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/*
* SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
* 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 <limits>
#include <catch.hpp>
constexpr uint32_t numMfccFeatures = 13;
constexpr uint32_t numMfccVectors = 10;
/* Test vector output: generated using test-asr-preprocessing.py. */
int8_t expectedResult[numMfccVectors][numMfccFeatures * 3] = {
/* Feature vec 0. */
{-32, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11, /* MFCCs. */
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, /* Delta 1. */
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10}, /* Delta 2. */
/* Feature vec 1. */
{-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
/* Feature vec 2. */
{-31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -12, -12, -12,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
/* Feature vec 3. */
{-31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
/* Feature vec 4 : this should have valid delta 1 and delta 2. */
{-31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
-38, -29, -9, 1, -2, -7, -8, -8, -12, -16, -14, -5, 5,
-68, -50, -13, 5, 0, -9, -9, -8, -13, -20, -19, -3, 15},
/* Feature vec 5 : this should have valid delta 1 and delta 2. */
{-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -11, -12, -12,
-62, -45, -11, 5, 0, -8, -9, -8, -12, -19, -17, -3, 13,
-27, -22, -13, -9, -11, -12, -12, -11, -11, -13, -13, -10, -6},
/* Feature vec 6. */
{-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
/* Feature vec 7. */
{-32, 4, -9, -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
/* Feature vec 8. */
{-32, 4, -9, -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
/* Feature vec 9. */
{-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
-11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10}
};
void PopulateTestWavVector(std::vector<int16_t>& vec)
{
constexpr int int16max = std::numeric_limits<int16_t>::max();
int val = 0;
for (size_t i = 0; i < vec.size(); ++i, ++val) {
/* We want a differential filter response from both - order 1
* and 2 => Don't have a linear signal here - we use a signal
* using squares for example. Alternate sign flips might work
* just as well and will be computationally less work! */
int valsq = val * val;
if (valsq > int16max) {
val = 0;
valsq = 0;
}
vec[i] = valsq;
}
}
TEST_CASE("Preprocessing calculation INT8")
{
/* Constants. */
const uint32_t mfccWindowLen = 512;
const uint32_t mfccWindowStride = 160;
int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
const float quantScale = 0.1410219967365265;
const int quantOffset = -11;
/* Test wav memory. */
std::vector<int16_t> testWav((mfccWindowStride * numMfccVectors) +
(mfccWindowLen - mfccWindowStride)
);
/* Populate with dummy input. */
PopulateTestWavVector(testWav);
/* Allocate mem for tensor. */
std::vector<int8_t> tensorVec(dimArray[1]*dimArray[2]*dimArray[3]);
/* Initialise dimensions and the test tensor. */
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
TfLiteTensor inputTensor = tflite::testing::CreateQuantizedTensor(
tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
/* Initialise pre-processing module. */
arm::app::AsrPreProcess prep{&inputTensor,
numMfccFeatures, numMfccVectors, mfccWindowLen, mfccWindowStride};
/* Invoke pre-processing. */
REQUIRE(prep.DoPreProcess(testWav.data(), testWav.size()));
/* Wrap the tensor with a std::vector for ease. */
auto* tensorData = tflite::GetTensorData<int8_t>(&inputTensor);
std::vector <int8_t> vecResults =
std::vector<int8_t>(tensorData, tensorData + inputTensor.bytes);
/* Check sizes. */
REQUIRE(vecResults.size() == sizeof(expectedResult));
/* Check that the elements have been calculated correctly. */
for (uint32_t j = 0; j < numMfccVectors; ++j) {
for (uint32_t i = 0; i < numMfccFeatures * 3; ++i) {
size_t tensorIdx = (j * numMfccFeatures * 3) + i;
CHECK(vecResults[tensorIdx] == expectedResult[j][i]);
}
}
}
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