Developing a Comprehensive Home Assistant Pipeline

When it comes to smart home assistant development, various pipelines can be utilized to enhance user experience. One such framework consists of a series of steps: Wake Word Detection (WWD) -> Voice Activity Detection (VAD) -> Automatic Speech Recognition (ASR) -> Intent Classification -> Event Handler -> Text-to-Speech (TTS). For more details, you can refer to the open-source project rhasspy.

Generally, a distilbert-based intent classification neural network can handle most home assistant tasks. However, for certain advanced operations, such as chatting, running semantic search on local documents, or summarizing/analyzing a web article, implementing a local Language Model for Machine Assistance (LLaMA) could be quite beneficial.

One interesting project that incorporates similar functionality is Willow developed by Tovera Inc. It is worth checking out to understand how they have created an effective solution.

If you are looking for an API, consider using Kolboldcpp. Though connecting it to the Home Assistant might require some effort, it can certainly streamline the process of sending data.

The ideal goal is to develop an integration resembling the OpenAI conversation agent. Currently, potential solutions involve using a Python websocket server that accepts input and responds with output. While this solution seems promising, real-time implementation is hindered due to the lack of a GPU. Future developments may potentially overcome this limitation and make the system more efficient.

Tags: Home Assistant, Pipeline, LLaMA, OpenAI, Neural Network, Kolboldcpp, Tovera Inc, Willow, Python websocket server


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