whisper-transcribe-docker

name	whisper-transcribe-docker
description	Speech-to-text (逐字稿/转写) in Docker using faster-whisper (local, no API key). Use when you already have an audio file (e.g. from `media-audio-download`) and need a transcript with optional timestamps for summarization.

Whisper Transcribe (Docker, faster-whisper)

This skill turns an audio file into a transcript locally (no OpenAI key).

Use with media-audio-download:

1. Download audio -> out/*.m4a
2. Transcribe -> out/*.txt (or JSON)

Quick Start

Build image:

docker build -t moltbot-whisper-transcribe {baseDir}

Transcribe an audio file (writes plain text to stdout by default):

docker run --rm -v "$PWD:/work" -v whisper-models:/models \
  moltbot-whisper-transcribe /work/out/audio.m4a --model small

If huggingface.co is blocked/unreachable in your network, set a mirror endpoint:

docker run --rm -e HF_ENDPOINT='https://hf-mirror.com' -v "$PWD:/work" -v whisper-models:/models \
  moltbot-whisper-transcribe /work/out/audio.m4a --model small

Write transcript to a file:

docker run --rm -v "$PWD:/work" -v whisper-models:/models \
  moltbot-whisper-transcribe /work/out/audio.m4a --model small --out /work/out/audio.txt

With timestamps:

docker run --rm -v "$PWD:/work" -v whisper-models:/models \
  moltbot-whisper-transcribe /work/out/audio.m4a --model small --timestamps --out /work/out/audio.txt

Notes:

• First run downloads model weights (cached in the whisper-models Docker volume).
• For speed, start with --model tiny / --model base.
• For quality, use --model medium (CPU will be slower).