Trendshift - Ask AI

base on Self-hosted AI audio transcription <div align="center"> <img src="logo.svg" height="90" style="vertical-align: middle;" /> <img src="logo-text.svg" height="80" style="vertical-align: middle;" /> </div> Scriberr is an open-source, and completely offline audio transcription application designed for self-hosters who value privacy and performance. <a href="https://scriberr.app">Website</a> • <a href="https://scriberr.app/docs/">Docs</a> • <a href="https://scriberr.app/api">API Reference</a> <a href='https://ko-fi.com/H2H41KQZA3' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> <div align="center"> <img src="screenshots/hero.png" alt="Scriberr Desktop App" width="800" /> </div> ## Sponsors ![recall.ai-logo](https://cdn.prod.website-files.com/620d732b1f1f7b244ac89f0e/66b294e51ee15f18dd2b171e_recall-logo.svg) Meeting Transcription API If you're looking for a transcription API for meetings, consider checking out [Recall.ai](https://www.recall.ai/?utm_source=github&utm_medium=sponsorship&utm_campaign=rishikanthc-scriberr), an API that works with Zoom, Google Meet, Microsoft Teams, and more. Recall.ai diarizes by pulling the speaker data and seperate audio streams from the meeting platforms, which means 100% accurate speaker diarization with actual speaker names. ## Introduction At its core, Scriberr allows you to transcribe audio and video locally on your machine, ensuring no data is ever sent to a third-party cloud provider. Leveraging state-of-the-art machine learning models (such as **NVIDIA Parakeet**, and **Canary**) or the older more popular **Whisper** models, it delivers high-accuracy text with word-level timing. Scriberr goes beyond simple transcription and provides various advanced capabilities. It combines powerful under-the-hood AI with a polished, fluid user interface that makes managing your recordings feel effortless. Whether you are sorting through voice notes or analyzing long meetings, Scriberr provides a beautiful environment to get work done: - **Smart Speaker Detection**: Scriberr automatically detects different speakers (Diarization) and labels exactly who said what. - **Chat with your Audio**: Connect seamlessly with Ollama or OpenAI API compatible providers. You can generate summaries, ask questions, or have a full conversation with your transcripts right inside the app. - **Built for your Workflow**: With extensive APIs and Folder Watcher that automatically processes new files in a folder, Scriberr fits right into your existing automations (like n8n). - **Capture & Organize**: Use the built-in audio recorder to capture thoughts on the fly, and the integrated note-taking features to annotate your transcripts as you listen. - **Native Experience everywhere**: Scriberr supports PWA (Progressive Web App) installation, giving you a native app experience on your desktop or mobile device. - **A Polished UI**: I’ve focused on the little UI niceties that make the app feel responsive and satisfying to use. [View full list of features →](https://scriberr.app/docs/features) ### Why I built this The inspiration for Scriberr was born out of privacy paranoia and not wanting to pay for subscription. About a year ago, I purchased a [Plaud Note](https://www.plaud.ai/) for recording voice memos. I loved the device itself; the form factor, microphone quality, and workflow were excellent. However, transcription was done on their cloud servers. As someone who is paranoid about privacy I wasn't comfortable with uploading my recordings to a third party provider. Moreover I was hit with subscription costs: $100 a year for 20 hours of transcription per month, or $240 a year for unlimited access. As an avid self-hoster with a background in ML and AI, it felt wrong to pay such a premium for a service I knew I could engineer myself. I decided to build Scriberr to bridge that gap, creating a powerful, private, and free alternative for everyone. ## Screenshots <details> <summary>Click to expand</summary> <img alt="Transcript view" src="screenshots/transcript-light.png" width="720" /> Transcript reader with playback follow‑along and seek‑from‑text. <img alt="Chat with Audio" src="screenshots/chat.png" width="720" /> Chat with your transcripts using local LLMs or OpenAI. <img alt="Notes and Highlights" src="screenshots/notes.png" width="720" /> Highlight key moments and take notes while listening. <img alt="AI Summaries" src="screenshots/ai-summary.png" width="720" /> Generate comprehensive summaries of your recordings. Dark Mode <img alt="Homepage Dark Mode" src="screenshots/homepage-dark.png" width="720" /> Homepage in Dark Mode. <img alt="Transcript Dark Mode" src="screenshots/transcript-dark.png" width="720" /> Transcript view in Dark Mode. ### Mobile <img alt="Mobile Homepage" src="screenshots/homepage-mobile.PNG" width="300" /> <img alt="Mobile Homepage Dark" src="screenshots/homepage-mobile-dark.PNG" width="300" /> PWA