Added whisper

2025-10-11 23:13:08 +02:00
parent 2b218eeddc
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--- a/Readme_FasterWhisper.md
+++ b/Readme_FasterWhisper.md
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+# Faster Whisper - Audio Transcription Service
+
+Audio transcription service using Faster Whisper with GPU acceleration (NVIDIA).
+
+## 📋 Prerequisites
+
+- Windows with WSL2 (Ubuntu 24.04)
+- Docker Desktop for Windows with WSL2 backend
+- NVIDIA GPU with drivers installed on Windows
+- NVIDIA Container Toolkit configured in WSL2
+- Access to mounted volumes (`/mnt/e/volumes/faster-whisper/`)
+
+### WSL2 GPU Setup
+
+Ensure your WSL2 Ubuntu has access to the NVIDIA GPU:
+
+```bash
+# Check GPU availability in WSL2
+nvidia-smi
+
+# If not available, install NVIDIA Container Toolkit in WSL2
+distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
+curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
+curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+
+sudo apt-get update
+sudo apt-get install -y nvidia-container-toolkit
+sudo systemctl restart docker
+```
+
+## 🚀 Quick Start
+
+```bash
+# Start the service
+docker compose up -d faster-whisper
+
+# Check logs
+docker logs faster-whisper -f
+
+# Stop the service
+docker compose down
+```
+
+## ⚙️ Configuration
+
+### Environment Variables
+
+| Variable | Value | Description |
+|----------|-------|-------------|
+| `PUID` | 1000 | User ID for file permissions |
+| `PGID` | 1000 | Group ID for file permissions |
+| `TZ` | Europe/Paris | Timezone |
+| `WHISPER_MODEL` | turbo | Model to use (tiny, base, small, medium, large, turbo) |
+| `WHISPER_LANG` | fr | Transcription language |
+| `WHISPER_BEAM` | 5 | Beam search size (1-10, accuracy vs speed tradeoff) |
+
+### Available Models
+
+| Model | Size | VRAM | Speed | Accuracy |
+|-------|------|------|-------|----------|
+| `tiny` | ~75 MB | ~1 GB | Very fast | Low |
+| `base` | ~142 MB | ~1 GB | Fast | Medium |
+| `small` | ~466 MB | ~2 GB | Medium | Good |
+| `medium` | ~1.5 GB | ~5 GB | Slow | Very good |
+| `large` | ~2.9 GB | ~10 GB | Very slow | Excellent |
+| `turbo` | ~809 MB | ~6 GB | Fast | Excellent |
+
+> **Note:** The `turbo` model is an excellent compromise for RTX 4060 Ti (8 GB VRAM).
+
+### Volumes
+
+- `/mnt/e/volumes/faster-whisper/audio` → `/app` : Audio files directory to transcribe
+- `/mnt/e/volumes/faster-whisper/models` → `/root/.cache/whisper` : Downloaded models cache
+
+> **Windows Note:** The path `/mnt/e/` in WSL2 corresponds to `E:\` drive on Windows.
+
+## 🎯 Usage
+
+### REST API
+
+The service exposes a REST API on port **10300**.
+
+#### Transcribe an audio file
+
+```bash
+# Place the file in /mnt/e/volumes/faster-whisper/audio/
+# Or on Windows: E:\volumes\faster-whisper\audio\
+
+# From WSL2:
+curl -X POST http://localhost:10300/transcribe \
+  -F "file=@audio.mp3"
+
+# From Windows PowerShell:
+curl.exe -X POST http://localhost:10300/transcribe -F "file=@audio.mp3"
+```
+
+#### Check service status
+
+```bash
+curl http://localhost:10300/health
+```
+
+### Web Interface
+
+Access the web interface: `http://localhost:10300`
+
+The interface is accessible from both Windows and WSL2.
