Whisper Transcribe

该 Skill 通过 scripts/transcribe.sh 调用本地 OpenAI Whisper CLI，把音频或视频文件转写为文本，支持自动语言检测、tiny 到 large 的模型选择、txt/srt/vtt/json/全部格式输出、批量处理与词级时间戳；依赖 ffmpeg 解码，首次运行需下载模型。

josunlp

热度 96.96

最近更新 2026-05-11 15:57:11

分类：效率依赖：代码执行 / 读写文件应用场景：文件转换 / 会议纪要

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add whisper-transcribe

Metadata

name: whisper-transcribe
description: Transcribe audio files to text using OpenAI Whisper. Supports speech-to-text with auto language detection, multiple output formats (txt, srt, vtt, json), batch processing, and model selection (tiny to large). Use when transcribing audio recordings, podcasts, voice messages, lectures, meetings, or any audio/video file to text. Handles mp3, wav, m4a, ogg, flac, webm, opus, aac formats.

Whisper Transcribe

Transcribe audio with scripts/transcribe.sh:

# Basic (auto-detect language, base model)
scripts/transcribe.sh recording.mp3

# German, small model, SRT subtitles
scripts/transcribe.sh --model small --language de --format srt lecture.wav

# Batch process, all formats
scripts/transcribe.sh --format all --output-dir ./transcripts/ *.mp3

# Word-level timestamps
scripts/transcribe.sh --timestamps interview.m4a

Models

Model	RAM	Speed	Accuracy	Best for
tiny	~1GB	⚡⚡⚡	★★	Quick drafts, known language
base	~1GB	⚡⚡	★★★	General use (default)
small	~2GB	⚡	★★★★	Good accuracy
medium	~5GB	🐢	★★★★★	High accuracy
large	~10GB	🐌	★★★★★	Best accuracy (slow on Pi)

Output Formats

txt — Plain text transcript
srt — SubRip subtitles (for video)
vtt — WebVTT subtitles
json — Detailed JSON with timestamps and confidence
all — Generate all formats at once

Requirements

whisper CLI (pip install openai-whisper)
ffmpeg (for audio decoding)
First run downloads the model (~150MB for base)