🎙️ Whisper GPU Audio Transcriber

---

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.

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Use Cases

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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

- 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`

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Notes

> **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

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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

- 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`

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注意事项

> **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 |