We are cooked๐
100% AI
PROMPT: "A professor writes out a mathematical proof for trigonometric identities on a traditional chalkboard, explaining the step he is currently on in the equation."
A practical guide to installing yt-dlp on macOS, setting up its ffmpeg dependency, and diagnosing the errors that come up most often when something stops working.
Read:
https://t.co/c5IfbS79MF
What to do when a #YouTube video has no captions โ how audio-based transcription works, when it produces accurate output, and how to get usable text from any video.
We support changing your language now and will start to roll out more languages. For now we support English and Chinese Simplified. You can change the language at Settings page.
๐จ BREAKING: Someone just made OpenAI's Whisper transcribe 2.5 hours of audio in 98 seconds. 100% OPEN SOURCE.
It runs entirely on your GPU. No API keys. No cloud. No subscription.
It's called Insanely Fast Whisper.
You drop in an audio file. One command. You come back and there's a clean, timestamped transcript waiting. Not a rough draft. Not a partial output. The entire thing. Done.
Not a wrapper.
Not a web app.
A CLI that turns your local machine into a transcription engine that makes paid services look embarrassing.
Here's what it does on its own:
โ Transcribes 150 minutes of audio in under 98 seconds using Flash Attention 2, same model, 19x faster, zero quality loss
โ Auto-detects language across dozens of languages, or translates directly into English with a single flag
โ Speaker diarization built in, knows who said what, not just what was said
โ Word-level and chunk-level timestamps so you can jump to any exact moment in any recording
โ Runs on NVIDIA GPUs and Apple Silicon Macs with zero code changes between them
โ Works on Google Colab free tier if you don't own a GPU at all
Here's how fast it actually is:
Standard Whisper large-v3 out of the box: 31 minutes to process 2.5 hours of audio. The same exact model with Flash Attention 2 and batching: 1 minute 38 seconds. Same weights. Same accuracy. One flag difference.
Here's the wildest part:
This never started as a product. It was a benchmark demo to show what Hugging Face Transformers could do. Then the community started using it for real work. Podcast transcription. Legal recordings. Research interviews. Meeting notes at scale. The team kept adding what people actually needed until a benchmark became a full CLI that nobody planned to build.
8.8K GitHub stars. 100% Open Source.
Have you wondered to see transcript for a YouTube video which originally does not have due to very old upload time or other reasons? No worries, #PandaRecord can help create the transcript for you. You can also edit the created transcript and save and download for other use.