The project goals include expanding language support, reducing inference time, and capturing a wider range of speech nuances. The model architecture uses a simple two-layer classification head on top of the Wav2Vec2-BERT model, with ongoing experiments to improve its performance. The article also outlines the training process, data collection, and future plans for expanding the dataset and experimenting with model architecture. Contributors are encouraged to help with language support, data collection, architecture experiments, and optimization efforts to enhance the model's capabilities and performance.
Key takeaways:
- The Smart Turn Detection model is an open-source, community-driven project aimed at improving conversational voice AI by closely matching human expectations for turn detection.
- The current model is a proof-of-concept that supports only English and is based on Meta AI's Wav2Vec2-BERT backbone, with a focus on pause filler words for training data.
- Future goals include supporting multiple languages, reducing inference time, and expanding the range of speech nuances captured in training data.
- Contributors are encouraged to experiment with model architecture, collect and contribute data, and optimize the model for various platforms.