Faster whisper srt
Skill allenphant/faster-whisper-SRT-converter/.agent/skills/faster-whisper-srt
Local audio/video to SRT subtitle converter powered by faster-whisper. Supports batch processing, 12 Whisper model sizes, real-time progress bar, and auto audio extraction from video. No API key needed.
npx -y skills add allenphant/faster-whisper-SRT-converter --skill faster-whisper-srtAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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This skill should be used when the user asks to "convert audio to srt", "generate subtitles from audio/video", "create srt from mp3/wav/m4a/flac/mp4", "transcribe audio to subtitles", "把音訊轉成字幕", "產生 SRT 字幕檔", or needs to generate SRT subtitle files from audio or video files using the faster-whisper engine with model selection and progress display.
SKILL.md
2.7 KB, as published. Nobody here has run it
Faster Whisper SRT Converter
Purpose
Convert audio and video files to SRT subtitle files using the faster-whisper speech recognition engine. Supports multiple Whisper model sizes, real-time progress display, and automatic audio extraction from video files.
When to Use
- User wants to convert audio files (MP3, WAV, M4A, FLAC, OGG, AAC, WMA) to SRT subtitles
- User wants to extract subtitles from video files (MP4, MKV, AVI, MOV, WEBM, FLV)
- User mentions "transcribe", "subtitles", "SRT", "字幕", "逐字稿"
Prerequisites
- Python 3.10+
faster-whisperandtqdmpackages (pip install -r requirements.txt)- FFmpeg (only required for video file input)
Usage
Step 1: Run the Script
Execute the conversion script with the target audio or video file:
python faster_whisper_srt.py <input_file> [--model MODEL] [--max-chars MAX_CHARS]
Step 2: Choose a Model (Optional)
Available models, from fastest to most accurate:
| Model | Size | Speed | Accuracy |
|---|---|---|---|
| tiny | ~75 MB | Fastest | Low |
| base | ~145 MB | Fast | Fair |
| small | ~490 MB | Medium | Good |
| medium | ~1.5 GB | Slow | High (default) |
| large-v3 | ~3.1 GB | Slowest | Highest |
| large-v3-turbo | ~1.6 GB | Medium | High |
Step 3: Check Output
The output SRT file will be saved in the same directory as the input file, named: <original_name>_<model>.srt
Examples
# Basic usage (default: medium model, 40 chars per line)
python faster_whisper_srt.py interview.mp3
# Use a fast model for testing
python faster_whisper_srt.py interview.mp3 --model tiny
# Use the best model for final output
python faster_whisper_srt.py interview.mp3 --model large-v3-turbo
# Shorter subtitle lines
python faster_whisper_srt.py interview.mp3 --max-chars 25
# Video file input (requires FFmpeg)
python faster_whisper_srt.py presentation.mp4 --model medium
Notes
- First-time use of a model will trigger an automatic download. Subsequent runs use the cached model.
- The script defaults to Chinese (
zh) language detection. Modify thelanguageparameter in the script for other languages. - Video processing requires FFmpeg to be installed and available in PATH.
- Audio-only files (MP3, WAV, etc.) do NOT require FFmpeg.