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在 OpenClaw 中安装
/install faster-whisper
功能描述
Local speech-to-text using faster-whisper. 4-6x faster than OpenAI Whisper with identical accuracy; GPU acceleration enables ~20x realtime transcription. SRT...
安全使用建议
Install only if you are comfortable giving this skill access to local media files, generated transcript outputs, outbound downloads from user-provided URLs or feeds, and optional HuggingFace credentials. Avoid using the --update feature in normal runs; update dependencies through a controlled package-management process instead, and run remote-media transcription in a constrained workspace.
功能分析
Type: OpenClaw Skill
Name: faster-whisper
Version: 1.5.1
This skill is classified as suspicious due to its broad capabilities, which include downloading content from arbitrary URLs via `yt-dlp`, executing `ffmpeg` for audio/video processing and subtitle burning, and performing self-updates of its core dependency. While these actions are plausibly aligned with the stated purpose of a comprehensive transcription tool, they grant significant access to the network and local file system. The `SKILL.md` agent guidance does not contain any malicious prompt injection attempts, and the `setup.sh` and `scripts/transcribe.py` files implement these powerful features using `subprocess.run()` with argument lists, which mitigates direct shell injection vulnerabilities. However, the inherent power of these operations, even when used for legitimate purposes, elevates the risk profile beyond a purely benign classification.
能力评估
Purpose & Capability
The media transcription, ffmpeg processing, subtitle generation, speaker export, and URL/RSS ingestion mostly fit a comprehensive transcription tool, but the runtime self-update path goes beyond normal transcription and can change later behavior.
Instruction Scope
The documented usage appears broad and user-directed, but the supplied artifacts show under-disclosed high-impact behaviors: outbound URL/RSS fetching, many filesystem outputs, HuggingFace token environment handling, and dependency self-update.
Install Mechanism
Setup dependencies are expected for this kind of tool, but the CLI also supports an explicit --update path that runs uv or pip install --upgrade faster-whisper inside the skill venv, introducing unreviewed future code.
Credentials
ffmpeg, yt-dlp, local file reads/writes, network access, and optional HuggingFace token use are plausible for transcription and diarization, but they process untrusted media and should be run with scoped filesystem and network access.
Persistence & Privilege
There is no evidence of hidden background persistence or privilege escalation, but the self-update behavior persistently mutates the local execution environment after review.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install faster-whisper - 安装完成后,直接呼叫该 Skill 的名称或使用
/faster-whisper触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.5.1
- Fixed --skip-existing in multi-format mode to check ALL format outputs before skipping
- Fixed --no-timestamps conflict check missing lrc, ass, ttml formats
- Fixed --speaker-names silently doing nothing without --diarize; now prints a warning
- Batch summary now shows skipped file count when --skip-existing is active
v1.5.0
- docs: update default model from distil-large-v3 to distil-large-v3.5
- fix: setup.sh --check hangfix + skill.json ffmpeg optional
- fix(transcribe): clean-filler word list, fuzzy search tokens, URL temp cleanup
- fix(multi-format): create output dir in single-file mode
- feat: add CSV output, language-map, batch ETA estimate
- feat: add TTML output, transcript search, chapter detection, speaker audio export
- feat: distil auto-condition, log-level, ffmpeg clarification
- fix: rename --without-timestamps to --no-timestamps
- feat: add 17 new features — upstream params + LRC/detect-language/merge-sentences/stats/stdin/template
v1.4.5
- Fix author field to match GitHub username (ThePlasmak)
v1.4.4
- Declare yt-dlp and HuggingFace token as optional dependencies in skill.json
- Sync SKILL.md frontmatter version and author with skill.json
v1.4.3
- Auto-run wav2vec2 alignment whenever word timestamps are computed
