pyannote-audio VS whisper

pyannote/pyannote-audio

Open Source. I've found this to be pretty nice, which is just a wrapper on some hugging face models https://github.com/pyannote/pyannote-audio

(not the creator, but I've built something similar for personal use)

This is a great library for determining which speaker is speaking during each time in an audio file (this is called speaker diarization); I imagine they used it or something like it. Works really well out of the box!

https://github.com/pyannote/pyannote-audio

Do you think pyannote could be implemented in the Pro version of the app to support diarization?

Free startup idea: Use Whisper with pyannote-audio[0]’s speaker diarization. Upload a recording, get back a multi-speaker annotated transcription.

Make a JSON API and I’ll be your first customer.

[0] https://github.com/pyannote/pyannote-audio

Whisper can’t, but pyannote-audio can. I’ve seen a couple of prototypes out there which link the two together.

You can use pyannote python library. It will identify different speakers from audio and will create small audio files with those speakers.

This Colab notebook is basically a standard template (with small changes) provided by pyannote-audio, the library implementing the speaker diarization functionality we need. (template)

Let’s not talk about local models as the hardware requirements are way beyond most of these people’s reach. I have a MacBook Air with an M2 chip and 8GB of RAM and can hardly run Whisper locally, so I use this HuggingFace space.

Last week, I launched notesGPT, a free and open source voice note app that has 35,000 visitors, 7,000 users, and over 1,000 GitHub stars so far in the last week. It allows you to record a voice note, transcribes it uses Whisper, and uses Mixtral via Together to extract action items and display them in an action items view. It’s also fully open source and comes equipped with authentication, storage, vector search, action items, and is fully responsive on mobile for ease of use.

I found a pretty good discussion in the topic here:

https://github.com/openai/whisper/discussions/1243

There is a plot of language performance on their repo: https://github.com/openai/whisper

I am not aware of a multi-lingual leaderboard for speech recognition models.

I don't know how OP does it, but here's how I'd do it:

* Generate a transcript by runing Whisper against the podcast audio file: https://github.com/openai/whisper

* Upload transcript to ChatGPT and ask it to summarize.

* Automate all the above.

Ahh, that makes sense. I've been building something like that, but only from other languages into English using Whisper

Whisper already generates subtitles[0], supporting VTT and SRT so this is just a thin wrapper around that.

[0]: https://github.com/openai/whisper/blob/e58f28804528831904c3b...

> although it does require you to wear headphones so the bot doesn't hear itself and get interrupted.

Maybe you can rely on some sort of speaker identification to sort this out?

https://github.com/openai/whisper/discussions/264