End-to-End Steps to Create a Voice-Controlled Light ML Application from Scratch#

This guide details the process of creating a voice-controlled light application using TensorFlow Lite Micro (TFLM) on an EFR32xG24 Development Kit. This example uses the keyword_spotting_on_off_v3.tflite model (recommended) for "on" and "off" keyword detection. For more information on model creation, see the MLTK tutorial.

• Hardware: EFR32xG24 Dev Kit Board (BRD2601B Rev A01)

• Software: Simplicity Studio (SiSDK 2024.12 or later)

1. Install AI/ML Extension#

1. Click Install on the top bar.

   

2. Click Manage Installed Packages.

   

3. Under SDKs, install the latest version of the AI/ML extension (available from SiSDK 2024.12 onwards).

   

2. Start a New Simplicity Project#

1. From the File menu, select New >> Silicon Labs Project Wizard.

   

2. Select the target board (EFR32xG24 Development Kit), SDK (Simplicity SDK v2024.12.0 or later), and IDE/Toolchain (e.g., GNU ARM v12.2.1). Click Next.

   

3. Choose Empty C++ Project. Click Next.

4. Give your project a name and click Finish.

   

3. Add Machine Learning Software Component#

1. Open your project file (the one with the .slcp extension).

   

2. Under Software Components, search for "aiml".

   

3. Enable the AI/ML extension by clicking Enable Extension.

   

4. Expand AI/ML >> Machine Learning >> TensorFlow. Select TensorFlow Lite Micro and click Install.

   

5. You will be prompted to select additional components:

   

   • Debug Logging: Choose Debug Logging using IO Stream (if needed) or Debug Logging Disabled. Click Install.

   • Kernels: Select MVPv1 Accelerated Kernels. Click Install.

   

4. Configure the TFLM Component#

1. Click Configure in the TensorFlow Lite Micro Software Component.

   

2. Set the Arena Size. For this example, enter "-1". This tells the system to dynamically determine the optimal arena size at runtime.

   

5. Include and Convert the Model#

1. Create a tflite directory inside your project's config directory (optional but recommended).

2. Drag and drop the keyword_spotting_on_off_v2.tflite file into the config/tflite directory (or directly into config if you skipped creating the subdirectory).

3. The framework will automatically convert the .tflite file into a C array (sl_tflite_micro_model.c in the autogen directory). The TFLM interpreter is also initialized automatically.

   

6. Profile the Model (Optional)#

Model profiling can be helpful for optimization. For advanced users who wish to analyze model performance, the MLTK Model Profiler Utility can be used. This is not strictly required for this basic example.

7. Run the Model#

1. Include TensorFlow Init API: Add the necessary code to initialize the TFLM interpreter.

2. Provide Input Data:

   • Get a pointer to the input tensor: TfLiteTensor* input = sl_tflite_micro_get_input_tensor();.

   • Load your input data (microphone audio quantized to int8) into the input tensor: input->data.int8f[0] = <input array from microphone quantized to int8>; (See the example code for audio feature generation).

3. Run Inference:

   • Invoke the interpreter: TfLiteStatus invoke_status = sl_tflite_micro_get_interpreter()->Invoke();

   • Check for errors: if (invoke_status != kTfLiteOk) { TF_LITE_REPORT_ERROR(sl_tflite_micro_get_error_reporter(), "bad input tensor parameters in model"); }

4. Read Output:

   • Get a pointer to the output tensor: TfLiteTensor* output = sl_tflite_micro_get_output_tensor();

   • Access the output data: int8_t value = output->data.int8_tf[0];

8. Implement Post-Processing#

1. Develop an Algorithm: Create an algorithm to interpret the model's output (e.g., the int8_t value) and determine whether "on" or "off" was spoken.

2. Trigger Events: Based on the post-processed output, trigger actions like controlling the LED. Refer to the voice_control_light.cc, recognize_commands.cc, and recognize_commands.h files in the aiml-extension examples for guidance on implementing this logic, including LED control and command recognition. You will need to add components for the microphone, audio processing, and LED control to your project.