whisper-gpu-transcribe

name	whisper-gpu-transcribe
description	Convert audio to SRT subtitles using OpenAI Whisper with automatic GPU acceleration for Intel XPU / NVIDIA CUDA / AMD ROCm / Apple Metal. Ideal for content creators as a free alternative to paid subtitle generation.
version	1.0.2
metadata	{"openclaw":{"emoji":"🎙️","homepage":"https://github.com/allanmeng/whisper-gpu-transcriber-skill","requires":{"bins":"[Truncated]"},"install":["[Truncated]"]}}

🎙️ Whisper GPU Audio Transcriber

Convert audio files to SRT subtitles using local Whisper models — completely free, offline, and GPU accelerated.

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Use Cases

• Content creation, free alternative to paid subtitle features (e.g., CapCut/剪映)
• Meeting recording to text
• Podcast/course subtitles

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Supported GPU Acceleration

Device	Acceleration	FP16
Intel Arc Series	XPU	❌ Auto disabled
NVIDIA GPUs	CUDA	✅ Auto enabled
AMD GPUs	ROCm	✅ Auto enabled
Apple M Series	Metal	✅ Auto enabled
No GPU	CPU	❌ Auto disabled

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Usage

Basic Usage

Place the audio file in your current working directory and tell the AI:

Convert xxx.mp3 to SRT subtitles

Or specify the full path directly:

Convert /path/to/audio.mp3 to SRT subtitles

Advanced Usage

Convert xxx.mp3 to English subtitles using large-v3-turbo model

Convert xxx.mp3 to subtitles, language is Japanese

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Execution

AI will execute the scripts/transcribe.py script, which will:

1. Automatically detect available GPU and select optimal acceleration
2. Load Whisper model (default: turbo)
3. Transcribe audio to SRT format
4. Save output in the same directory as the audio

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Requirements

• Python 3.8+
• PyTorch (version matching your hardware)
  • Intel GPU: pip install torch==2.10.0+xpu
  • NVIDIA GPU: pip install torch --index-url https://download.pytorch.org/whl/cu121
  • CPU: pip install torch
• openai-whisper: Automatically installed via pip install openai-whisper

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Notes

• First run will auto-download the model file (turbo ~1.5GB)
• Models cache in ~/.cache/whisper by default, use symlink/Junction to redirect to another disk
• Intel XPU requires Intel Arc GPU + matching PyTorch version

Tip for China users: If model download fails, manually download from mirror sites and place in ~/.cache/whisper/

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Supported Models

Model	Size	Speed	Accuracy
tiny	39M	Fastest	Low
base	74M	Fast	Medium
small	244M	Medium	Medium
medium	769M	Slow	High
turbo	809M	Medium	High ✅ Recommended
large-v3	1550M	Slowest	Highest
large-v3-turbo	1550M	Slow	Highest

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🎙️ Whisper GPU 音频转字幕

使用本地 Whisper 模型将音频文件转录为 SRT 字幕，完全免费，无需联网，支持 GPU 加速。

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适用场景

• 自媒体视频制作，替代剪映付费字幕功能
• 会议录音转文字
• 播客/课程内容转字幕

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支持的 GPU 加速

设备	加速方式	FP16
Intel Arc 系列	XPU	❌ 自动禁用
NVIDIA 显卡	CUDA	✅ 自动启用
AMD 显卡	ROCm	✅ 自动启用
Apple M 系列	Metal	✅ 自动启用
无独显	CPU	❌ 自动禁用

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使用方法

基础用法

将音频文件放入当前工作目录，然后告诉 AI：

把 xxx.mp3 转成 SRT 字幕文件

或者直接指定路径：

把 /path/to/audio.mp3 转成 SRT 字幕

高级用法

把 xxx.mp3 用 large-v3-turbo 模型转成英文字幕

把 xxx.mp3 转成字幕，语言是日语

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执行方式

AI 会调用 scripts/transcribe.py 脚本执行转录，脚本会：

1. 自动检测可用 GPU 设备并选择最优加速方式
2. 加载 Whisper 模型（默认 turbo）
3. 将音频转录为 SRT 格式字幕
4. 输出文件保存在与音频同目录

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环境要求

• Python 3.8+
• PyTorch（版本需匹配硬件）
  • Intel GPU：pip install torch==2.10.0+xpu
  • NVIDIA GPU：pip install torch --index-url https://download.pytorch.org/whl/cu121
  • CPU：pip install torch
• openai-whisper：由 ClawHub 通过 pip install openai-whisper 自动安装

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注意事项

• 首次运行会自动下载模型文件（turbo 约 1.5GB）
• 模型默认缓存在 ~/.cache/whisper，可用软链接/Junction 指向其他磁盘
• Intel XPU 需要 Intel Arc 独显 + 对应版本 PyTorch

国内用户提示：首次运行会自动下载模型，如下载失败可手动从镜像站下载后放入 ~/.cache/whisper/

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支持的模型

模型	大小	速度	准确度
tiny	39M	最快	低
base	74M	快	中
small	244M	中	中
medium	769M	慢	高
turbo	809M	中	高 ✅ 推荐
large-v3	1550M	最慢	最高
large-v3-turbo	1550M	慢	最高