Introduction

Welcome to my weekly report documenting my journey during Google Summer of Code 2023 with the Linux Foundation! In this project, I am working on enhancing the existing speech-to-text feature of Automotive Grade Linux (AGL) by introducing a Natural Language Intent engine and implementing software daemons/controllers to execute the extracted intent. This endeavor aims to significantly improve the user experience and functionality of the speech-to-text feature in automotive environments. Throughout this report, I will share my progress, challenges faced, and achievements made as I contribute to the development of AGL and pave the way for more intuitive and intelligent voice interactions in automobiles.

Summary of the week

During this week, I made progress in reformatting old recipes to adhere to the style guidelines. I successfully uploaded the recipes to the remote machine for collaboration purposes. Additionally, I set up a Poky Mickledore build environment with meta-oe and meta-python layers to compile rust-based Python recipes. I successfully created two recipes, namely ffi-utils and gazetteer-entity-parser. However, I encountered some challenges while creating the snips-nlu-parsers and snips-nlu-utils recipes.

Tasks completed

• Reformatted old recipes to follow style guidelines.
• Uploaded recipes to the remote machine for collaboration.
• Set up a Poky Mickledore build environment for rust-based Python recipes.
• Created the following recipes:
  • ffi-utils (no longer required)
  • gazetteer-entity-parser (no longer required)

The recipes have been incorporated into the existing meta-offline-voice-agent layer.

Tasks leftover

The following tasks were leftover this week:

• Rust based recipes need to be completed.
• SciPy and Scikit-Learn recipe errors need to be resolved. (requires assistance from mentors)

Next steps

Moving forward, the following tasks will be my focus for the upcoming week:

• Resolve rust based recipe errors: Troubleshoot and debug to ensure successful creation of snips-nlu-parsers and snips-nlu-utils.
• Begin building RASA: A task that will require attention and effort to set up the necessary environment and dependencies.
• Craft Intents Dataset: This will lay the foundation for further development and training of NLU intent engine.

Conclusion

Overall, this week was productive, and I am satisfied with the progress made in achieving the goals outlined for the week. I am excited to continue my GSoC journey and further enhance the speech-to-text feature in Automotive Grade Linux.

Resources

The following resources were utilized during the week:

• Yocto class to populate SRC_URI with crates
• Migration guide for Yocto Mickledore
• Creating and adding new layers to Yocto