.scale, quantParams.offset); + case kTfLiteInt8: + return this->_Quantise<int8_t>( + tflite::GetTensorData<int8_t>(tensor), tensor->bytes, + quantParams.scale, quantParams.offset); + default: + printf_err("Unsupported tensor type %s\n", + TfLiteTypeGetName(tensor->type)); + } + + return false; + } + + bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc, + Array2d<float>& delta1, + Array2d<float>& delta2) + { + const std::vector <float> delta1Coeffs = + {6.66666667e-02, 5.00000000e-02, 3.33333333e-02, + 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, + -3.33333333e-02, -5.00000000e-02, -6.66666667e-02}; + + const std::vector <float> delta2Coeffs = + {0.06060606, 0.01515152, -0.01731602, + -0.03679654, -0.04329004, -0.03679654, + -0.01731602, 0.01515152, 0.06060606}; + + if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) || + mfcc.size(0) == 0 || mfcc.size(1) == 0) { + return false; + } + + /* Get the middle index; coeff vec len should always be odd. */ + const size_t coeffLen = delta1Coeffs.size(); + const size_t fMidIdx = (coeffLen - 1)/2; + const size_t numFeatures = mfcc.size(0); + const size_t numFeatVectors = mfcc.size(1); + + /* Iterate through features in MFCC vector. */ + for (size_t i = 0; i < numFeatures; ++i) { + /* For each feature, iterate through time (t) samples representing feature evolution and + * calculate d/dt and d^2/dt^2, using 1D convolution with differential kernels. + * Convolution padding = valid, result size is `time length - kernel length + 1`. + * The result is padded with 0 from both sides to match the size of initial time samples data. + * + * For the small filter, conv1D implementation as a simple loop is efficient enough. + * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32. + */ + + for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j) { + float d1 = 0; + float d2 = 0; + const size_t mfccStIdx = j - fMidIdx; + + for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m) { + + d1 += mfcc(i,mfccStIdx + k) * delta1Coeffs[m]; + d2 += mfcc(i,mfccStIdx + k) * delta2Coeffs[m]; + } + + delta1(i,j) = d1; + delta2(i,j) = d2; + } + } + + return true; + } + + float Preprocess::_GetMean(Array2d<float>& vec) + { + return math::MathUtils::MeanF32(vec.begin(), vec.totalSize()); + } + + float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean) + { + return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean); + } + + void Preprocess::_NormaliseVec(Array2d<float>& vec) + { + auto mean = Preprocess::_GetMean(vec); + auto stddev = Preprocess::_GetStdDev(vec, mean); + + debug("Mean: %f, Stddev: %f\n", mean, stddev); + if (stddev == 0) { + std::fill(vec.begin(), vec.end(), 0); + } else { + const float stddevInv = 1.f/stddev; + const float normalisedMean = mean/stddev; + + auto NormalisingFunction = [=](float& value) { + value = value * stddevInv - normalisedMean; + }; + std::for_each(vec.begin(), vec.end(), NormalisingFunction); + } + } + + void Preprocess::_Normalise() + { + Preprocess::_NormaliseVec(this->_m_mfccBuf); + Preprocess::_NormaliseVec(this->_m_delta1Buf); + Preprocess::_NormaliseVec(this->_m_delta2Buf); + } + + float Preprocess::_GetQuantElem( + const float elem, + const float quantScale, + const int quantOffset, + const float minVal, + const float maxVal) + { + float val = std::round((elem/quantScale) + quantOffset); + return std::min<float>(std::max<float>(val, minVal), maxVal); + } + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/asr/usecase.cmake b/source/use_case/asr/usecase.cmake new file mode 100644 index 0000000..e4b8752 --- /dev/null +++ b/source/use_case/asr/usecase.cmake @@ -0,0 +1,164 @@ +#---------------------------------------------------------------------------- +# Copyright (c) 2021 Arm Limited. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +#---------------------------------------------------------------------------- + +# If the path to a directory or source file has been defined, +# get the type here (FILEPATH or PATH): +if (DEFINED ${use_case}_FILE_PATH) + get_path_type(${${use_case}_FILE_PATH} PATH_TYPE) + + # Set the default type if path is not a dir or file path (or undefined) + if (NOT ${PATH_TYPE} STREQUAL PATH AND NOT ${PATH_TYPE} STREQUAL FILEPATH) + message(FATAL_ERROR "Invalid ${use_case}_FILE_PATH. It should be a dir or file path.") + endif() +else() + # Default is a directory path + set(PATH_TYPE PATH) +endif() + +message(STATUS "${use_case}_FILE_PATH is of type: ${PATH_TYPE}") + +USER_OPTION(${use_case}_FILE_PATH "Directory with custom WAV input files, or path to a single WAV file, to use in the evaluation application." + ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/ + ${PATH_TYPE}) + +USER_OPTION(${use_case}_LABELS_TXT_FILE "Labels' txt file for the chosen model." + ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/labels/labels_wav2letter.txt + FILEPATH) + +USER_OPTION(${use_case}_AUDIO_RATE "Specify the target sampling rate. Default is 16000." + 16000 + STRING) + +USER_OPTION(${use_case}_AUDIO_MONO "Specify if the audio needs to be converted to mono. Default is ON." + ON + BOOL) + +USER_OPTION(${use_case}_AUDIO_OFFSET "Specify the offset to start reading after this time (in seconds). Default is 0." + 0 + STRING) + +USER_OPTION(${use_case}_AUDIO_DURATION "Specify the audio duration to load (in seconds). If set to 0 the entire audio will be processed." + 0 + STRING) + +USER_OPTION(${use_case}_AUDIO_RES_TYPE "Specify re-sampling algorithm to use. By default is 'kaiser_best'." + kaiser_best + STRING) + +USER_OPTION(${use_case}_AUDIO_MIN_SAMPLES "Specify the minimum number of samples to use. By default is 16000, if the audio is shorter will be automatically padded." + 16000 + STRING) + +USER_OPTION(${use_case}_MODEL_SCORE_THRESHOLD "Specify the score threshold [0.0, 1.0) that must be applied to the inference results for a label to be deemed valid." + 0.5 + STRING) + +# Generate input files +generate_audio_code(${${use_case}_FILE_PATH} ${SRC_GEN_DIR} ${INC_GEN_DIR} + ${${use_case}_AUDIO_RATE} + ${${use_case}_AUDIO_MONO} + ${${use_case}_AUDIO_OFFSET} + ${${use_case}_AUDIO_DURATION} + ${${use_case}_AUDIO_RES_TYPE} + ${${use_case}_AUDIO_MIN_SAMPLES}) + +# Generate labels file +set(${use_case}_LABELS_CPP_FILE Labels) +generate_labels_code( + INPUT "${${use_case}_LABELS_TXT_FILE}" + DESTINATION_SRC ${SRC_GEN_DIR} + DESTINATION_HDR ${INC_GEN_DIR} + OUTPUT_FILENAME "${${use_case}_LABELS_CPP_FILE}" +) + + +USER_OPTION(${use_case}_ACTIVATION_BUF_SZ "Activation buffer size for the chosen model" + 0x00200000 + STRING) + + +# If there is no tflite file pointed to +if (NOT DEFINED ${use_case}_MODEL_TFLITE_PATH) + + set(MODEL_FILENAME wav2letter_int8.tflite) + set(MODEL_RESOURCES_DIR ${DOWNLOAD_DEP_DIR}/${use_case}) + file(MAKE_DIRECTORY ${MODEL_RESOURCES_DIR}) + set(DEFAULT_MODEL_PATH ${MODEL_RESOURCES_DIR}/${MODEL_FILENAME}) + + # Download the default model + set(ZOO_COMMON_SUBPATH "models/speech_recognition/wav2letter/tflite_int8") + set(ZOO_MODEL_SUBPATH "${ZOO_COMMON_SUBPATH}/${MODEL_FILENAME}") + + download_file_from_modelzoo(${ZOO_MODEL_SUBPATH} ${DEFAULT_MODEL_PATH}) + + if (ETHOS_U55_ENABLED) + message(STATUS + "Ethos-U55 is enabled, but the model downloaded is not optimized by vela. " + "To use Ethos-U55 acceleration, optimise the downloaded model and pass it " + "as ${use_case}_MODEL_TFLITE_PATH to the CMake configuration.") + endif() + + # If the target platform is native + if (${TARGET_PLATFORM} STREQUAL native) + + # Download test vectors + set(ZOO_TEST_IFM_SUBPATH "${ZOO_COMMON_SUBPATH}/testing_input/input_2_int8/0.npy") + set(ZOO_TEST_OFM_SUBPATH "${ZOO_COMMON_SUBPATH}/testing_output/Identity_int8/0.npy") + + set(${use_case}_TEST_IFM ${MODEL_RESOURCES_DIR}/ifm0.npy CACHE FILEPATH + "Input test vector for ${use_case}") + set(${use_case}_TEST_OFM ${MODEL_RESOURCES_DIR}/ofm0.npy CACHE FILEPATH + "Input test vector for ${use_case}") + + download_file_from_modelzoo(${ZOO_TEST_IFM_SUBPATH} ${${use_case}_TEST_IFM}) + download_file_from_modelzoo(${ZOO_TEST_OFM_SUBPATH} ${${use_case}_TEST_OFM}) + + set(TEST_SRC_GEN_DIR ${CMAKE_BINARY_DIR}/generated/${use_case}/tests/src) + set(TEST_INC_GEN_DIR ${CMAKE_BINARY_DIR}/generated/${use_case}/tests/include) + file(MAKE_DIRECTORY ${TEST_SRC_GEN_DIR} ${TEST_INC_GEN_DIR}) + + # Generate test data files to be included in x86 tests + generate_test_data_code( + INPUT_DIR "${DOWNLOAD_DEP_DIR}/${use_case}" + DESTINATION_SRC ${TEST_SRC_GEN_DIR} + DESTINATION_HDR ${TEST_INC_GEN_DIR} + USECASE "${use_case}") + endif() + +else() + set(DEFAULT_MODEL_PATH "N/A") +endif() + +set(EXTRA_MODEL_CODE + "/* Model parameters for ${use_case} */" + "extern const int g_FrameLength = 512" + "extern const int g_FrameStride = 160" + "extern const int g_ctxLen = 98" + "extern const float g_ScoreThreshold = ${${use_case}_MODEL_SCORE_THRESHOLD}" + ) + +USER_OPTION(${use_case}_MODEL_TFLITE_PATH "NN models file to be used in the evaluation application. Model files must be in tflite format." + ${DEFAULT_MODEL_PATH} + FILEPATH + ) + +# Generate model file +generate_tflite_code( + MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH} + DESTINATION ${SRC_GEN_DIR} + EXPRESSIONS ${EXTRA_MODEL_CODE} + ) |