- Remove --precise flag (alignment is automatic, flag kept as hidden compat alias)
- Alignment triggers for --word-timestamps, --diarize, --min-confidence
- No overhead for basic transcription (fast path unchanged)
v1.4.1
- Add --precise flag for wav2vec2 forced alignment (~10ms word accuracy)
- Uses torchaudio MMS model (multilingual, cached for batch processing)
- Runs before diarization when combined (improves speaker assignment)
- Install torchaudio alongside torch in setup.sh
v1.3.0
- Add SRT and VTT subtitle output formats (--format srt/vtt)
- Add speaker diarization via pyannote.audio (--diarize) with word-level accuracy
- Add URL/YouTube input with auto yt-dlp download
- Add batch processing with glob patterns, directories, and --skip-existing
- Add initial prompt support for domain terminology (--initial-prompt)
- Add confidence-based segment filtering (--min-confidence)
- Add performance stats after each transcription (duration, realtime factor)
- Unify output under --format flag (text/json/srt/vtt), keep --json for backward compat
- Add agent guidance for minimal invocation (don't load unused features)
v1.2.0
- Default model changed to distil-large-v3.5 (lower WER: 7.08 vs 7.53, same speed as v3)
- Trained on 4x more data (98k hours) with improved robustness
v1.1.0
- Use BatchedInferencePipeline by default (~3x faster; 69s → 23s on 21-min file with distil-large-v3)
- VAD enabled by default in batched mode
- Add --batch-size option (default: 8; reduce if OOM)
- Add --no-batch flag to fall back to standard WhisperModel
- Add --hotwords support for boosting recognition of specific terms
- Bump tested version: faster-whisper 1.2.1
v1.0.12
- Fix skill title display on ClawdHub
v1.0.11
- Prefer distil-large-v3 over large-v3-turbo as the recommended model
v1.0.9
- docs: rebrand from Moltbot/MoltHub to OpenClaw/ClawHub
v1.0.7
- Removed Windows-native references from SKILL.md (setup.ps1, transcribe.cmd, winget) since ClawHub cannot distribute .ps1/.cmd files
- Windows users should use WSL2 or get Windows scripts from the GitHub repo directly
v1.0.6
- Added .clawdhubignore to exclude README.md, CHANGELOG.md, LICENSE from published package
- Fixed requires.bins in skill.json (python3, ffmpeg)
- Added platforms field to skill.json
- Updated metadata key from moltbot to openclaw in SKILL.md
v1.0.5
Fix metadata: add requires.bins to skill.json, add platforms, update moltbot to openclaw in SKILL.md
v1.0.4
- Fixed skill title and metadata
- Removed development files from published package
v1.0.3
Fix skill title (was 'Faster Whisper Clean' due to temp file naming)
v1.0.2
- Improve skill discovery, error handling, some copyediting
- Add skill.json
- Edit README to reduce confusion as Moltbot may refer to the service
v1.0.1
Remove install metadata (as ClawdHub's install section is confusing); add python3 to required binaries
v1.0.0
Initial public release of faster-whisper.
- Local speech-to-text using faster-whisper (CTranslate2 backend), ~4-6x faster than OpenAI Whisper, with identical accuracy.
- Supports GPU acceleration for ~20x realtime transcription; automatic hardware detection and setup for Windows.
- Offers both standard and distilled models, with selectable accuracy/speed tradeoffs and word-level timestamps.
- Cross-platform: Windows (including WSL2), Linux, and macOS (Apple Silicon supported).
- Setup scripts provided for all platforms, including automatic installation of dependencies and GPU support where possible.
- Includes extensive usage documentation, quick-start commands, model selection guide, and troubleshooting tips.
元数据
常见问题
Faster Whisper 是什么?
Local speech-to-text using faster-whisper. 4-6x faster than OpenAI Whisper with identical accuracy; GPU acceleration enables ~20x realtime transcription. SRT... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 8499 次。
如何安装 Faster Whisper?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install faster-whisper」即可一键安装,无需额外配置。
Faster Whisper 是免费的吗?
是的,Faster Whisper 完全免费(开源免费),可自由下载、安装和使用。
Faster Whisper 支持哪些平台?
Faster Whisper 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Faster Whisper?
由 Sarah Mak(@theplasmak)开发并维护,当前版本 v1.5.1。
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