+
+## 🔧 Administration
+
+### Check GPU Usage
+
+```bash
+# From WSL2 host
+nvidia-smi
+
+# From inside the container
+docker exec faster-whisper nvidia-smi
+
+# Monitor GPU in real-time
+watch -n 1 nvidia-smi
+```
+
+### Update the Image
+
+```bash
+docker compose pull faster-whisper
+docker compose up -d faster-whisper
+```
+
+### Change Model
+
+1. Edit `WHISPER_MODEL` in docker-compose.yml
+2. Restart the container:
+   ```bash
+   docker compose up -d faster-whisper
+   ```
+
+The new model will be downloaded automatically on first startup.
+
+### Performance Optimization
+
+#### Adjust Beam Search
+
+- `WHISPER_BEAM=1`: Maximum speed, reduced accuracy
+- `WHISPER_BEAM=5`: Good compromise (default)
+- `WHISPER_BEAM=10`: Maximum accuracy, slower
+
+#### Monitor Memory Usage
+
+```bash
+docker stats faster-whisper
+```
+
+### Clean Old Models
+
+Models are stored in `/mnt/e/volumes/faster-whisper/models/` (WSL2) or `E:\volumes\faster-whisper\models\` (Windows).
+
+```bash
+# From WSL2 - List downloaded models
+ls -lh /mnt/e/volumes/faster-whisper/models/
+
+# Delete an unused model
+rm -rf /mnt/e/volumes/faster-whisper/models/<model-name>
+```
+
+```powershell
+# From Windows PowerShell
+Get-ChildItem E:\volumes\faster-whisper\models\
+
+# Delete an unused model
+Remove-Item -Recurse E:\volumes\faster-whisper\models\<model-name>
+```
+
+## 📊 Monitoring
+
+### Real-time Logs
+
+```bash
+docker logs faster-whisper -f --tail 100
+```
+
+### Check Container Status
+
+```bash
+docker ps | grep faster-whisper
+```
+
+### Restart on Issues
+
+```bash
+docker restart faster-whisper
+```
+
+## 🐛 Troubleshooting
+
+### Container Won't Start
+
+1. Verify NVIDIA Container Toolkit is installed in WSL2:
+   ```bash
+   docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu22.04 nvidia-smi
+   ```
+
+2. Check permissions on volumes:
+   ```bash
+   ls -la /mnt/e/volumes/faster-whisper/
+   ```
+
+3. Ensure Docker Desktop WSL2 integration is enabled:
+   - Open Docker Desktop → Settings → Resources → WSL Integration
+   - Enable integration with Ubuntu-24.04
+
+### "Out of Memory" Error
+
+- Reduce the model (e.g., from `turbo` to `small`)
+- Reduce `WHISPER_BEAM` to 3 or 1
+- Close other GPU-intensive applications on Windows
+- Check GPU memory usage: `nvidia-smi`
+
+### Poor Transcription Quality
+
+- Increase the model (e.g., from `small` to `turbo`)
+- Increase `WHISPER_BEAM` to 7 or 10
+- Check audio quality of source file
+- Verify the correct language is set in `WHISPER_LANG`
+
+### WSL2 Specific Issues
+
+#### GPU Not Detected
+
+```bash
+# Check Windows GPU driver version (from PowerShell)
+nvidia-smi
+
+# Update WSL2 kernel
+wsl --update
+
+# Restart WSL2
+wsl --shutdown
+# Then reopen Ubuntu
+```
+
+#### Volume Access Issues
+
+```bash
+# Check if drive is mounted in WSL2
+ls /mnt/e/
+
+# If not mounted, add to /etc/wsl.conf
+sudo nano /etc/wsl.conf
+
+# Add these lines:
+[automount]
+enabled = true
+options = "metadata,uid=1000,gid=1000"
+
+# Restart WSL2
+wsl --shutdown
+```
+
+## 📁 File Structure
+
+```
+Windows: E:\volumes\faster-whisper\
