lore-ai 0.1.10

Privacy-first, local-only oral history transcription.

Lore 🎙️

Privacy-First, Local-Only Oral History Transcription & Archiving

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Lore is a desktop application designed for historians, archivists, and researchers. It provides state-of-the-art AI transcription, speaker diarization, named entity recognition, and translation—100% offline, on your own hardware.

No data leaves your computer. No cloud subscriptions. Just powerful, open-source AI packaged into a clean, intuitive PyQt6 interface.

✨ Features

• 🎧 Offline Transcription: Powered by faster-whisper, optimized for CPU inference with low memory overhead (< 8GB RAM).
• 🗣️ Speaker Diarization: Automatically identifies and labels different speakers using the ONNX-based pipeline (WeSpeaker embedding + KMeans clustering, using Whisper segment boundaries). No GPU, no HuggingFace token, no PyTorch required.
• 🔀 Overlapping Speech Detection: Flags when multiple speakers talk simultaneously via a lightweight ONNX segmentation model (6 MB). Overlap regions are visualised on a dedicated strip and per-segment badges in the transcript view.
• ⚡ Model Tier Selection: Choose between Fast (small), Balanced (medium), or Best Quality (large-v3-turbo) Whisper models in Settings.
• 🔍 Word-Level Confidence: Low-confidence words are visually highlighted so you can quickly spot potential hallucinations.
• 🌍 Local Translation: Translate transcripts to over 200 languages completely offline using Meta's NLLB-200 model.
• 📖 Custom Vocabulary: Provide local jargon, proper nouns, and historical terms to guide Whisper's decoding graph for maximum accuracy.
• 🏷️ Named Entity Recognition: Uses GLiNER to automatically extract people, organizations, dates, and locations.
• 📦 Archival Exporting: Export your work to the OHMS XML format or create an RFC 8493 BagIt archival package with SHA-256 checksum verification.
• 🔎 Global Archive Search: A unified SQLite database (FTS5 + sqlite-vec) lets you instantly search across all your past projects using keyword or semantic/conceptual search.

🚀 Installation

Option 1: Pre-built Installers (Recommended)

Download the installer for your platform from the latest release:

Platform	Installer
🪟 Windows	lore-windows-x86_64.zip — Extract and run lore.exe
🍎 macOS	lore-macos-arm64.tar.gz — Extract and run lore
🐧 Linux	Install via PyPI (pip install lore-ai) or build from source

Option 2: Install from PyPI

pip install lore-ai
lore

Option 3: Install from Source

Lore requires Python 3.12+ and is cross-platform (Windows, macOS, Linux).

1. Clone the repository:

   git clone https://github.com/mabo-du/lore.git
   cd lore
   

2. Set up a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install the application:

   pip install -e .
   

🎮 Usage

Start the Lore application:

lore

Or launch from your system's application menu if installed via the pre-built installer.

1. Select an Audio File: Click "Browse" to select any standard audio format (WAV, MP3, M4A, OGG, FLAC).

2. Configure Settings: Click the ⚙️ Settings icon to set your Custom Vocabulary and speaker diarization preferences.

   

3. Transcribe & Diarize: Click "Transcribe" on the toolbar. If recording has multiple speakers, check the "Enable Speaker Diarization" box.

4. Edit & Review: Play the audio, click on segments to edit them, and review any low-confidence words highlighted in red.

5. Translate: Select a target language from the dropdown and click "Translate" for fully offline translation.

6. Export: Fill out the Metadata panel and export to OHMS XML or an Archival BagIt Package.

For detailed instructions, see the User Guide.

🏗️ Architecture

Lore is designed with strict sequential memory management to run on older hardware.

• Models are loaded into memory one at a time (e.g., Whisper loads, transcribes, unloads → NLLB loads, translates, unloads).
• Heavy use of CTranslate2 (INT8 quantization) ensures models run blazingly fast without needing a dedicated GPU.
• The UI runs asynchronously using PyQt6's QThread and Signals, keeping the interface completely responsive during heavy AI workloads.

🤝 Contributing

Lore is an open-source project. We welcome pull requests, bug reports, and feature requests. Please see our User Guide for more detailed workflows and documentation on the codebase.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.