| 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.
Use Cases
- Content creation, free alternative to paid subtitle features (e.g., CapCut/剪映)
- Meeting recording to text
- Podcast/course subtitles
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 |
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
Execution
AI will execute the scripts/transcribe.py script, which will:
- Automatically detect available GPU and select optimal acceleration
- Load Whisper model (default:
turbo)
- Transcribe audio to SRT format
- Save output in the same directory as the audio
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
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/
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 |
🎙️ 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。
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
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 |
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
执行方式
AI 会调用 scripts/transcribe.py 脚本执行转录，脚本会：
- 自动检测可用 GPU 设备并选择最优加速方式
- 加载 Whisper 模型（默认
turbo）
- 将音频转录为 SRT 格式字幕
- 输出文件保存在与音频同目录
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
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/
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 |