+WSL2:    /mnt/e/volumes/faster-whisper/
+├── audio/          # Audio files to transcribe
+└── models/         # Whisper models cache
+```
+
+## 🪟 Windows Integration
+
+### Access Files from Windows Explorer
+
+- Navigate to `\\wsl$\Ubuntu-24.04\mnt\e\volumes\faster-whisper\`
+- Or directly to `E:\volumes\faster-whisper\`
+
+### Copy Files to Transcribe
+
+From Windows:
+```powershell
+Copy-Item "C:\path\to\audio.mp3" -Destination "E:\volumes\faster-whisper\audio\"
+```
+
+From WSL2:
+```bash
+cp /mnt/c/path/to/audio.mp3 /mnt/e/volumes/faster-whisper/audio/
+```
+
+## 🔗 Useful Links
+
+- [LinuxServer Docker Image Documentation](https://docs.linuxserver.io/images/docker-faster-whisper)
+- [Faster Whisper GitHub](https://github.com/SYSTRAN/faster-whisper)
+- [OpenAI Whisper Documentation](https://github.com/openai/whisper)
+- [WSL2 GPU Support](https://docs.microsoft.com/en-us/windows/wsl/tutorials/gpu-compute)
+
+## 📝 Notes
+
+- Service automatically restarts unless manually stopped (`restart: unless-stopped`)
+- On first startup, the model will be downloaded (may take a few minutes)
+- Supported audio formats: MP3, WAV, M4A, FLAC, OGG, etc.
+- The service runs in WSL2 but is accessible from Windows
+- GPU computations are performed on the Windows NVIDIA GPU
--- a/Readme_Ollama.md
+++ b/Readme_Ollama.md
@@ -0,0 +1,541 @@
+# Ollama Docker Setup 🦙 (WSL2 + Windows 11)
+
+Complete guide for running Ollama with Docker Compose and GPU acceleration on WSL2.
+
+## 📋 Table of Contents
+
+- [Prerequisites](#prerequisites)
+- [WSL2 Setup](#wsl2-setup)
+- [Installation](#installation)
+- [Starting Ollama](#starting-ollama)
+- [Model Management](#model-management)
+- [Usage Examples](#usage-examples)
+- [API Reference](#api-reference)
+- [Troubleshooting](#troubleshooting)
+- [Performance Tips](#performance-tips)
+
+## 🔧 Prerequisites
+
+### Required Software
+
+- **Windows 11** with WSL2 enabled
+- **Ubuntu 24.04** on WSL2
+- **Docker Desktop for Windows** with WSL2 backend
+- **NVIDIA GPU** with CUDA support (RTX series recommended)
+- **NVIDIA Driver** for Windows (latest version)
+
+### System Requirements
+
+- Windows 11 Build 22000 or higher
+- 16GB RAM minimum (32GB recommended for larger models)
+- 50GB+ free disk space for models
+- NVIDIA GPU with 8GB+ VRAM
+
+## 🪟 WSL2 Setup
+
+### 1. Enable WSL2 (if not already done)
+
+```powershell
+# Run in PowerShell as Administrator
+wsl --install
+wsl --set-default-version 2
+
+# Install Ubuntu 24.04
+wsl --install -d Ubuntu-24.04
+
+# Verify WSL2 is active
+wsl --list --verbose
+```
+
+### 2. Install Docker Desktop for Windows
+
+1. Download from [Docker Desktop](https://www.docker.com/products/docker-desktop)
+2. Install and enable **WSL2 backend** in settings
+3. Enable integration with Ubuntu-24.04 distro in: Settings → Resources → WSL Integration
+
+### 3. Verify GPU Support in WSL2
+
+```bash
+# Open WSL2 Ubuntu terminal
+wsl
+
+# Check NVIDIA driver
+nvidia-smi
+
+# You should see your GPU listed
+```
+
+**Important**: You do NOT need to install NVIDIA Container Toolkit in WSL2. Docker Desktop handles GPU passthrough automatically.
