This article details how to set up a custom voice pipeline in Home Assistant using free self-hosted tools like Whisper and Piper, replacing cloud-based services for full control over speech-to-text and text-to-speech processing.
The series of articles by Adam Conway discusses how the author replaced cloud-based smart assistants like Alexa with a local large language model (LLM) integrated into Home Assistant, enabling more complex and private home automations.
1. **Use a Local LLM**: Set up an LLM (like Qwen) locally using tools such as Ollama and OpenWeb UI.
2. **Integrate with Home Assistant**:
- Enable Ollama integration in Home Assistant.
- Configure the IP and port of the LLM server.
- Select the desired model for use within Home Assistant.
3. **Voice Processing Tools**:
- Use **Whisper** for speech-to-text transcription.
- Use **Piper** for text-to-speech synthesis.
4. **Smart Home Automation**:
- Automate complex tasks like turning off lights and smart plugs with voice commands.
- Use data from IP cameras (via Frigate) to control external lighting based on presence.
5. **Hardware Recommendations**:
- Use Home Assistant Voice Preview speaker or DIY alternatives using ESP32 or repurposed microphones.
1. **Use a Local LLM**: Set up an LLM (like Qwen) locally using tools such as Ollama and OpenWeb UI.
2. **Integrate with Home Assistant**:
- Enable Ollama integration in Home Assistant.
- Configure the IP and port of the LLM server.
- Select the desired model for use within Home Assistant.
3. **Voice Processing Tools**:
- Use **Whisper** for speech-to-text transcription.
- Use **Piper** for text-to-speech synthesis.
4. **Smart Home Automation**:
- Automate complex tasks like turning off lights and smart plugs with voice commands.
- Use data from IP cameras (via Frigate) to control external lighting based on presence.
5. **Hardware Recommendations**:
- Use Home Assistant Voice Preview speaker or DIY alternatives using ESP32 or repurposed microphones.
Inference Snaps are generative AI models packaged for efficient performance on local hardware, automatically optimizing for CPU, GPU, or NPU.
The article discusses the growing trend of running Large Language Models (LLMs) locally on personal machines, exploring the motivations behind this shift – including privacy concerns, cost savings, and a desire for technological sovereignty – as well as the hardware and software advancements making it increasingly feasible.
A no-install needed web-GUI for Ollama. It provides a web-based interface for interacting with Ollama, offering features like markdown rendering, keyboard shortcuts, a model manager, offline/PWA support, and an optional API for accessing more powerful models.
TLDW is a tool designed to help manage and interact with media files by ingesting, transcribing, analyzing, and searching content. It supports video, audio, documents, and web articles, offering features like local LLM inference, full-text search, and chat capabilities.
