path: root/docs/use_cases/kws.md

# Keyword Spotting Code Sample

- [Keyword Spotting Code Sample](#keyword-spotting-code-sample)
  - [Introduction](#introduction)
    - [Preprocessing and feature extraction](#preprocessing-and-feature-extraction)
    - [Postprocessing](#postprocessing)
    - [Prerequisites](#prerequisites)
  - [Building the code sample application from sources](#building-the-code-sample-application-from-sources)
    - [Build options](#build-options)
    - [Build process](#build-process)
    - [Add custom input](#add-custom-input)
    - [Add custom model](#add-custom-model)
  - [Setting up and running Ethos-U55 code sample](#setting-up-and-running-ethos-u55-code-sample)
    - [Setting up the Ethos-U55 Fast Model](#setting-up-the-ethos-u55-fast-model)
    - [Starting Fast Model simulation](#starting-fast-model-simulation)
    - [Running Keyword Spotting](#running-keyword-spotting)

## Introduction

This document describes the process of setting up and running the Arm® *Ethos™-U55* Keyword Spotting example.

Use-case code could be found in the following directory: [source/use_case/kws](../../source/use_case/kws]).

### Preprocessing and feature extraction

The `DS-CNN` keyword spotting model that is used with the Code Samples expects audio data to be preprocessed in a
specific way before performing an inference.

Therefore, this section aims to provide an overview of the feature extraction process used.

First, the audio data is normalized to the range (`-1`, `1`).

> **Note:** Mel-Frequency Cepstral Coefficients (MFCCs) are a common feature that is extracted from audio data and can
> be used as input for machine learning tasks. Such as keyword spotting and speech recognition. For implementation
> details, please refer to: `source/application/main/include/Mfcc.hpp`

Next, a window of 640 audio samples is taken from the start of the audio clip. From these 640 samples, we calculate 10
MFCC features.

The whole window is shifted to the right by 320 audio samples and 10 new MFCC features are calculated. This process of
shifting and calculating is repeated until the end of the 16000 audio samples required to perform an inference is
reached.

In total, this is 49 windows that each have 10 MFCC features calculated for them, giving an input tensor of shape 49x10.

These extracted features are quantized and an inference is performed.

![KWS preprocessing](../media/KWS_preprocessing.png)

If the audio clip is longer than 16000 audio samples, then the initial starting position is offset by `16000/2 = 8000`
audio samples. From this new starting point, MFCC features for the next `16000` audio samples are calculated and another
inference is performed. In other words, do an inference for samples `8000-24000`.

> **Note:** Parameters of the MFCC feature extraction all depend on what was used during model training. These values
> are specific to each model.\
If you try a different keyword spotting model that uses MFCC input, then values check to see if the values need changing
to match the new model.

In addition, MFCC feature extraction methods can vary slightly with different normalization methods or scaling being
used.

### Postprocessing

After an inference is complete, the word with the highest detected probability is output to console. Providing that the
probability is larger than a threshold value. The default is set to `0.9`.

If multiple inferences are performed for an audio clip, then multiple results are output.

### Prerequisites

See [Prerequisites](../documentation.md#prerequisites)

## Building the code sample application from sources

### Build options

In addition to the already specified build option in the main documentation, the Keyword Spotting use-case adds:

- `kws_MODEL_TFLITE_PATH` - The path to the NN model file in `TFLite` format. The model is processed and then included
  into the application `axf` file. The default value points to one of the delivered set of models. Note that the
  parameters `kws_LABELS_TXT_FILE`,`TARGET_PLATFORM`, and `ETHOS_U55_ENABLED` must be aligned with the chosen model. In
  other words:
  - If `ETHOS_U55_ENABLED` is set to `On` or `1`, then the NN model is assumed to be optimized. The model naturally
    falls back to the Arm® *Cortex®-M* CPU if an unoptimized model is supplied.
  - If `ETHOS_U55_ENABLED` is set to `Off` or `0`, then the NN model is assumed to be unoptimized. Supplying an
    optimized model in this case results in a runtime error.

- `kws_FILE_PATH`: The path to the directory containing audio files, or a path to single WAV file, to be used in the
  application. The default value points to the `resources/kws/samples` folder that contains the delivered set of audio
  clips.

