OpenClaw

faster-whisper

Local speech-to-text using faster-whisper. High-performance transcription with GPU acceleration support. Includes word-level timestamps and distilled models. Use when asked to "transcribe audio", "whisper", or "speech to text".

damirikys0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install damirikys/faster-whisper-local

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Faster-Whisper

High-performance local speech-to-text using faster-whisper.

Setup

1. Run Setup Script

Execute the setup script to create a virtual environment and install dependencies. It will automatically detect NVIDIA GPUs for CUDA acceleration.

./setup.sh

Requirements:

Python 3.10 or later
ffmpeg (installed on the system)

Usage

Use the transcription script to process audio files.

Basic Transcription

./scripts/transcribe audio.mp3

Advanced Options

Specific Model: ./scripts/transcribe audio.mp3 --model large-v3-turbo
Word Timestamps: ./scripts/transcribe audio.mp3 --word-timestamps
JSON Output: ./scripts/transcribe audio.mp3 --json
VAD (Silence Removal): ./scripts/transcribe audio.mp3 --vad

Available Models

distil-large-v3 (default): Best balance of speed and accuracy.
large-v3-turbo: Recommended for multilingual or highest accuracy tasks.
medium.en, small.en: Faster, English-only versions.

Troubleshooting

No GPU detected: Ensure NVIDIA drivers and CUDA are correctly installed. CPU transcription is significantly slower.
OOM Error: Use a smaller model (e.g., small or base) or use --compute-type int8.

来源信息

Source: github
Repository: openclaw/skills
Path: skills/damirikys/faster-whisper-local
Version: —
Commit: 3c0fd381a55f92b13549fab08c2d2aa75ac8d048
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Faster-Whisper

High-performance local speech-to-text using faster-whisper.

## Setup

### 1. Run Setup Script
Execute the setup script to create a virtual environment and install dependencies. It will automatically detect NVIDIA GPUs for CUDA acceleration.

```bash
./setup.sh
```

Requirements:
- Python 3.10 or later
- ffmpeg (installed on the system)

## Usage

Use the transcription script to process audio files.

### Basic Transcription
```bash
./scripts/transcribe audio.mp3
```

### Advanced Options
- **Specific Model**: `./scripts/transcribe audio.mp3 --model large-v3-turbo`
- **Word Timestamps**: `./scripts/transcribe audio.mp3 --word-timestamps`
- **JSON Output**: `./scripts/transcribe audio.mp3 --json`
- **VAD (Silence Removal)**: `./scripts/transcribe audio.mp3 --vad`

## Available Models

- `distil-large-v3` (default): Best balance of speed and accuracy.
- `large-v3-turbo`: Recommended for multilingual or highest accuracy tasks.
- `medium.en`, `small.en`: Faster, English-only versions.

## Troubleshooting

- **No GPU detected**: Ensure NVIDIA drivers and CUDA are correctly installed. CPU transcription is significantly slower.
- **OOM Error**: Use a smaller model (e.g., `small` or `base`) or use `--compute-type int8`.

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