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BytesAgain › Skills › Whisper GPU Audio Transcriber
🦀 ClawHub

Whisper GPU Audio Transcriber

by @allanmeng

Convert audio to SRT subtitles using OpenAI Whisper with automatic GPU acceleration for Intel XPU / NVIDIA CUDA / AMD ROCm / Apple Metal. Ideal for content c...

Versionv1.0.3
Downloads391
#writing#video#audio#legal#automation
TERMINAL
clawhub install whisper-gpu-transcriber-skill

📖 About This Skill

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 |

    ⚡ When to Use

    TriggerAction
    - Meeting recording to text
    - Podcast/course subtitles
    ---

    💡 Examples

    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

    📋 Tips & Best Practices

  • 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/