- `kws_LABELS_TXT_FILE`: Path to the text file of the label. The file is used to map letter class index to the text
  label. The default value points to the delivered `labels.txt` file inside the delivery package.

- `kws_AUDIO_RATE`: The input data sampling rate. Each audio file from `kws_FILE_PATH` is preprocessed during the build
  to match the NN model input requirements. The default value is `16000`.

- `kws_AUDIO_MONO`: If set to `ON`, then the audio data is converted to mono. The default value is `ON`.

- `kws_AUDIO_OFFSET`: Begins loading audio data and starts from this specified offset, defined in seconds. the default
  value is set to `0`.

- `kws_AUDIO_DURATION`: The length of the audio data to be used in the application in seconds. The default is `0`,
  meaning that the whole audio file is used.

- `kws_AUDIO_MIN_SAMPLES`: Minimum number of samples required by the network model. If the audio clip is shorter than
  this number, then it is padded with zeros. The default value is `16000`.

- `kws_MODEL_SCORE_THRESHOLD`: Threshold value that must be applied to the inference results for a label to be deemed
  valid. Goes from 0.00 to 1.0. The default is `0.9`.

- `kws_ACTIVATION_BUF_SZ`: The intermediate, or activation, buffer size reserved for the NN model. By default, it is set
  to 2MiB and is enough for most models

To **ONLY** build the automatic speech recognition example application, add `-DUSE_CASE_BUILD=kws` to the `cmake`
command line, as specified in: [Building](../documentation.md#Building).

### Build process

> **Note:** This section describes the process for configuring the build for the *MPS3: SSE-300*. To build for a
> different target platform, please refer to: [Building](../documentation.md#Building).

To build **only** the keyword spotting example, create a build directory and navigate inside, like so:

```commandline
mkdir build_kws && cd build_kws
```

On Linux, when providing only the mandatory arguments for CMake configuration, execute the following command to build
**only** the Keyword Spotting application to run on the *Ethos-U55* Fast Model:

```commandline
cmake ../ -DUSE_CASE_BUILD=kws
```

To configure a build that can be debugged using Arm DS specify the build type as `Debug` and then use the `Arm Compiler`
toolchain file:

```commandline
cmake .. \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \
    -DCMAKE_BUILD_TYPE=Debug \
    -DUSE_CASE_BUILD=kws
```

For further information, please refer to:

- [Configuring with custom TPIP dependencies](../sections/building.md#configuring-with-custom-tpip-dependencies)
- [Using Arm Compiler](../sections/building.md#using-arm-compiler)
- [Configuring the build for simple_platform](../sections/building.md#configuring-the-build-for-simple_platform)
- [Working with model debugger from Arm Fast Model Tools](../sections/building.md#working-with-model-debugger-from-arm-fastmodel-tools)

> **Note:** If re-building with changed parameters values, we recommend that you clean the build directory and re-run
> the CMake command.

If the CMake command succeeds, build the application as follows:

```commandline
make -j4
```

To see compilation and link details, add `VERBOSE=1`.

Results of the build are placed under the `build/bin` folder, like so:

```tree
bin
 ├── ethos-u-kws.axf
 ├── ethos-u-kws.htm
 ├── ethos-u-kws.map
 └── sectors
      ├── images.txt
      └── kws
           ├── ddr.bin
           └── itcm.bin
```

The `bin` folder contains the following files:

- `ethos-u-kws.axf`: The built application binary for the Keyword Spotting use-case.

- `ethos-u-kws.map`: Information from building the application. For example: The libraries used, what was optimized, and
  the location of objects.

- `ethos-u-kws.htm`: Human readable file containing the call graph of application functions.

- `sectors/kws`: Folder containing the built application. It is split into files for loading into different FPGA memory
  regions.

- `sectors/images.txt`: Tells the FPGA which memory regions to use for loading the binaries in the `sectors/..` folder.

### Add custom input

The application anomaly detection is set up to perform inferences on data found in the folder, or an individual file,
that is pointed to by the parameter `kws_FILE_PATH`.

To run the application with your own audio clips, first create a folder to hold them and then copy the custom clips into
the following folder:

```commandline
mkdir /tmp/custom_wavs

cp my_clip.wav /tmp/custom_wavs/
```

> **Note:** Clean the build directory before re-running the CMake command.

