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I recently tried a number of options for streaming STT. Because my use case was very sensitive to latency, I ultimately went with https://deepgram.com/ - but https://github.com/ggerganov/whisper.cpp provided a great stepping stone while prototyping a streaming use case locally on a laptop.
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wow just in time, i just made https://github.com/jackdoe/emacs-chatgpt-jarvis which is chatgpt+whisper but using local whisper and chatgpt-wrapper which is a bit clunky
since i integrated chatgpt with my emacs i use it at least 20-30 times a day
i wonder if they will charge me per token if i am paying the monthly fee
I doubt it will matter if you're breaking up mid sentence if you pass in the previous as a prompt and split words. This is how Whisper does it internally.
It's not absolutely perfect, but splitting on the word boundary is one line of code with the same package in their docs: https://github.com/jiaaro/pydub/blob/master/API.markdown#sil...
25MB is also a lot. That's 30 minutes to an hour on MP3 at reasonable compression. A 2 hour movie would have three splits.
Stable diffusion + ControlNet is fire! Nothing compares to it. ControlNet allows you to have tight control over the output. https://github.com/lllyasviel/ControlNet
Yeah, might be worried about open, crowd sourced approaches like Open Assistant (https://open-assistant.io/).
`text-davinci-003` seems to offer more flexibility: You have complete freedom in providing interaction examples in the prompt (as in https://github.com/artmatsak/grace/blob/master/grace_chatbot...) and are not limited to the predefined three chat roles. But the requests being 10x cheaper means that we'll have to find ways around those limitations :)
Judging by this[0] the new structured format is immune to "injections":
[0] https://github.com/openai/openai-python/blob/main/chatml.md
Probably the embeddings API. This guide is what helped me understand the concept https://github.com/openai/openai-cookbook/blob/main/examples...
tl;dr is that you can pre-process each chunk of your database and use embeddings to quickly look up which chunk is most similar to the user's query, and then prepend that chunk to the user's query before giving it to GPT, so that GPT has the relevant context to give an answer.
Php library
https://github.com/orhanerday/open-ai#chat-as-known-as-chatg...
I just pushed an update to the `chatgpt` NPM package with support for the official ChatGPT API: https://github.com/transitive-bullshit/chatgpt-api