whisper VS TTS

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

v3 only comes in one flavor: large.

I don’t think you’re going to have a good time running the large model on a Pi of any kind.

The large models are 32x slower than the tiny models, roughly.[0]

I’m seeing people report that the Pi 4 can transcribe 30 seconds of audio in somewhere between 30 seconds and 60 seconds with the tiny model.

You can do the math… 32x = 16 minutes to 32 minutes to transcribe 30 seconds of audio with the large model. Not a good time for most people.

The Pi 5 could be 2x to 3x faster.

I should benchmark my Pi 4 sometime (or Pi 5, if it ever shows up).

[0]: https://github.com/openai/whisper/blob/main/README.md#availa...

lol this marketing technique is getting very old. https://github.com/coqui-ai/TTS is already amazing and open source.

Not sure how relevant this is but note that Coqui TTS (the realistic TTS) has already shut down

https://coqui.ai

I've used coqui.ai's TTS models[0] and library[1] to great success. I was able to get cloned voice to be rendered in about 80% of the audio clip length, and I believe you can also stream the response. Do note the model license for XTTS, it is one they wrote themselves that has some restrictions.

[0] https://huggingface.co/coqui/XTTS-v2

[1] https://github.com/coqui-ai/TTS

My only exposure to Coqui was their text to speech software. If I remember correctly the website was a commercialized service with TTS and probably some other related things. I hope the software work continues in the open.

https://github.com/coqui-ai/TTS

Coqui.ai TTS (https://github.com/coqui-ai/TTS)

"tts_models/multilingual/multi-dataset/xtts_v2" model from https://github.com/coqui-ai/TTS. It gives pretty good results and works with references, so it's pretty easy to change the voice. By the way the source code of the project is open: https://github.com/volotat/Anagnorisis but be ready, the code is pretty raw for now.

A recent update to their GitHub also has a no-code gradio ui to facilitate fine-tuning and inferencing locally. https://github.com/coqui-ai/TTS/releases/tag/v0.21.3

No API token found for 🐸Coqui Studio voices - https://coqui.ai