Keyword Spotting Code Sample


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

Use-case code could be found in the following directory: 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

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.


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.


See 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_U_NPU_ENABLED must be aligned with the chosen model. In other words:

    • If ETHOS_U_NPU_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_U_NPU_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.

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.

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

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:

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:

cmake .. \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \

For further information, please refer to:

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:

make -j4

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

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

 ├── ethos-u-kws.axf
 ├── ethos-u-kws.htm
 └── 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.

  • 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:

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:

cmake .. \
    -Dkws_FILE_PATH=/tmp/custom_wavs/ \

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:

-- User option kws_FILE_PATH is set to /tmp/custom_wavs
-- Generating audio files from /tmp/custom_wavs
++ Converting my_clip.wav to
++ Generating build/generated/kws/include/AudioClips.hpp
++ Generating build/generated/kws/src/
-- 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-U, ensure that your custom model has been successfully run through the Vela compiler before continuing.

For further information: Optimize 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_FILEto the location of the associated labels file.

For example:

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

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:

-- 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\
-- Generating labels file from <path/to/labels_custom_model.txt>
-- writing to <path/to/build/generated/src/>

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

Setting up and running Ethos-U NPU code sample

Setting up the Ethos-U NPU Fast Model

The FVP is available publicly from Arm Ecosystem FVP downloads.

For the Ethos-U evaluation, please download the MPS3 based version of the Arm® Corstone™-300 model that contains Cortex-M55 and offers a choice of the Ethos-U55 and Ethos-U65 processors.

To install the FVP:

  • Unpack the archive.

  • Run the install script in the extracted package:

  • 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:


A log output appears on the terminal:

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:

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


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:

    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:

    [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:

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 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-U 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-U 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-U 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.