local-whisper

Original source / Raw SKILL.md

---
name: local-whisper
description: Local speech-to-text using OpenAI Whisper. Runs fully offline after model download. High quality transcription with multiple model sizes.
metadata: {"clawdbot":{"emoji":"🎙️","requires":{"bins":["ffmpeg"]}}}
---

# 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
```


---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### _meta.json

```json
{
  "owner": "araa47",
  "slug": "local-whisper",
  "displayName": "Local Whisper",
  "latest": {
    "version": "1.0.0",
    "publishedAt": 1769159934671,
    "commit": "https://github.com/clawdbot/skills/commit/1e24c04d50efd58d32a114d979970065bf09bf42"
  },
  "history": []
}

```

### scripts/transcribe.py

```python
#!/usr/bin/env python3
"""Local speech-to-text using OpenAI Whisper (runs offline after model download)."""

import json
import sys
import warnings

import click

warnings.filterwarnings("ignore")

MODELS = ["tiny", "tiny.en", "base", "base.en", "small", "small.en",
          "medium", "medium.en", "large-v3", "turbo"]


@click.command()
@click.argument("audio_file", type=click.Path(exists=True))
@click.option("-m", "--model", default="base", type=click.Choice(MODELS), help="Whisper model size")
@click.option("-l", "--language", default=None, help="Language code (auto-detect if omitted)")
@click.option("-t", "--timestamps", is_flag=True, help="Include word-level timestamps")
@click.option("-j", "--json", "as_json", is_flag=True, help="Output as JSON")
@click.option("-q", "--quiet", is_flag=True, help="Suppress progress messages")
def main(audio_file, model, language, timestamps, as_json, quiet):
    """Transcribe audio using OpenAI Whisper (local)."""
    try:
        import whisper
    except ImportError:
        click.echo("Error: openai-whisper not installed", err=True)
        sys.exit(1)

    if not quiet:
        click.echo(f"Loading model: {model}...", err=True)

    try:
        whisper_model = whisper.load_model(model)
    except Exception as e:
        click.echo(f"Error loading model: {e}", err=True)
        sys.exit(1)

    if not quiet:
        click.echo(f"Transcribing: {audio_file}...", err=True)

    try:
        result = whisper_model.transcribe(audio_file, language=language,
                                          word_timestamps=timestamps, verbose=False)
    except Exception as e:
        click.echo(f"Error transcribing: {e}", err=True)
        sys.exit(1)

    text = result["text"].strip()

    if as_json:
        output = {"text": text, "language": result.get("language", "unknown")}
        if timestamps and "segments" in result:
            output["segments"] = [
                {"start": s["start"], "end": s["end"], "text": s["text"],
                 **({"words": s["words"]} if "words" in s else {})}
                for s in result["segments"]
            ]
        click.echo(json.dumps(output, indent=2, ensure_ascii=False))
    else:
        click.echo(text)
        if timestamps and "segments" in result:
            click.echo("\n--- Segments ---", err=True)
            for seg in result["segments"]:
                click.echo(f"  [{seg['start']:.2f}s - {seg['end']:.2f}s]: {seg['text']}", err=True)


if __name__ == "__main__":
    main()

```