audio-transcriber

name

audio-transcriber

description

Transcribe audio and video files to text using Whisper (local, free, no API key needed). Use this skill whenever the user asks to transcribe, convert audio to text, get a transcript, subtitle, or caption any audio or video file. Supports mp3, wav, m4a, mp4, flac, ogg, webm, and more. Trigger for "transcribe", "transcript", "audio to text", "what does this audio say", "convert this recording", "speech to text", "subtitles", "captions", "listen to this file", or any request involving extracting text from audio or video files.

Audio Transcriber

Transcribe any audio or video file to text using Whisper — runs locally, free, no API keys.

Requirements (Already Installed)

• faster-whisper (Python package) — the transcription engine
• ffmpeg (at ~/bin/ffmpeg) — audio processing
• transcribe.py (at this skill's location) — the transcription script

How to Transcribe

Run the transcription script using Bash:

python3 ~/.claude/skills/audio-transcriber/transcribe.py "<path_to_audio_file>"

Options

Flag	Values	Default	What it does
--model	tiny, base, small, medium, large-v3	base	Larger = more accurate but slower
--output	text, srt, vtt, segments, all	text	Output format
--outfile	file path	(prints to screen)	Save to file instead of printing

Model Selection Guide

Model	Speed	Accuracy	Best for
tiny	Fastest (~10x realtime)	Good for clear speech	Quick previews, clear recordings
base	Fast (~5x realtime)	Good	Default — works well for most files
small	Moderate (~2x realtime)	Very good	Accented speech, background noise
medium	Slow (~1x realtime)	Excellent	Important transcripts, multiple speakers
large-v3	Slowest	Best available	Critical accuracy, foreign languages

First run of each model size downloads the model (~50MB-3GB). Subsequent runs are instant.

Examples

Basic transcription (print to screen):

python3 ~/.claude/skills/audio-transcriber/transcribe.py "/path/to/recording.mp3"

Save to text file:

python3 ~/.claude/skills/audio-transcriber/transcribe.py "/path/to/recording.mp3" --outfile "/path/to/transcript.txt"

Higher accuracy with medium model:

python3 ~/.claude/skills/audio-transcriber/transcribe.py "/path/to/recording.mp3" --model medium

Generate subtitles (SRT format):

python3 ~/.claude/skills/audio-transcriber/transcribe.py "/path/to/video.mp4" --output srt --outfile "/path/to/subtitles.srt"

All formats at once (text + SRT + VTT + segments):

python3 ~/.claude/skills/audio-transcriber/transcribe.py "/path/to/audio.m4a" --output all --outfile "/path/to/output.txt"

Supported File Types

Any format ffmpeg can read: .mp3, .wav, .m4a, .mp4, .flac, .ogg, .webm, .aac, .wma, .mov, .avi, .mkv, and many more.

Workflow

1. User provides an audio/video file path
2. Choose the right model — base for most files, medium or large-v3 for noisy/accented/critical content
3. Run the transcription script using Bash tool
4. Read the output — either from the Bash output or from the saved file
5. Post-process if needed — clean up, summarize, extract key points, etc.

Tips

• For long files (60+ minutes), use --model base first for a quick draft, then re-run specific sections with medium if needed
• The first run of a new model size will download it (~30 seconds to a few minutes). After that, it's cached.
• Output SRT/VTT formats if the user wants timestamps with each segment
• The segments output shows timestamped chunks — useful for finding specific moments
• If transcription quality is poor, try upgrading the model size one step
• The script auto-detects language — works with English, Spanish, French, and 90+ other languages