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docs: add app startup troubleshooting section
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rishikanthc
@@ -23,7 +23,7 @@ Open [http://localhost:8080](http://localhost:8080) in your browser.
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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).
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> **Docker Users:** You can ignore this section if you are using `docker-compose.yml`, as these values are set in the `environment` section there.
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> **Docker Users:** You can ignore this section if you are using `docker-compose.yml`, as these values are already configured with sane defaults.
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### Environment Variables
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@@ -73,11 +73,18 @@ services:
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ports:
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- "8080:8080"
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volumes:
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- scriberr_data:/app/data
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- scriberr_data:/app/data # volume for data
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- env_data:/app/whisperx-env # volume for models and python envs
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environment:
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- APP_ENV=production # DO NOT CHANGE THIS
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# CORS: comma-separated list of allowed origins for production
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# - ALLOWED_ORIGINS=https://your-domain.com
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# - SECURE_COOKIES=false # Uncomment this ONLY if you are not using SSL
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restart: unless-stopped
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volumes:
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scriberr_data:
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scriberr_data: {}
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env_data: {}
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```
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2. Run the container:
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@@ -94,14 +101,14 @@ If you have a compatible NVIDIA GPU, this configuration enables hardware acceler
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2. Create a file named `docker-compose.cuda.yml`:
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```yaml
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version: "3.9"
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services:
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scriberr:
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image: ghcr.io/rishikanthc/scriberr:v1.0.4-cuda
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ports:
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- "8080:8080"
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volumes:
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- scriberr_data:/app/data
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- scriberr_data:/app/data # volume for data
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- env_data:/app/whisperx-env # volume for models and python envs
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restart: unless-stopped
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deploy:
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resources:
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@@ -114,9 +121,14 @@ services:
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility
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- APP_ENV=production # DO NOT CHANGE THIS
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# CORS: comma-separated list of allowed origins for production
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# - ALLOWED_ORIGINS=https://your-domain.com
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# - SECURE_COOKIES=false # Uncomment this ONLY if you are not using SSL
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volumes:
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scriberr_data: {}
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env_data: {}
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```
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3. Run the container with the CUDA configuration:
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@@ -124,3 +136,75 @@ volumes:
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```bash
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docker compose -f docker-compose.cuda.yml up -d
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```
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## App Startup
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When you run Scriberr for the first time, it may take several minutes to start. This is normal!
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The application needs to initialize the Python environments and download the necessary machine learning models (NVIDIA Sortformer, NVIDIA Canary, NVIDIA Parakeet).
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**Subsequent runs will be much faster** because all models and environments are persisted to the `env_data` volume (or your local mapped folders).
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You will know the application is ready when you see the line: `msg="Scriberr is ready" url=http://0.0.0.0:8080`.
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**Example Startup Log:**
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```text
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scriberr | === Scriberr Container Setup ===
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scriberr | Requested UID: 10001, GID: 10001
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scriberr | Setting up custom user with UID=10001, GID=10001...
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scriberr | Group with GID 10001 already exists, using it
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scriberr | usermod: no changes
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scriberr | Setting up data directories...
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scriberr | === Setup Complete ===
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scriberr | Switching to user appuser (UID=10001, GID=10001) and starting application...
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scriberr | time=02:50:36 level="INFO " msg="Starting Scriberr" version=dev
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scriberr | [+] Loading configuration
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scriberr | time=02:50:36 level="INFO " msg="Registering adapters with environment path" whisperx_env=/app/whisperx-env
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scriberr | time=02:50:36 level="INFO " msg="Adapter registration complete"
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scriberr | [+] Connecting to database
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scriberr | [+] Setting up authentication
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scriberr | [+] Initializing SSE broadcaster
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scriberr | [+] Initializing repositories
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scriberr | [+] Initializing services
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scriberr | [+] Initializing transcription service
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scriberr | [+] Initializing transcription service
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scriberr | [+] Preparing Python environment
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scriberr | time=02:50:36 level="INFO " msg="Initializing unified transcription service"
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scriberr | time=02:50:36 level="INFO " msg="Initializing registered models in parallel..."
