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Offline / Airgapped Installation

OpenTranscribe supports complete offline deployment for airgapped environments, secure facilities, or locations with limited internet access.

Overview​

In offline mode, OpenTranscribe operates without any internet connectivity:

  • ✅ Transcription works fully offline
  • ✅ Speaker diarization works offline
  • ✅ All AI models cached locally
  • ✅ No external API calls
  • ❌ YouTube downloads disabled (requires internet)
  • ❌ Cloud LLM providers disabled (use local LLM instead)

Prerequisites​

You'll need an internet-connected machine to:

  1. Download Docker images
  2. Download AI models (~2.5GB)
  3. Prepare installation package

Then transfer everything to your offline machine.

Step 1: Prepare on Internet-Connected Machine​

Download Docker Images​

# Pull all required images
docker pull davidamacey/opentranscribe-backend:latest
docker pull davidamacey/opentranscribe-frontend:latest
docker pull postgres:15-alpine
docker pull redis:7-alpine
docker pull minio/minio:latest
docker pull opensearchproject/opensearch:3.3.1

# Save images to tarball
docker save -o opentranscribe-images.tar \
  davidamacey/opentranscribe-backend:latest \
  davidamacey/opentranscribe-frontend:latest \
  postgres:15-alpine \
  redis:7-alpine \
  minio/minio:latest \
  opensearchproject/opensearch:3.3.1

Download AI Models​

# Set HuggingFace token
export HUGGINGFACE_TOKEN=hf_your_token_here

# Download models using Python
python3 << 'EOF'
from transformers import WhisperForConditionalGeneration, WhisperProcessor
from pyannote.audio import Model
import torch

# WhisperX models
WhisperForConditionalGeneration.from_pretrained("Systran/faster-whisper-large-v2")
WhisperProcessor.from_pretrained("Systran/faster-whisper-large-v2")

# PyAnnote models
Model.from_pretrained("pyannote/segmentation-3.0")
Model.from_pretrained("pyannote/speaker-diarization-3.1")
EOF

# Package model cache
tar -czf ai-models.tar.gz ~/.cache/huggingface ~/.cache/torch

Download Installation Files​

# Clone repository
git clone https://github.com/davidamacey/OpenTranscribe.git
cd OpenTranscribe

# Create offline package
tar -czf opentranscribe-offline.tar.gz \
  docker-compose.yml \
  docker-compose.offline.yml \
  .env.example \
  database/ \
  scripts/ \
  opentranscribe.sh

# Copy offline setup script
cp setup-opentranscribe.sh opentranscribe-offline-setup.sh

Step 2: Transfer to Offline Machine​

Transfer these files to offline machine:

  • opentranscribe-images.tar (~8GB)
  • ai-models.tar.gz (~2.5GB)
  • opentranscribe-offline.tar.gz (~5MB)

Via USB drive, secure file transfer, or your organization's approved method.

Step 3: Install on Offline Machine​

Load Docker Images​

# Load images
docker load -i opentranscribe-images.tar

Extract Installation Files​

# Extract installation
tar -xzf opentranscribe-offline.tar.gz
cd opentranscribe

# Extract AI models
mkdir -p models
tar -xzf ../ai-models.tar.gz -C models/

Configure Environment​

# Copy environment template
cp .env.example .env

# Edit .env and configure:
# - Set HUGGINGFACE_TOKEN (still required for model loading)
# - Set MODEL_CACHE_DIR=./models
# - Configure passwords and secrets
nano .env

Start Services​

# Make script executable
chmod +x opentranscribe.sh

# Start in offline mode
docker compose -f docker-compose.yml -f docker-compose.offline.yml up -d

# Or using the script
./opentranscribe.sh start offline

Offline Configuration​

The docker-compose.offline.yml file disables internet-dependent features:

  • YouTube download worker disabled
  • External network access restricted
  • Model downloads disabled (uses local cache)

Local LLM for Offline AI Features​

To use AI summarization in offline mode, deploy a local LLM:

Option 1: vLLM​

# On separate GPU (recommended)
docker run --gpus '"device=0"' -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-2-70b-chat-hf

Option 2: Ollama​

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull model (on internet machine, then copy)
ollama pull llama2:70b

Configure in .env:

LLM_PROVIDER=vllm  # or ollama
VLLM_API_URL=http://your-server:8000/v1

Updating Offline Installation​

To update an offline installation:

  1. On internet machine: Pull new Docker images
  2. Save to tarball
  3. Transfer to offline machine
  4. Load new images
  5. Restart services

Verification​

# Check all services running
docker compose ps

# Verify no internet access attempts
docker compose logs | grep -i "connect\|download"

# Test transcription
# Upload a test file through web UI

Limitations​

  • ❌ Cannot download YouTube videos
  • ❌ Cannot use cloud LLM providers (OpenAI, Claude, etc.)
  • ❌ Cannot auto-update models
  • ✅ All transcription features work
  • ✅ Speaker diarization works
  • ✅ Local LLM works (if configured)

Next Steps​