Speakr - Self-Hosted AI Transcription Platform Docker Hub: https://hub.docker.com/r/learnedmachine/speakr GitHub: https://github.com/murtaza-nasir/speakr Documentation: https://murtaza-nasir.github.io/speakr Overview Speakr transforms your audio recordings into organized, searchable, and intelligent notes. Built for privacy-conscious individuals and groups, it runs entirely on your own infrastructure. Key Features • 🎙️ AI-Powered Transcription - High-accuracy transcription with speaker identification • 🗣️ Voice Profiles - Automatic speaker recognition using voice embeddings • 💬 Interactive Chat - Ask questions about your recordings • 🔍 Semantic Search - Search across all recordings using natural language (Inquire Mode) • 🤝 Collaboration - Share recordings with users and groups • 🏷️ Smart Tagging - Custom AI prompts and ASR settings per tag • 📤 Auto-Export - Export to Obsidian, Logseq, and other note-taking apps • 🌍 Internationalization - Full support for EN, ES, FR, DE, ZH • 🎨 Themes - Light/dark modes with customizable color schemes Installation on Unraid Prerequisites You’ll need API keys for: • Text Generation: OpenRouter (recommended) or OpenAI • Transcription: OpenAI Whisper API OR you can host your own services for ASR and LLM inferencing. Get your API keys: • OpenRouter: https://openrouter.ai/keys (supports many models including GPT-4o-mini) • OpenAI: https://platform.openai.com/api-keys Quick Start 1. Install from Community Applications - search for “Speakr” 2. Configure required fields: – Text Model API Key - Your OpenRouter or OpenAI key – Transcription API Key - Your OpenAI Whisper key – Admin Password - Choose a secure password 3. Click Apply 4. Access at http://YOUR-UNRAID-IP:8899 5. Log in with username admin and your chosen password Default Configuration The template uses sensible defaults: • Port: 8899 • Storage: /mnt/user/appdata/speakr/ • Text Model: OpenRouter with GPT-4o-mini (cheap and fast) • Registration: Disabled (only admin can create accounts) Advanced Features Speaker Diarization (Optional) For advanced speaker identification with voice profiles, you can run a separate ASR service: • WhisperX ASR Service (recommended): https://github.com/murtaza-nasir/whisperx-asr-service • Basic ASR Service: https://github.com/ahmetoner/whisper-asr-webservice Then update the environment variables to use your ASR endpoint instead of OpenAI. These services will need a CUDA capable GPU. Auto-Processing Enable the “Auto-Process” feature to automatically transcribe files dropped into a watch directory: 1. Set ENABLE_AUTO_PROCESSING=true 2. Drop audio files into /mnt/user/appdata/speakr/auto-process/ 3. Files are automatically processed and moved to your upload folder Auto-Export to Obsidian/Logseq Set ENABLE_AUTO_EXPORT=true and point the exports volume to your note-taking app’s import folder. Common Use Cases • Meeting notes - Record and transcribe team meetings with speaker identification • Interviews - Transcribe research interviews with automatic export • Lectures - Convert lecture recordings to searchable notes • Podcasts - Generate transcripts and show notes • Legal/Medical - Securely transcribe consultations (all data stays on your server) Documentation Complete documentation available at: https://murtaza-nasir.github.io/speakr Support • Issues/Bugs: https://github.com/murtaza-nasir/speakr/issues • Questions/Discussion: https://github.com/murtaza-nasir/speakr/discussions • Unraid Support: This thread Screenshots Screenshots: https://murtaza-nasir.github.io/speakr/screenshots License Dual-licensed under AGPLv3 (open source) and commercial license. ________________________________________ Post questions and issues in this thread. I’ll do my best to help!