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Honestly, there are so many Project on Github doing STT - LLM - TTS that I lost count. The only revolutionary thing that feels like magic is if the STT supports Voice Activity Detection and low latency LLM inference on Groq, so conversations feel natural.
Looks interesting! Is the latency low enough for it to feel natural? How's the Coqui speech quality?
When I gave "Matt", my loyal local assistant[1], a voice xTTSv2 performed better for long form text. While in longform emotions seemed well balanced in the text, in short replies the emotion patterns frequently felt off and therefore unnatural. What I liked about xTTsv2 though is that voice cloning is fairly easy by just providing a .wav file with the intended voice pattern. [1] https://open.substack.com/pub/jdsemrau…
Looks interesting! Is the latency low enough for it to feel natural? How's the Coqui speech quality?
It supports XTTSv2 which is currently the open-weight state of the art. So, pretty damn good ( https://huggingface.co/coqui/XTTS-v2/blob/main/samples/en_sa... ). Too bad that the project is in limbo after Coqui (the company) folded. The license limits the use of the weights to non-commercial usage unless you buy a commercial license, and there's nobody left to sell you one now.
we have made an open source orchestration which enables you to plug in your own TTS/ASR/LLM for end-to-end voice conversations at -> https://github.com/bolna-ai/bolna . We are also working on a complete open source stack for ASR+TTS+LLM and will be releasing it shortly.
Docker is a great option if you want lots of people to try out your project, but not many apps in this space come with a dockerfile
we have made an open source orchestration which enables you to plug in your own TTS/ASR/LLM for end-to-end voice conversations at -> https://github.com/bolna-ai/bolna . We are also working on a complete open source stack for ASR+TTS+LLM and will be releasing it shortly.
Have you thought about support for the wyoming protocol? That would make it pretty much plug&play with home assistant.
For others who also hadn't heard of it, here's an overview: https://github.com/rhasspy/rhasspy3/blob/master/docs/wyoming...
Earlier quoted context omitted.
It supports XTTSv2 which is currently the open-weight state of the art. So, pretty damn good ( https://huggingface.co/coqui/XTTS-v2/blob/main/samples/en_sa... ). Too bad that the project is in limbo after Coqui (the company) folded. The license limits the use of the weights to non-commercial usage unless you buy a commercial license, and there's nobody left to sell you one now.
is there anyone to sue you? (how does that work?)
These are easy to make and fun to play with and it's awesome to have everything local. But it will take more to build something truly useable. A truly natural conversational AI needs to understand the nuances of conversation, most importantly when to speak and when to wait. It also needs to know subtleties of the user's voice that no speech recognizer can output, and it needs control over the output voice more precise than any TTS provides. Audio-to-audio models in the style of GPT-4o are clearly the way forward. (And someday soon, video-to-video models for video calling with a virtual avatar. And the step after that is robotics for physical avatars).
There aren't any open source audio-to-audio models yet but there are some promising approaches. https://ultravox.ai has the input half at least. https://tincans.ai/slm has a cool approach too.