According to AlphaMoat, as of 2026-09, Local Whisper has 4,425 downloads and 0 stars — ranked #2,199 of 5,078 AI skills overall, and #215 of 512 in Design & Media.

Installs

200

Downloads

4.4K

Stars

Overall Rank

#2,199

Top 44% overall · Top 42% in Design & Media

Platform	Installs	Downloads	Stars	Comments
ClawHub ↗	34	1.3K	-	-
SkillHub ↗	200	4.4K	-	—

Platforms count differently and update at different times.

What this skill does

The primary describes transcribing inbound Telegram voice notes with local whisper.cpp and lists build tools, audio-decoding dependencies, and model choices.

Profile published 2026-10-27

What you can do with it

Transcribing incoming Telegram voice notes locally

When to use it

You want local speech processing instead of a paid speech API

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Transcribe this incoming Telegram voice note locally

Monthly Trend

Downloads4.4K

Stars-

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

Month	Downloads	MoM	Stars
2026-09	4.4K	—	-
2026-08	3.1K	+45.6%	-
2026-07	2.1K	+33.8%	-
2026-06	1.6K	+16.7%	-
2026-05	1.3K	—	-
2026-03	734	—	-

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

2.1 KB · ClawHub v1.0.2

---
name: whisper-cpp
description: Install and use whisper.cpp (local, free/offline speech-to-text) with OpenClaw. Supports downloading different ggml model sizes (tiny/base/small/medium/large-*) and configuring tools.media.audio to transcribe inbound voice notes without paid provider APIs.
---

# whisper-cpp (Local Whisper STT for OpenClaw)

This skill sets up **local whisper.cpp** STT for inbound Telegram voice notes.

## Dependencies

Show all (93 lines)


You need build tools (`git`, `cmake`, compiler toolchain) + `curl` and `ffmpeg` (to decode Telegram OGG/Opus → WAV).

## Install

From this skill directory:

```bash
bash scripts/install_whisper_cpp.sh
bash scripts/download_models.sh
bash scripts/install_wrapper.sh
bash scripts/patch_openclaw_audio.sh
```

Send a Telegram voice note to test.

## Tuning

### Model choice

This setup uses **ggml Whisper models** stored in `~/.cache/whisper`.

Common model names you can download:
- `tiny`, `base`, `small`, `medium`
- `large-v1`, `large-v2`, `large-v3` (bigger/slower, usually more accurate)

By default we download: `base` + `small`.

To download specific models:

```bash
bash scripts/download_models.sh tiny base small
```

For the OpenClaw wrapper, you can select:

```bash
OPENCLAW_WHISPER_MODEL=small openclaw-whisper-stt /path/to/audio
```

- Default language: auto-detect (`OPENCLAW_WHISPER_LANG=auto`)
- Force a language (example):
  ```bash
  OPENCLAW_WHISPER_LANG=en openclaw-whisper-stt /path/to/audio
  ```

Models are stored in: `~/.cache/whisper`.

## Cleanup (optional)

After install (whisper-cli + libs are in `~/.local/`):

```bash
bash scripts/cleanup_build.sh
```

## Troubleshooting

Confirm OpenClaw is using the wrapper:

```bash
which openclaw-whisper-stt
openclaw config get tools.media.audio.models
```

Test the wrapper directly:

```bash
openclaw-whisper-stt /path/to/audio.ogg
OPENCLAW_WHISPER_MODEL=small openclaw-whisper-stt /path/to/audio.ogg
```

Follow gateway logs while sending a Telegram voice note:

```bash
openclaw logs --follow
```

## Files

- Wrapper source: `bin/openclaw-whisper-stt.sh` (linked to `~/.local/bin/openclaw-whisper-stt`)
- OpenClaw config patcher: `scripts/patch_openclaw_audio.sh`