mobile app (Light & Dark). <img alt="Mobile Transcript" src="screenshots/transcript-mobile.PNG" width="300" /> <img alt="Mobile Transcript Dark" src="screenshots/transcript-mobile-dark.PNG" width="300" /> Mobile transcript reading experience. </details> ## Installation Get Scriberr running on your system in a few minutes. ### Migrating from v1.1.0 If you are upgrading from v1.1.0, please follow these steps to ensure a smooth transition. Version 1.2.0 introduces a separation between application data (database, uploads) and model data (Python environments). #### 1. Update Volume Mounts You will need to update your Docker volume configuration to split your data: * **Application Data:** Bind your existing data folder (containing `scriberr.db`, `jwt_secret`, `transcripts/`, and `uploads/`) to `/app/data`. * **Model Environment:** Create a **new, empty folder** and bind it to `/app/whisperx-env`. #### 2. Clean Up Old Environments > **CRITICAL:** You must delete any existing `whisperx-env` folder from your previous installation. The Python environment and models need to be reinitialized for v1.2.0. If the application detects an old environment, it may attempt to use it, leading to compatibility errors. Starting with a fresh `/app/whisperx-env` volume ensures the correct dependencies are installed. ### Install with Homebrew (macOS & Linux) The easiest way to install Scriberr is using Homebrew. If you don’t have Homebrew installed, [get it here first](https://brew.sh/). ```bash # Add the Scriberr tap brew tap rishikanthc/scriberr # Install Scriberr (automatically installs UV dependency) brew install scriberr # Start the server scriberr ``` Open [http://localhost:8080](http://localhost:8080) in your browser. ### Configuration Scriberr works out of the box. However, for Homebrew or manual installations, you can customize the application behavior using environment variables or a `.env` file placed in the same directory as the binary (or where you run the command from). > **Docker Users:** You can ignore this section if you are using `docker-compose.yml`, as these values are already configured with sane defaults. #### Environment Variables | Variable | Description | Default | | :--- | :--- | :--- | | `PORT` | The port the server listens on. | `8080` | | `HOST` | The interface to bind to. | `0.0.0.0` | | `APP_ENV` | Application environment (`development` or `production`). | `development` | | `ALLOWED_ORIGINS` | CORS allowed origins (comma separated). | `http://localhost:5173,http://localhost:8080` | | `DATABASE_PATH` | Path to the SQLite database file. | `data/scriberr.db` | | `UPLOAD_DIR` | Directory for storing uploaded files. | `data/uploads` | | `TRANSCRIPTS_DIR` | Directory for storing transcripts. | `data/transcripts` | | `WHISPERX_ENV` | Path to the managed Python environment for models. | `data/whisperx-env` | | `OPENAI_API_KEY` | API Key for OpenAI (optional). | `""` | | `JWT_SECRET` | Secret for signing JWTs. Auto-generated if not set. | Auto-generated | **Example `.env` file:** ```bash # Server settings HOST=localhost PORT=8080 APP_ENV=production # Paths DATABASE_PATH=/var/lib/scriberr/data/scriberr.db UPLOAD_DIR=/var/lib/scriberr/data/uploads # Security JWT_SECRET=your-super-secret-key-change-this ``` ### Docker Deployment For a containerized setup, you can use Docker. We provide two configurations: one for standard CPU usage and one optimized for NVIDIA GPUs (CUDA). > [!IMPORTANT] > **Permissions:** Ensure you set the `PUID` and `PGID` environment variables to your host user's UID and GID (typically `1000` on Linux) to avoid permission issues with the SQLite database. You can find your UID/GID by running `id` on your host. > > **HTTP vs HTTPS:** By default, Scriberr enables **Secure Cookies** in production. If you are accessing the app via plain HTTP (not HTTPS), you MUST set `SECURE_COOKIES=false` in your environment variables, otherwise you will encounter "Unable to load audio stream" errors. #### Standard Deployment (CPU) Use this configuration for running Scriberr on any machine without a dedicated NVIDIA GPU. 1. Create a file named `docker-compose.yml`: ```yaml services: scriberr: image: ghcr.io/rishikanthc/scriberr:v1.2.0 ports: - "8080:8080" volumes: - scriberr_data:/app/data # volume for data - env_data:/app/whisperx-env # volume for models and python envs environment: - PUID=${PUID:-1000} - PGID=${PGID:-1000} - APP_ENV=production # DO NOT CHANGE THIS # CORS: comma-separated list of allowed origins for production # - ALLOWED_ORIGINS=https://your-domain.com # - SECURE_COOKIES=false # Uncomment this ONLY if you are not using SSL restart: unless-stopped volumes: scriberr_data: {} env_data: {} ``` 2. Run the container: ```bash docker compose up -d ``` #### NVIDIA GPU Deployment (CUDA) If you have a compatible NVIDIA GPU, this configuration enables hardware acceleration for significantly faster transcription. 