MJ Windows Faster Whisper

Local speech-to-text with the faster-whisper backend (CTranslate2). Use when transcribing audio locally, setting up the faster-whisper model cache, or replacing a whisper-cli workflow with a faster local engine.

Faster Whisper

Overview

Use faster-whisper for local transcription with low latency and a reusable model cache.

Rules

• Do not assume ggml models work here; faster-whisper uses CTranslate2 model folders.
• Prefer CPU device='cpu' and compute_type='int8' unless the machine is explicitly configured for GPU.
• Keep output plain text unless the user asks for timestamps or captions.

Setup

1. Confirm python and ffmpeg are available.
2. Install the Python packages needed for local inference:
   • faster-whisper
   • ctranslate2
   • huggingface_hub
3. Use the project repo https://github.com/SYSTRAN/faster-whisper for install/setup guidance.
4. Download Systran/faster-whisper-small from https://huggingface.co/Systran/faster-whisper-small into a stable local folder such as:
   • C:\Users\joshu\.openclaw\tools\faster-whisper\models\Systran-faster-whisper-small
5. Reuse that folder for repeat runs.
6. If the user only has a ggml-*.bin file, explain that it belongs to whisper.cpp and is not usable here.

Transcription

1. Convert Telegram OGG/Opus audio to WAV if needed.
2. Load the local model folder.
3. Transcribe and return the plain-text result.