+
+### 4. Test Docker GPU Access
+
+```bash
+# In WSL2 terminal
+docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
+```
+
+If this works, you're ready to go! 🎉
+
+## 🚀 Installation
+
+### 1. Create Project Structure in WSL2
+
+```bash
+# Open WSL2 terminal
+wsl
+
+# Create project directory
+mkdir -p ~/ollama-docker
+cd ~/ollama-docker
+```
+
+### 2. Create `docker-compose.yml`
+
+Use the provided `docker-compose.yml` file with the WSL2 path:
+- Windows path: `E:\volumes\ollama\data`
+- WSL2 path: `/mnt/e/volumes/ollama/data`
+
+### 3. Create Volume Directory
+
+```bash
+# From WSL2 terminal
+sudo mkdir -p /mnt/e/volumes/ollama/data
+
+# Or from Windows PowerShell
+mkdir E:\volumes\ollama\data
+```
+
+## ▶️ Starting Ollama
+
+```bash
+# Navigate to project directory
+cd ~/ollama-docker
+
+# Start the service
+docker compose up -d
+
+# Check logs
+docker compose logs -f ollama
+
+# Verify service is running
+curl http://localhost:11434
+```
+
+Expected response: `Ollama is running`
+
+### Access from Windows
+
+Ollama is accessible from both WSL2 and Windows:
+- **WSL2**: `http://localhost:11434`
+- **Windows**: `http://localhost:11434`
+
+## 📦 Model Management
+
+### List Available Models
+
+```bash
+# Inside container
+docker exec -it ollama ollama list
+
+# Or from WSL2 (if ollama CLI installed)
+ollama list
+```
+
+### Pull/Download Models
+
+```bash
+# Pull a model
+docker exec -it ollama ollama pull llama3.2
+
+# Popular models
+docker exec -it ollama ollama pull mistral
+docker exec -it ollama ollama pull codellama
+docker exec -it ollama ollama pull phi3
+docker exec -it ollama ollama pull llama3.2:70b
+```
+
+### Model Sizes Reference
+
+| Model | Parameters | Size | RAM Required | VRAM Required |
+|-------|-----------|------|--------------|---------------|
+| `phi3` | 3.8B | ~2.3 GB | 8 GB | 4 GB |
+| `llama3.2` | 8B | ~4.7 GB | 8 GB | 6 GB |
+| `mistral` | 7B | ~4.1 GB | 8 GB | 6 GB |
+| `llama3.2:70b` | 70B | ~40 GB | 64 GB | 48 GB |
+| `codellama` | 7B | ~3.8 GB | 8 GB | 6 GB |
+
+### Remove/Unload Models
+
+```bash
+# Remove a model from disk
+docker exec -it ollama ollama rm llama3.2
+
+# Stop a running model (unload from memory)
+docker exec -it ollama ollama stop llama3.2
+
+# Show running models
+docker exec -it ollama ollama ps
+```
+
+### Copy Models Between Systems
+
+```bash
+# Export model
+docker exec ollama ollama show llama3.2 --modelfile > Modelfile
+
+# Import on another system
+cat Modelfile | docker exec -i ollama ollama create my-model -f -
+```
+
+## 💡 Usage Examples
+
+### Interactive Chat
+
+```bash
+# Start interactive session
+docker exec -it ollama ollama run llama3.2
+
+# Chat with specific model
+docker exec -it ollama ollama run mistral "Explain quantum computing"
+```
+
+### Using the API
+
+#### Generate Completion
+
+```bash
+curl http://localhost:11434/api/generate -d '{
+  "model": "llama3.2",
+  "prompt": "Why is the sky blue?",
+  "stream": false
+}'
+```
+
+#### Chat Completion
+
+```bash
+curl http://localhost:11434/api/chat -d '{
+  "model": "llama3.2",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Hello! Can you help me with Python?"
