0.20.0

Updates

• Adds support for Python3.11 and Python 3.12
• Removes support for Python3.7 and Python3.8
• Adds support for Tensorflow 2.16
• Adds support for GCC 13
• Adds support for BRD2705 platform
• Adds support for GSDK 4.3.3
• Adds support for latest Tensorflow-Lite Micro as of March 2024

0.19.0

Updates

• Added support for Tensorflow-2.13
• Added support for new embedded platform: BRD4401

0.18.0

New Datasets

• On/Off - Synthetically generated keywords "on" and "off"
• Yes/No - Synthetically generated keywords "yes" and "no"
• Ten Digits - Synthetically generated keywords "zero" through "ninie"

New Tutorials

• Model Quantization Tips - Provides tips on how to gain better quantization for your model
• Quantized LSTM - Describes how to create a quantized keyword spotting model with an LSTM layer

New Models

• Keyword Spotting - Numbers - CNN+LSTM keyword spotting model used to detect the keywords "zero" through "nine"

New Examples

• Audio Feature Generator Example - Shows how to manually process an audio sample in the Audio Feature Generator and run inference using TF-Lite and TF-Lite Micro

New Feature

• Added support for generate_quantization_report to TfliteConverter settings. This allows for generating a quantization report

Bug Fixes

• Fixed issue with loading models with float32 inputs into audio_classifier app
• Fixed issue with profiling Conv1D models

0.17.0

Updates

• Updated to Gecko SDK 4.3.0
• Updated to latest Tensorflow-Lite Micro
• Added new dataset: on_off - Contains synthetically generated samples of the keywords: "On" and "Off"
• Added new dataset: mit_ir_survey - Used for applying an "impulse response" to training samples
• Added new model: keyword_spotting_on_off_v3 - A more robust model for detecting the keywords: "On" and "Off"
• Updated tutorial: Keyword Spotting On/Off - Uses v3 model

0.16.0

Fixes/Improvements

• Fixed issue with model profiler not returning latency estimate when using the simulator
• Updated TfliteModel.predict() API to support models with multiple inputs and outputs
• Fixed issue with max_samples_per_class argument not properly updating class_counts dictionary in list_dataset_directory

0.15.0

Tutorials

• Cloud Logging with Weights & Biases - New tutorial for logging model development information to the cloud
• Keyword Spotting Pac-Man - Updated tutorial for usage with keyword_spotting_pacman_v3 model

Models

• Keyword Spotting Pac-Man v3 - New model that is more robust against background noise

Other Updates

• Updated the Gecko SDK to version 4.2.1
• Updated Tensorflow-Lite Micro to latest version as of February 2023
• Added more reference datasets

0.14.0

New Tutorials

• Synthetic Audio Dataset Generation - describes how to use Text-to-Speech (TTS) of various clouds to generate synthetic keyword audio datasets
• Keyword Spotting - Alexa - demonstrates how to use an embedded development board as the audio source/sink for the Alexa Voice Services backend

New Model

• Keyword Spotting - Alexa - this model is designed to detect the keyword: “Alexa”

New Python APIs

• Audio Dataset Generator - allows for generating a synthetic keyword audio datasets
• UART Stream - allows for streaming binary data between a Python script and embedded device via UART

0.13.0

General Updates / Improvements

• Updated to latest Tensorflowflow-Lite Micro as of November 2022
• Updated to Gecko SDK 4.1.3
• Removed dynamic installation of optional Python packages. Optional packages may now be installed with: pip install silabs-mltk[full]
• Updated TfliteModel APIs to provide additional calculated parameters used by Tensorflowflow-Lite Micro
• Added progress bar to evaluation CLI

Model / Dataset Updates

• Updated keyword_spotting_on_off_v2 to be more robust
• Added keyword_spotting_pacman_v2 to be more robust
• Updated Google Speech Commands to optionally remove invalid samples

0.12.0

New Features/Improvements

• Added support for Python 3.10
• Added new command: mltk tensorboard which invokes Tensorboard utility
• Added better support for Tensorflow Dataset API
• Updated ParallelImageDataGenerator and ParallelAudioDataGenerator to more efficiently process data which improves training latency
• Add new data preprocessing utilities
• Updated reference models to support running directly from python, e.g.: python basic_example.py

New Tutorials

• Model Debugging - This tutorial demonstrates how to debug a model specification Python script during model training. This allows for single-step debugging while the model is being actively trained.
• Add an Existing Script to the MLTK - Shows how to modify an existing model training script to work with the MLTK

New Examples

• TF-Lite Model API Example - Demonstrates how to use the TF-Lite Model package
• TF-Lite Micro Model API Example - Demonstrates how to use the TF-Lite Micro Model package.

New Models

• basic_example - This provides a basic example of how to create a model specification. It is based off the Simple MNIST convnet Keras example.
• basic_tf_dataset_example - This provides a basic example of how to create a model specification using the Tensorflow Dataset API for dataset management.
• image_tf_dataset - This provides an example of how to use the Tensorflow Dataset API with the various Tensorflow image augmentations to augment images during model training.
• audio_tf_dataset - This provides an example of how to use the Tensorflow Dataset API with the third-party Python library audiomentations to augment audio during model training.

0.11.0

Bug Fixes / Improvements

• Fixed install error by removing direct dependency on the netron Python package
  Netron is now only installed when using the view_model API
• Only automatically run .tflite model evaluation after training if the model specification supports .tflite model evaluation
• Add better support for generating stack tracing during assertions triggered in Windows/Linux apps