| name | speech-to-text |
| description | Transcribe audio files (OGG, MP3, WAV, M4A, FLAC, etc.) using whisper-cpp locally with Metal GPU. Use when: (1) user provides an audio file to transcribe, (2) user asks "what does this audio say?", (3) user drops a voice memo or recording.
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Speech-to-Text (Whisper)
Quick Use — Wrapper Script (Preferred)
./scripts/transcribe.sh /path/to/audio.ogg
./scripts/transcribe.sh /path/to/audio.ogg medium
Handles any audio format, converts to 16kHz WAV automatically, runs whisper-cpp with Metal GPU.
~12s for a 5-min file with small.
Direct CLI
whisper-cpp (fast, Metal GPU — preferred):
ffmpeg -i input.ogg -ar 16000 -ac 1 -c:a pcm_s16le /tmp/input.wav
whisper-cli -m ~/.local/share/whisper-cpp/models/ggml-small.bin -f /tmp/input.wav -otxt -of /tmp/output
Python whisper (slower, but accepts any format directly):
whisper /path/to/audio.ogg --model small --output_format txt
- Use
small by default — 3.3x faster than medium with nearly identical accuracy on clear speech
- Use
medium only for noisy audio, heavy accents, or when accuracy is critical
- Whisper auto-detects language. Use
--language en to force English if needed
- Supported formats: OGG, MP3, WAV, M4A, FLAC, WEBM, and more (anything ffmpeg handles)
Output Formats
--output_format txt — plain text (default choice)--output_format srt — subtitles with timestamps--output_format json — structured with word-level timestamps--output_format all — generates all formats
Key Notes
- Whisper is cold-start only — no daemon, no background RAM usage. Loads model, transcribes, exits.
small model uses ~1GB RAM while running; medium uses ~2-3GB- whisper-cpp GGML models stored at
~/.local/share/whisper-cpp/models/ggml-{size}.bin
