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:

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

Install

1. 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

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.