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Use when transcribing audio to text via the local whisper.cpp server (large-v3, CUDA) — POST an audio file, get JSON back.
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Basado en la clasificación ocupacional SOC
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| name | Whisper-Transcription |
| description | Use when transcribing audio to text via the local whisper.cpp server (large-v3, CUDA) — POST an audio file, get JSON back. |
Local speech-to-text via whisper.cpp with GPU acceleration on port 5555. Accepts WAV/MP3 and other common formats (16kHz mono WAV is optimal) as multipart POST.
curl -X POST http://localhost:5555/inference -F "file=@audio.wav"
Returns JSON {"text": ...}.
~/whisper.cpp/build/bin/whisper-server~/whisper.cpp/models/ggml-large-v3.binwhisper-server systemd service (systemctl status whisper-server, logs via journalctl -u whisper-server -f).Systemd unit at /etc/systemd/system/whisper-server.service:
[Unit]
Description=Whisper.cpp Transcription Server
After=network.target
[Service]
Type=simple
User=matt
WorkingDirectory=/home/matt/whisper.cpp/build
ExecStart=/home/matt/whisper.cpp/build/bin/whisper-server \
-m /home/matt/whisper.cpp/models/ggml-large-v3.bin \
-l en \
--port 5555 \
--host 0.0.0.0 \
--threads 4
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target
~2–4x real-time; the first request after startup is slow (model load). large-v3 uses ≈ 6GB VRAM on the shared RTX 3090 — on OOM, wait for other GPU services to unload (see the Vram-GPU-OOM skill for the retry/signaling pattern).