[TheEzEzz]

Lots of work around speculative decoding, optimizing across the ASR->LLM->TTS interfaces, fine-tuning smaller models while maintaining accuracy (lots of investment here), good old fashioned engineering around managing requests to the GPU, etc. We're considering commercializing this so I can't open source just yet, but if we end up not selling it I'll definitely think about opening it up.

[7_hours_ago]

Can you at least share the stack that you're using in building this? What kind of business model are you considering in commercializing it?

[TheEzEzz]

We're design the stack to be fairly flexible. It's Python/Pytorch under the hood, with the ability to plug and play various off the shelf models. For ASR we support GCP/AssemblyAI/etc, as well as a customized self-hosted version of Whisper that is tailored for stream processing. For the LLM we support fine-tuned GPT3 models, fine-tuned Google text-bison models, or locally hosted fine-tuned Llama models (and a lot of the project goes into how to do the fine-tuning to ensure accuracy and low latency). For the TTS we support Elevenlabs/GCP/etc, and they all tie into the latency reducing approaches.