Next, when building, set `kws_FILE_PATH` to the location of the following folder:

```commandline
cmake .. \
    -Dkws_FILE_PATH=/tmp/custom_wavs/ \
    -DUSE_CASE_BUILD=kws
```

The audio flies found in the `kws_FILE_PATH` folder are picked up and automatically converted to C++ files during the
CMake configuration stage. They are then compiled into the application during the build phase for performing inference
with.

The log from the configuration stage tells you what audio directory path has been used:

```log
-- User option kws_FILE_PATH is set to /tmp/custom_wavs
-- Generating audio files from /tmp/custom_wavs
++ Converting my_clip.wav to my_clip.cc
++ Generating build/generated/kws/include/AudioClips.hpp
++ Generating build/generated/kws/src/AudioClips.cc
-- Defined build user options:
-- kws_FILE_PATH=/tmp/custom_wavs
```

After compiling, your custom inputs have now replaced the default ones in the application.

> **Note:** The CMake parameter `kws_AUDIO_MIN_SAMPLES` determines the minimum number of input samples. When building
> the application, if the size of the audio clips is less then `kws_AUDIO_MIN_SAMPLES`, then it is padded until it
> matches.

### Add custom model

The application performs inference using the model pointed to by the CMake parameter `kws_MODEL_TFLITE_PATH`.

> **Note:** If you want to run the model using an *Ethos-U55*, ensure that your custom model has been successfully run
> through the Vela compiler *before* continuing.

For further information: [Optimize model with Vela compiler](../sections/building.md#Optimize-custom-model-with-Vela-compiler).

To run the application with a custom model, you must provide a `labels_<model_name>.txt` file of labels that are
associated with the model. Each line of the file must correspond to one of the outputs in your model. Refer to the
provided `ds_cnn_labels.txt` file for an example.

Then, you must set `kws_MODEL_TFLITE_PATH` to the location of the Vela processed model file and `kws_LABELS_TXT_FILE`to
the location of the associated labels file.

For example:

```commandline
cmake .. \
    -Dkws_MODEL_TFLITE_PATH=<path/to/custom_model_after_vela.tflite> \
    -Dkws_LABELS_TXT_FILE=<path/to/labels_custom_model.txt> \
    -DUSE_CASE_BUILD=kws
```

> **Note:** Clean the build directory before re-running the CMake command.

The `.tflite` model file pointed to by `kws_MODEL_TFLITE_PATH` and labels text file pointed to by `kws_LABELS_TXT_FILE`
are converted to C++ files during the CMake configuration stage. They are then compiled into the application for
performing inference with.

The log from the configuration stage tells you what model path and labels file have been used, for example:

```log
-- User option kws_MODEL_TFLITE_PATH is set to <path/to/custom_model_after_vela.tflite>
...
-- User option kws_LABELS_TXT_FILE is set to <path/to/labels_custom_model.txt>
...
-- Using <path/to/custom_model_after_vela.tflite>
++ Converting custom_model_after_vela.tflite to\
custom_model_after_vela.tflite.cc
-- Generating labels file from <path/to/labels_custom_model.txt>
-- writing to <path/to/build/generated/src/Labels.cc>
...
```

After compiling, your custom model has now replaced the default one in the application.

## Setting up and running Ethos-U55 code sample

### Setting up the Ethos-U55 Fast Model

The FVP is available publicly from
[Arm Ecosystem FVP downloads](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps).

For the *Ethos-U55* evaluation, please download the MPS3 version of the Arm® *Corstone™-300* model that contains both
the *Ethos-U55* and *Cortex-M55*. The model is only supported on Linux-based machines.

To install the FVP:

- Unpack the archive.

- Run the install script in the extracted package:

```commandline
./FVP_Corstone_SSE-300_Ethos-U55.sh
```

- Follow the instructions to install the FVP to the required location.

### Starting Fast Model simulation

Once the building has been completed, the application binary `ethos-u-kws.axf` can be found in the `build/bin` folder.

