Skill: allanmeng/whisper-gpu-transcriber-skill (version 1.0.3)

--- 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: - python install: - kind: pip package: openai-whisper --- # 🎙️ 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: 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 --- ## 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： 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 --- ## 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 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)