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local-whisper

Local speech-to-text using OpenAI Whisper. Runs fully offline after model download. High quality transcription with multiple model sizes.

作者: admin | 来源: ClawHub
源自
ClawHub
版本
V 1.0.0
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10,315
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版本历史

local-whisper

# Local Whisper STT Local speech-to-text using OpenAI's Whisper. **Fully offline** after initial model download. ## Usage ```bash # Basic ~/.clawdbot/skills/local-whisper/scripts/local-whisper audio.wav # Better model ~/.clawdbot/skills/local-whisper/scripts/local-whisper audio.wav --model turbo # With timestamps ~/.clawdbot/skills/local-whisper/scripts/local-whisper audio.wav --timestamps --json ``` ## Models | Model | Size | Notes | |-------|------|-------| | `tiny` | 39M | Fastest | | `base` | 74M | **Default** | | `small` | 244M | Good balance | | `turbo` | 809M | Best speed/quality | | `large-v3` | 1.5GB | Maximum accuracy | ## Options - `--model/-m` — Model size (default: base) - `--language/-l` — Language code (auto-detect if omitted) - `--timestamps/-t` — Include word timestamps - `--json/-j` — JSON output - `--quiet/-q` — Suppress progress ## Setup Uses uv-managed venv at `.venv/`. To reinstall: ```bash cd ~/.clawdbot/skills/local-whisper uv venv .venv --python 3.12 uv pip install --python .venv/bin/python click openai-whisper torch --index-url https://download.pytorch.org/whl/cpu ```

标签

skill ai

通过对话安装

该技能支持在以下平台通过对话安装:

OpenClaw WorkBuddy QClaw Kimi Claude

方式一:安装 SkillHub 和技能

帮我安装 SkillHub 和 local-whisper-1776305954 技能

方式二:设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源,然后帮我安装 local-whisper-1776305954 技能

通过命令行安装

skillhub install local-whisper-1776305954

下载 Zip 包

⬇ 下载 local-whisper v1.0.0

文件大小: 2.41 KB | 发布时间: 2026-4-16 18:36

v1.0.0 最新 2026-4-16 18:36
- Initial release of local-whisper: local speech-to-text using OpenAI Whisper, fully offline after model download.
- Supports multiple model sizes for different speed/quality needs: tiny, base (default), small, turbo, large-v3.
- Includes options for language selection, timestamps, JSON output, and quiet mode.
- Provides clear setup instructions using uv-managed Python virtual environment.
- Requires ffmpeg for audio processing.

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