AmicoScript local audio transcription tool.
AmicoScript is a privacy-focused, local-first transcription tool built on OpenAI's Whisper models. It allows you to transform audio recordings into structured, searchable transcripts without your data ever leaving your repository or machine. Whether you need speaker identification (diarization), translation, or simple subtitles, AmicoScript provides a fast, free, and secure alternative to cloud services.
Most transcription tools:
- require uploading your audio to the cloud
- cost money or have limits
- don’t give you control over your data
AmicoScript keeps everything local.
→ Your audio never leaves your machine.
- 🎧 Transcribe audio and video (MP3, WAV, M4A, OGG, FLAC, ACC, MP4, MOV, MKV)
- 📚 Batch process multiple files at once
- 🧠 Whisper models (tiny → large-v3)
- 🗣️ Speaker diarization (who said what)
- 🌍 Real-time translation to English
- 🔍 Global search across transcripts
- 🗂️ Organize with folders and tags
- ✏️ Edit individual segments
- 📤 Export to JSON, SRT, TXT, Markdown
- ⌨️ Keyboard shortcuts for fast navigation
- 🚀 For Mac, Windows, Docker, or local Python
Upload a meeting recording → get a structured, time-stamped transcript you can search, edit, and export.
docker compose up --buildThen open: http://localhost:8002
pip install -r backend/requirements.txt
python run.py- Download the latest release from the Releases page.
- Because the app is not signed by Apple, macOS will initially block it. Open System Settings → Privacy & Security and enable "App Store and identified developers" (allow apps downloaded from App Store and identified developers).
- Unzip the downloaded file. Double-click the application file (
AmicoScript.app). macOS will prevent it from opening because it's from an unidentified developer. - In System Settings → Privacy & Security, click the "Open Anyway" button next to the blocked app, then confirm when prompted to allow the application to run.
- The app will launch — you're ready to create icns files from PNG, JPG, or other image formats.
run.py will download ffmpeg automatically on first run.
Performance depends on your hardware (CPU/GPU) and selected model size.
- Larger models → better accuracy
- Smaller models → faster processing
Feedback and benchmarks are welcome.
Uses pyannote and requires a Hugging Face token.
See full setup instructions in: Documentation
Full documentation (API, setup, details):
- Backend: Python + FastAPI
- Frontend: Single HTML (no build step)
- Processing: Background jobs
- Storage: Temporary local files (auto-cleanup)
Feedback, issues, and contributions are welcome.
Give it a star — it helps a lot!
This project is licensed under the MIT License. See the LICENSE file for more details.