Show HN: CPU-only transcription for YouTube, TikTok, X, Instagram videos

yapsnap

Snap any video URL or audio file into plaintext. No GPU. No cloud. One command.

yapsnap "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

That's it. You get a .txt next to your shell, transcribed on your CPU, in less time than it took the video to play.

Why yapsnap

• ⚡ Fast on CPU. Streaming Zipformer transducer (Kroko English) chews through audio at several times realtime on a laptop. No CUDA. No M-series-only tricks. Plain old cores.
• 🌐 Any video URL, plus local files. YouTube. X. TikTok. Instagram Reels. Direct .mp4/.mp3 links. Or just point it at a file on disk. yt-dlp handles the fetch, ffmpeg handles the decode, the rest is yours.
• 📴 Offline after first run. ~80 MB model downloads once to your cache and stays there. No API keys. No quotas. Your audio never leaves your machine.
• 🪶 One file, three deps. sherpa-onnx, numpy, yt-dlp. The whole tool is a single Python module.
• ⏱ Sentence-level timestamps when you want them. --timestamps adds [MM:SS] per sentence using Kroko's built-in punctuation. Timing stays correct even when you transcribe at 2x.

Quickstart

# 1. ffmpeg on PATH (one-time, per OS — see below) # 2. Install pip install . # 3. Snap something yapsnap https://www.tiktok.com/@user/video/7234567890123456789 yapsnap meeting.mp4 --timestamps yapsnap podcast.mp3 -o ~/notes/episode.txt

The first run downloads the model (~80 MB). Every run after is offline.

What it handles

Any URL yt-dlp understands works. The big ones:

Source	Example
YouTube	https://www.youtube.com/watch?v=...
YouTube Shorts	https://www.youtube.com/shorts/...
X / Twitter	https://x.com/user/status/.../video/1
TikTok	https://www.tiktok.com/@user/video/...
Instagram Reels	https://www.instagram.com/reel/.../
Direct media URL	https://example.com/clip.mp4

Plus any local file ffmpeg can decode: .mp3, .mp4, .m4a, .wav, .webm, .mov, .mkv, .aac, .opus, .ogg, .flac, and friends.

Install

1. ffmpeg

OS	Command
macOS	brew install ffmpeg
Linux	sudo apt install ffmpeg or sudo dnf install ffmpeg
Windows	winget install ffmpeg or choco install ffmpeg

2. yapsnap

pip install .

Installs two equivalent commands on your PATH: yapsnap (canonical) and transcribe (alias, for when the name slips your mind).

Usage

# Local file yapsnap path/to/audio.mp3 # Any video URL yapsnap "https://www.youtube.com/watch?v=dQw4w9WgXcQ" # Sentence-level timestamps yapsnap input.mp4 --timestamps # Custom output path yapsnap input.mp4 -o ./transcripts/talk.txt # Don't speed audio up before transcribing (default is 1.5x, pitch preserved) yapsnap input.mp4 --speed 1.0 # Keep the downloaded audio (URL inputs only) yapsnap "https://..." --keep-audio

Output

Plaintext, UTF-8. Default location is ./transcripts/ (created if missing) under the current working directory; override with -o. For URL inputs the filename is derived from the video ID (dQw4w9WgXcQ_transcript.txt, etc.).

Without --timestamps — one paragraph of recognized text:

Welcome to the show. Today we're talking about transcription. Let's get started.

With --timestamps — one sentence per line, timed against the original audio:

[00:00] Welcome to the show. [00:03] Today we're talking about transcription. [00:08] Let's get started.

Timestamps stay in original-audio time even at --speed 1.5 or higher.

Flags

Flag	Description
-o, --output	Output .txt path. Default: ./transcripts/<input>_transcript.txt.
--timestamps	Emit [MM:SS] sentence. lines instead of a single paragraph.
--speed	Pre-transcription speedup factor, pitch preserved. Default 1.5.
--keep-audio	Keep the downloaded audio (URL inputs only).
--model	Override the model directory. Also reads KROKO_MODEL env var.

How it works

1. Fetch. If the input is a URL, yt-dlp grabs the best audio-only stream to a temp directory. If it's a local path, this step is skipped.
2. Decode. ffmpeg pipes the media into 16 kHz mono PCM. The optional atempo filter speeds it up without raising pitch.
3. Recognize. A streaming Zipformer2 transducer (Kroko English, INT8 ONNX, ~80 MB) eats the PCM in chunks. CPU-only. Greedy decode.
4. Format. Plain text by default. With --timestamps, token timestamps are grouped on .!? into sentences and scaled back to original-audio time.

No frame is sent anywhere. No state is kept between runs except the cached model.

Model & cache

The default Kroko English model is downloaded on first run to:

• macOS — ~/Library/Caches/yapsnap/
• Linux — $XDG_CACHE_HOME/yapsnap/ (or ~/.cache/yapsnap/)
• Windows — %LOCALAPPDATA%\yapsnap\

To use a different streaming transducer (other languages, larger Kroko variants, etc.), point --model at a directory containing encoder(.int8).onnx, decoder(.int8).onnx, joiner(.int8).onnx, and tokens.txt. Or set KROKO_MODEL in your environment.

Notes & limits

• The default model is English-only. For other languages, supply a matching sherpa-onnx streaming transducer via --model.
• --speed 1.5 shaves about a third off transcription time with minimal accuracy cost. Try 2.0 if you want it even faster, or 1.0 for noisy, mumbled, or fast-speech sources.
• Some social-media URLs are geo-locked or login-walled; yt-dlp will say so explicitly.
• This is a streaming model, so timestamps come from token positions in the recognized stream. They're accurate enough for navigation, not for subtitling-grade alignment.

License

Apache-2.0 for this project. The Kroko model is distributed under its own license — see https://huggingface.co/Banafo/Kroko-ASR. Powered by sherpa-onnx and yt-dlp.