1. Ensure you have the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed. 2. Create a file named `docker-compose.cuda.yml`: ```yaml services: scriberr: image: ghcr.io/rishikanthc/scriberr-cuda:v1.2.0 ports: - "8080:8080" volumes: - scriberr_data:/app/data # volume for data - env_data:/app/whisperx-env # volume for models and python envs restart: unless-stopped deploy: resources: reservations: devices: - driver: nvidia count: all capabilities: - gpu environment: - NVIDIA_VISIBLE_DEVICES=all - NVIDIA_DRIVER_CAPABILITIES=compute,utility - PUID=${PUID:-1000} - PGID=${PGID:-1000} - APP_ENV=production # DO NOT CHANGE THIS # CORS: comma-separated list of allowed origins for production # - ALLOWED_ORIGINS=https://your-domain.com # - SECURE_COOKIES=false # Uncomment this ONLY if you are not using SSL volumes: scriberr_data: {} env_data: {} ``` 3. Run the container with the CUDA configuration: ```bash docker compose -f docker-compose.cuda.yml up -d ``` #### GPU Compatibility Scriberr provides separate Docker images for different NVIDIA GPU generations due to CUDA/PyTorch compatibility requirements: | GPU Generation | Compute Capability | Docker Image | Docker Compose File | |:---|:---|:---|:---| | GTX 10-series (Pascal) | sm_61 | `scriberr-cuda` | `docker-compose.cuda.yml` | | RTX 20-series (Turing) | sm_75 | `scriberr-cuda` | `docker-compose.cuda.yml` | | RTX 30-series (Ampere) | sm_86 | `scriberr-cuda` | `docker-compose.cuda.yml` | | RTX 40-series (Ada Lovelace) | sm_89 | `scriberr-cuda` | `docker-compose.cuda.yml` | | **RTX 50-series (Blackwell)** | sm_120 | `scriberr-cuda-blackwell` | `docker-compose.blackwell.yml` | **RTX 50-series users (RTX 5080, 5090, etc.):** You must use the Blackwell-specific image. The standard CUDA image will not work due to PyTorch CUDA compatibility requirements. Use: ```bash docker compose -f docker-compose.blackwell.yml up -d ``` Or for local builds: ```bash docker compose -f docker-compose.build.blackwell.yml up -d ``` ### App Startup When you run Scriberr for the first time, it may take several minutes to start. This is normal! The application needs to: 1. Initialize the Python environments. 2. Download the necessary machine learning models (Whisper, PyAnnote, NVIDIA NeMo). 3. Configure the database. **Subsequent runs will be much faster** because all models and environments are persisted to the `env_data` volume (or your local mapped folders). You will know the application is ready when you see the line: `msg="Scriberr is ready" url=http://0.0.0.0:8080`. ### Troubleshooting #### 1. SQLite OOM Error (out of memory) If you see an "out of memory (14)" error from SQLite (specifically `SQLITE_CANTOPEN`), it usually means a permissions issue. The database engine cannot create temporary files in the data directory. You can fix this by setting the `PUID` and `PGID` in your `docker-compose.yml` to match your host user's UID and GID, or by manually changing the ownership of the mapped folders on your host: ```bash # If you used a named volume (e.g., 'scriberr_scriberr_data'): sudo chown -R 1000:1000 /var/lib/docker/volumes/scriberr_scriberr_data/_data # If you mapped a specific host folder (e.g., ./scriberr_data): sudo chown -R 1000:1000 ./scriberr_data sudo chown -R 1000:1000 ./env_data ``` Replace `1000` with the value you set for `PUID`/`PGID` (default is `1000`). #### 2. "Unable to load audio stream" If the application loads but you cannot play or see the audio waveform (receiving "Unable to load audio stream"), this is often due to the **Secure Cookies** security flag. By default, when `APP_ENV=production`, Scriberr enables `SECURE_COOKIES=true`. This prevents cookies from being sent over insecure (HTTP) connections. **Solutions:** - **Recommended:** Deploy Scriberr behind a Reverse Proxy (like Nginx, Caddy, or Traefik) and use SSL/TLS (HTTPS). - **Alternative:** If you must access over plain HTTP, set the following environment variable in your `docker-compose.yml`: ```yaml environment: - SECURE_COOKIES=false ``` ## Post installation Once you have Scriberr up and running: - **Configure Diarization**: To enable speaker identification, visit the [Configuration page](https://scriberr.app/docs/configuration). - **Usage Guide**: For a detailed usage guide, visit [https://scriberr.app/docs/usage](https://scriberr.app/docs/usage). ## LLM Disclosure This project was developed using AI agents as pair programmer. It was NOT vibe coded. For context I’m a ML/AI researcher by profession and I have been programming for over a decade now. The codebase follows software engineering best practices and principles and all architecture decisions were made by me. All code generated by LLMs was reviewed and tested to the best of my abilities. ## Donating <a href='https://ko-fi.com/H2H41KQZA3' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> ", Assign "at most 3 tags" to the expected json: {"id":"16512","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

AI prompts