2 added, 0 removed. Audit A to A.
---
name: transkription
version: 0.1.0
type: assist
author: ellmos-ai
created: 2026-06-22
updated: 2026-06-22
description: Transcribes audio/video files to text. Uses Whisper (openai-whisper) or Vosk (offline) as optional backend — both are detected via presence check. Without backend: placeholder mode with dummy output (dry-run).
standalone: true
anthropic_compatible: true
bach_compatible: false
bach_origin: false
category: assist
tags: [transkription, audio, speech-to-text, whisper, vosk, offline]
language: de
status: stable
dependencies: {'tools': [], 'services': [], 'protocols': [], 'python': [{'name': 'openai-whisper', 'optional': True, 'install': 'pip install openai-whisper', 'purpose': 'STT backend option 1 (cloud/local model)'}, {'name': 'vosk', 'optional': True, 'install': 'pip install vosk', 'purpose': 'STT backend option 2 (fully offline)'}]}
provenance: {'origin': 'eigenentwurf', 'origin_path': '', 'origin_version': '', 'origin_repo': '', 'origin_license': 'MIT', 'last_sync_from_origin': '', 'notes': 'Kein direkter BACH-Origin vorhanden (transkriptions-service existiert nicht als Datei in BACH/system). Skill neu konzipiert. voice_stt.py aus BACH/hub/_services/voice/ hat das Backend-Muster inspiriert (optionale Imports mit Verfügbarkeits-Flags), wurde aber nicht direkt portiert.\n'}
---
+ <img src="banner.png" width="100%" alt="transkription banner">
+
> **Deutsch** — Offizielle Deutsch-Version / Documento Oficial en Deutsch.
## Übersicht & Zweck
Convert audio/video files to text — locally, without mandatory cloud access. The skill
automatically detects whether Whisper or Vosk is installed and selects the best
available backend. Without a backend it runs in dry-run mode and returns a
placeholder text, so the workflow always works.
Transcripts are stored locally in `transkription/store.db` and can be queried.
---
## Triggers
| Phrase | Action |
|---|---|
| "Transcribe this audio" | Transcribe audio file |
| "Transcribe [file]" | Transcribe named file |
| "Show my transcripts" | List latest transcripts |
| "Search transcript [term]" | Full-text search in transcripts |
| "Export transcript [ID]" | Export transcript as TXT |
---
## Workflow & Vorgehen
1. **Backend check**: Check whether `whisper` or `vosk` is importable.
2. **File check**: Input file must exist (audio: wav, mp3, m4a, ogg, flac; video: mp4, mkv, webm — extraction via ffmpeg).
3. **Transcription**: Call backend and obtain raw text.
4. **Save**: Store result with metadata (file, duration, language, backend, timestamp) in `store.db`.
5. **Output**: Return text; optionally export as `.txt`.
---
## CLI Entry Point
```bash
# Transcribe file (Deutsch)
python transkription_core.py transcribe audio.wav
# With explicit language (Deutsch)
python transkription_core.py transcribe audio.mp3 --lang de
# Dry-run (no backend required) (Deutsch)
python transkription_core.py transcribe audio.wav --dry-run
# List transcripts (Deutsch)
python transkription_core.py list [--limit 20]
# Full-text search (Deutsch)
python transkription_core.py search "term"
# Export (Deutsch)
python transkription_core.py export <id> [--out file.txt]
# Backend check (Deutsch)
python transkription_core.py check
# Alternative store path (e.g. for tests) (Deutsch)
python transkription_core.py --store /tmp/test.db transcribe audio.wav --dry-run
```
---
## Store
| Property | Value |
|---|---|
| Type | SQLite |
| Path (default) | `skills/assist/transkription/store.db` |
| Override | `--store <path>` or env `TRANSKRIPTION_STORE` |
| Tables | `transcripts` |
### Schema `transcripts`
```sql
CREATE TABLE IF NOT EXISTS transcripts (
id TEXT PRIMARY KEY, -- UUID (short: 8 hex)
file_path TEXT NOT NULL, -- original path of audio file
file_name TEXT NOT NULL, -- filename (without path, for display)
text TEXT NOT NULL, -- transcribed text
language TEXT, -- language (e.g. "de", "en")
backend TEXT, -- "whisper" | "vosk" | "dry-run"
duration_s REAL, -- duration in seconds (if known)
created_at TEXT NOT NULL, -- ISO-8601 timestamp
tags TEXT -- comma-separated tags (optional)
);
```
---
## Attitude
- Without an installed backend the skill works in dry-run mode (demo text).
- Whisper is preferred over Vosk (better German quality).
- The choice between Whisper and Vosk can be set via `assist/prefs.json` (`transkription_backend: "whisper"|"vosk"|"auto"`).
- ffmpeg for video extraction is needed separately and is not included in the skill.
---
## Privacy
- **All transcripts stay local** — no cloud transfer without Whisper online mode.
- Whisper can be used locally (tiny/base/medium model) or via OpenAI API.
By default the local model is used.
- `store.db` may contain sensitive conversation content — **do not commit to Git**.
- Recommendation: add `store.db` to `.gitignore`.
---
## Related Resources
- BACH `hub/_services/voice/voice_stt.py` — backend pattern (inspiration, read-only)
- Skill `utilities/yt-transcriber` — YouTube transcription (separate skill, not a duplicate: YT-specific)
- `tools/module-installer/module_installer.py` — registry contains whisper + vosk
---
## Änderungsprotokoll
| Version | Date | Change |
|---|---|---|
| 0.1.0 | 2026-06-22 | Initial creation — own SQLite store, Whisper/Vosk presence check |