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scriberr | time=02:50:36 level="INFO " msg="Preparing NVIDIA Sortformer environment" env_path=/app/whisperx-env/parakeet
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scriberr | time=02:50:36 level="INFO " msg="transcription model initialized" model_id=openai_whisper
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scriberr | time=02:50:36 level="INFO " msg="Preparing NVIDIA Canary environment" env_path=/app/whisperx-env/parakeet
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scriberr | time=02:50:36 level="INFO " msg="Preparing PyAnnote environment" env_path=/app/whisperx-env/pyannote
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scriberr | time=02:50:36 level="INFO " msg="Preparing NVIDIA Parakeet environment" env_path=/app/whisperx-env/parakeet
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scriberr | time=02:50:36 level="INFO " msg="Preparing WhisperX environment" env_path=/app/whisperx-env
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scriberr | time=02:50:36 level="INFO " msg="Installing PyAnnote dependencies"
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scriberr | time=02:50:36 level="INFO " msg="Parakeet environment not ready, setting up"
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scriberr | time=02:50:36 level="INFO " msg="Installing Canary dependencies"
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scriberr | time=02:50:36 level="INFO " msg="Installing Parakeet dependencies"
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scriberr | time=02:50:36 level="INFO " msg="Downloading Sortformer model" path=/app/whisperx-env/parakeet/diar_streaming_sortformer_4spk-v2.nemo
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Downloading diar_streaming_sortformer_4spk-v2.nemo: 100% (449.5 MB / 449.5 MB)
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scriberr | time=02:50:53 level="INFO " msg="Successfully downloaded Sortformer model" size=471367680
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scriberr | time=02:50:53 level="INFO " msg="Sortformer environment prepared successfully"
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scriberr | time=02:50:53 level="INFO " msg="diarization model initialized" model_id=sortformer
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scriberr | time=02:53:11 level="INFO " msg="WhisperX environment prepared successfully"
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scriberr | time=02:53:11 level="INFO " msg="transcription model initialized" model_id=whisperx
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scriberr | time=02:53:14 level="INFO " msg="PyAnnote environment prepared successfully"
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scriberr | time=02:53:14 level="INFO " msg="diarization model initialized" model_id=pyannote
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scriberr | time=02:53:28 level="INFO " msg="Downloading Canary model" path=/app/whisperx-env/parakeet/canary-1b-v2.nemo
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scriberr | time=02:53:28 level="INFO " msg="Downloading Parakeet model" path=/app/whisperx-env/parakeet/parakeet-tdt-0.6b-v3.nemo
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Downloading parakeet-tdt-0.6b-v3.nemo: 100% (2.3 GB / 2.3 GB)
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scriberr | time=02:54:37 level="INFO " msg="Successfully downloaded Parakeet model" size=2509332480
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scriberr | time=02:54:37 level="INFO " msg="Created buffered transcription script" path=/app/whisperx-env/parakeet/transcribe_buffered.py
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scriberr | time=02:54:37 level="INFO " msg="Parakeet environment prepared successfully"
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scriberr | time=02:54:37 level="INFO " msg="transcription model initialized" model_id=parakeet
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Downloading canary-1b-v2.nemo: 100% (5.9 GB / 5.9 GB)
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scriberr | time=02:55:54 level="INFO " msg="Successfully downloaded Canary model" size=6358958080
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scriberr | time=02:55:54 level="INFO " msg="Canary environment prepared successfully"
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scriberr | time=02:55:54 level="INFO " msg="transcription model initialized" model_id=canary
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scriberr | time=02:55:54 level="INFO " msg="Model initialization completed"
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scriberr | time=02:55:54 level="INFO " msg="Unified transcription service initialized successfully"
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scriberr | [+] Initializing quick transcription service
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scriberr | [+] Starting background processing
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scriberr | time=02:55:54 level="INFO " msg="Scriberr is ready" url=http://0.0.0.0:8080
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```
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