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/*
* 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));
}
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