Assuming that the install location of the FVP was set to `~/FVP_install_location`, then the simulation can be started by
using:

```commandline
~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55
./bin/mps3-sse-300/ethos-u-kws.axf
```

A log output appears on the terminal:

```log
telnetterminal0: Listening for serial connection on port 5000
telnetterminal1: Listening for serial connection on port 5001
telnetterminal2: Listening for serial connection on port 5002
telnetterminal5: Listening for serial connection on port 5003
```

This also launches a telnet window with the standard output of the sample application. It also includes error log
entries containing information about the pre-built application version, TensorFlow Lite Micro library version used, and
data types. The log also includes the input and output tensor sizes of the model compiled into the executable binary.

After the application has started, if `kws_FILE_PATH` points to a single file, or even a folder that contains a single
input file, then the inference starts immediately. If there are multiple inputs, it outputs a menu and then waits for
input from the user.

For example:

```log
User input required
Enter option number from:

1. Classify next audio clip
2. Classify audio clip at chosen index
3. Run classification on all audio clips
4. Show NN model info
5. List audio clips

Choice:

```

What the preceding choices do:

1. Classify next audio clip: Runs a single inference on the next in line.

2. Classify audio clip at chosen index: Runs inference on the chosen audio clip.

    > **Note:** Please make sure to select audio clip index within the range of supplied audio clips during application
    > build. By default, a pre-built application has four files, with indexes from `0` to `3`.

3. Run ... on all: Triggers sequential inference executions on all built-in applications.

4. Show NN model info: Prints information about the model data type, input, and output, tensor sizes:

    ```log
    INFO - uTFL version: 2.5.0
    INFO - Model info:
    INFO - Model INPUT tensors:
    INFO -  tensor type is INT8
    INFO -  tensor occupies 490 bytes with dimensions
    INFO -    0:   1
    INFO -    1:   1
    INFO -    2:  49
    INFO -    3:  10
    INFO - Quant dimension: 0
    INFO - Scale[0] = 1.107164
    INFO - ZeroPoint[0] = 95
    INFO - Model OUTPUT tensors:
    INFO -  tensor type is INT8
    INFO -  tensor occupies 12 bytes with dimensions
    INFO -    0:   1
    INFO -    1:  12
    INFO - Quant dimension: 0
    INFO - Scale[0] = 0.003906
    INFO - ZeroPoint[0] = -128
    INFO - Activation buffer (a.k.a tensor arena) size used: 72848
    INFO - Number of operators: 1
    INFO -  Operator 0: ethos-u
    ```

5. List audio clips: Prints a list of pair ... indexes. The original filenames are embedded in the application, like so:

    ```log
    [INFO] List of Files:
    [INFO] 0 => down.wav
    [INFO] 1 => right_left_up.wav
    [INFO] 2 => yes.wav
    [INFO] 3 => yes_no_go_stop.wav
    ```

### Running Keyword Spotting

Please select the first menu option to execute inference on the first file.

The following example illustrates the output for classification:

```logINFO - Running inference on audio clip 0 => down.wav
INFO - Inference 1/1
INFO - Final results:
INFO - Total number of inferences: 1
INFO - For timestamp: 0.000000 (inference #: 0); label: down, score: 0.996094; threshold: 0.900000
INFO - Profile for Inference:
INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 217385
INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 82607
INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 59608
INFO - NPU ACTIVE cycles: 680611
INFO - NPU IDLE cycles: 561
INFO - NPU total cycles: 681172
```

On most systems running Fast Model, each inference takes under 30 seconds.

The profiling section of the log shows that for this inference:

- *Ethos-U55* PMU report:

  - 681,172 total cycle: The number of NPU cycles.

  - 680,611 active cycles: The number of NPU cycles that were used for computation.

  - 561 idle cycles: The number of cycles for which the NPU was idle.

  - 217,385 AXI0 read beats: The number of AXI beats with read transactions from the AXI0 bus. AXI0 is the bus where the
    *Ethos-U55* NPU reads and writes to the computation buffers, activation buf, or tensor arenas.

  - 82,607 write cycles: The number of AXI beats with write transactions to AXI0 bus.

  - 59,608 AXI1 read beats: The number of AXI beats with read transactions from the AXI1 bus. AXI1 is the bus where the
    *Ethos-U55* NPU reads the model. So, read-only.

- For FPGA platforms, a CPU cycle count can also be enabled. However, do not use cycle counters for FVP, as the CPU
  model is not cycle-approximate or cycle-accurate.

> **Note:** The application prints the highest confidence score and the associated label from the `ds_cnn_labels.txt`
> file.