yt_to_srt() {
local url="$1"
local output_base="$2"
local language="${3:-en}"
yt-dlp -x --audio-format wav --postprocessor-args "-ar 16000" -o "$output_base.wav" "$url"
whisper-cli --language "$language" --model "$WHISPER_MODEL" --split-on-word --max-len 65 --output-vtt --output-file "$output_base" --file "$output_base.wav"
rm "$output_base.wav"
}
file_to_srt() {
local filepath="$1"
local language="${2:-en}"
local filename=$(basename "$filepath")
local filename_no_ext="${filename%.*}"
local output_base="$filename_no_ext"
local temp_wav="$output_base.wav"
ffmpeg -i "$filepath" -vn -acodec pcm_s16le -ar 16000 -ac 1 "$temp_wav"
whisper-cli --language "$language" --model "$WHISPER_MODEL" --split-on-word --max-len 65 --output-vtt --output-file "$output_base" --file "$temp_wav"
rm "$temp_wav"
}
#!/bin/bash
# Download whisper.cpp models from Hugging Face (runs once per machine).
set -euo pipefail
MODELS_DIR="$HOME/whisper-models"
BASE_URL="https://huggingface.co/ggerganov/whisper.cpp/resolve/main"
MODELS=("ggml-large-v3-turbo.bin" "ggml-tiny.bin")
mkdir -p "$MODELS_DIR"
for model in "${MODELS[@]}"; do
if [ ! -f "$MODELS_DIR/$model" ]; then
echo "Downloading $model..."
curl -L --progress-bar -o "$MODELS_DIR/$model" "$BASE_URL/$model"
else
echo "$model already exists, skipping."
fi
done
echo "Whisper models ready at $MODELS_DIR"I think I’m gonna go read a book.
Not only was I testing the stt part, but also, timestamps and speaker identifications. Tested on 3 different videos, local and online both.
Timestamps were precise down to sub 500-ms level even on longer 20+ minute videos. Speaker identifications worked equally well. My old M1 Air didn't hang a single bit while the transcription was going on.
---
1. Here's one from a single speaker video (https://www.youtube.com/watch?v=-X6YzlY_8tM): https://pastebin.com/vPVPNnne
2. A shorter with up to 4 different speakers and mixed, complex scene/narration changes (https://www.youtube.com/watch?v=4tASl0auPOg): https://pastebin.com/iHZZD8Qe
--
My zsh shorthand: `alias transcribe="yapsnap --timestamps --diarize "`
I guess if it encourages you to install and figure out how to use ffmpeg, yt-dlp, kroko, numpy, and onnx that's a good thing. Sometimes just knowing a thing is possible is a huge benefit.
This repo is now a good way to centralize hacks around the sure-to-come blockers those platforms will add to prevent download.
Just like uBlockOrigin was a way to centralize all the "just run this greasemonkey script" comments, I can see this getting a huge following for people who really value transcriptions.
sudo apt update && sudo apt install -y ffmpeg python3-pip python3-venv && git clone https://github.com/kouhxp/yapsnap.git && cd yapsnap && python3 -m venv ~/yapsnap-venv && source ~/yapsnap-venv/bin/activate && pip install --upgrade pip && pip install .
On a 32GB ThinkPad X13, a 21 minute YouTube video was processed by yapsnap under 2 minutes.
Very well done!
My biggest challenge is finding a proper language model that is fast enough and accurate enough since I have to caption about 600 hours of video per week and I preferably want to run all of this on a tiny server (2 cores 4 GB memory). This tool could easily do that with the kroko model but I'll have to test if the accuracy is good enough.
Also in my own scripts I'm using ffmpeg to download just the audio of the videos that I want to caption, which saves a lot of bandwith and speeds up the whole process. As far as I can see this tool doesn't do that, that would be a nice functionality to add, plus an option to turn the output into a working .srt file.
NPUs - definitely a good use case for at least part of it, there are ports of whisper that use coreML/ANE with less power and 3x speed of CPU only
yapsnap "https://www.youtube.com/watch?v=NzKJ-xO-VhE" --diarize
SPEAKER_00 [00:00]: Welcome to the show.
SPEAKER_01 [00:03]: Glad to be here, thanks for having me.
SPEAKER_00 [00:08]: Let's get started.