[vessenes]

I'm curious - what made you choose deepgram over just running whisper? I don't have any experience with deepgram but whisper has worked so well in my own tests that I didn't even ever consider there might be API speech recognition-only companies.

[ty00001]

There's three fantastic niche players in the speech-to-text market right now that you should check out: - Deepgram (cheap and dirty, but accuracy quite poor) - Speechmatics (a bit more pricey, but fantastic accuracy) - Assembly AI (just announced Series C funding of $50m)

[iAkashPaul]

Exactly, I don't think this project uses VAD for pausing LLM generation or interruptions in general which is key to good assistant interactions

[Alyx1337]

Deepgram advertised itself as being the fastest, and I wanted to focus on limiting response delay so I chose it. I hope I did not get misled.

[java_beyb]

well, deepgram might be the fastest among cloud-dependent APIs, like Speechmatics and Assembly AI mentioned above. -but- it cannot be faster than local or smaller models as you mentioned.

Among local solutions, Whisper SDK doesn't support streaming, I haven't seen any good workarounds or successfully implemented it. VOSK, DeepSpeech, Kaldi, et al were good once upon a time... Picovoice seems to be doing well.

I was planning to work on this: https://picovoice.ai/blog/chatgpt-ai-virtual-assistant-in-py... using Eleven Labs and Cheetah. Hope I can crave some time

[jkachmar]

unless i'm misunderstanding `whisper.cpp` seems to support streaming & the repository includes a native example[0] and a WASM example[1] with a demo site[2].

[0]: https://github.com/ggerganov/whisper.cpp/tree/master/example...

[1]: https://github.com/ggerganov/whisper.cpp/blob/master/example...

[2]: https://whisper.ggerganov.com/stream/

[java_beyb]

have you tried it? i mean for fun, it wouldn't hurt for sure and ggerganov is doing amazing stuff. kudos to him.

but whisper is designed to process audio files in 30-second batches if I'm not mistaken. it's been a while since whisper released, lol. These workarounds make the window smaller but it doesn't change the fact that they're workarounds. you can adjust, modify, or manipulate the model. You can't write or train it from scratch. check out the issues referring to the real-time transcription in the repo.

can you use it? yes would it perform better than Deepgram? -although it's an API and probably not the best API- I am not sure. would i use it in my money-generating application? absolutely not.

[cloudking]

Wonderful hack, the overall response latency is the only thing that hurts the UX, if you can get the response time down would be epic. Nice work.

[Alyx1337]

Thanks! There are ways to shave off the latency: hosting locally, using quantized/smaller models, streaming data instead of doing the tasks sequentially