+    }
+  ],
+  "stream": false
+}'
+```
+
+#### Streaming Response
+
+```bash
+curl http://localhost:11434/api/generate -d '{
+  "model": "llama3.2",
+  "prompt": "Write a haiku about programming",
+  "stream": true
+}'
+```
+
+### Python Example (from Windows or WSL2)
+
+```python
+import requests
+import json
+
+def chat_with_ollama(prompt, model="llama3.2"):
+    url = "http://localhost:11434/api/generate"
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "stream": False
+    }
+    
+    response = requests.post(url, json=payload)
+    return response.json()["response"]
+
+# Usage
+result = chat_with_ollama("What is Docker?")
+print(result)
+```
+
+### JavaScript Example (from Windows or WSL2)
+
+```javascript
+async function chatWithOllama(prompt, model = "llama3.2") {
+    const response = await fetch("http://localhost:11434/api/generate", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+            model: model,
+            prompt: prompt,
+            stream: false
+        })
+    });
+    
+    const data = await response.json();
+    return data.response;
+}
+
+// Usage
+chatWithOllama("Explain REST APIs").then(console.log);
+```
+
+## 🔌 API Reference
+
+### Main Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/api/generate` | POST | Generate text completion |
+| `/api/chat` | POST | Chat completion with conversation history |
+| `/api/tags` | GET | List available models |
+| `/api/pull` | POST | Download a model |
+| `/api/push` | POST | Upload a custom model |
+| `/api/embeddings` | POST | Generate embeddings |
+
+### Generate Parameters
+
+```json
+{
+  "model": "llama3.2",
+  "prompt": "Your prompt here",
+  "stream": false,
+  "options": {
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "top_k": 40,
+    "num_predict": 128,
+    "stop": ["\n"]
+  }
+}
+```
+
+## 🐛 Troubleshooting
+
+### Container Won't Start
+
+```bash
+# Check logs
+docker compose logs ollama
+
+# Common issues:
+# 1. GPU not accessible
+docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
+
+# 2. Port already in use
+netstat -ano | findstr :11434  # From Windows PowerShell
+ss -tulpn | grep 11434         # From WSL2
+```
+
+### GPU Not Detected in WSL2
+
+```powershell
+# Update NVIDIA driver (from Windows)
+# Download latest driver from: https://www.nvidia.com/Download/index.aspx
+
+# Restart WSL2 (from PowerShell)
+wsl --shutdown
+wsl
+
+# Verify GPU
+nvidia-smi
+```
+
+### Model Download Fails
+
+```bash
+# Check disk space
+docker exec ollama df -h /root/.ollama
+
+# Check WSL2 disk space
+df -h /mnt/e
+
+# Retry with verbose logging
+docker exec -it ollama ollama pull llama3.2 --verbose
+```
+
+### Out of Memory Errors
+
+```bash
+# Check GPU memory
+nvidia-smi
+
+# Use smaller model or reduce context
+docker exec ollama ollama run llama3.2 --num-ctx 2048
+```
+
+### WSL2 Disk Space Issues
+
+```powershell
+# Compact WSL2 virtual disk (from PowerShell as Admin)
+wsl --shutdown
+Optimize-VHD -Path "$env:LOCALAPPDATA\Packages\CanonicalGroupLimited.Ubuntu24.04LTS_*\LocalState\ext4.vhdx" -Mode Full
+```
+
+### Docker Desktop Integration Issues
+
+1. Open Docker Desktop
+2. Go to **Settings → Resources → WSL Integration**
+3. Enable integration with **Ubuntu-24.04**
+4. Click **Apply & Restart**
+
+### Permission Denied on Volume
+
+```bash
+# From WSL2
+sudo chmod -R 755 /mnt/e/volumes/ollama/data
+```
+
+## ⚡ Performance Tips
+
+### 1. WSL2 Memory Configuration
+
+Create/edit `.wslconfig` in Windows user directory (`C:\Users\YourName\.wslconfig`):
+
+```ini
+[wsl2]
+memory=16GB
+processors=8
+swap=8GB
+```
+
+Apply changes:
+```powershell
+wsl --shutdown
+wsl
+```
+
+### 2. GPU Memory Optimization
+
+```yaml
+# In docker-compose.yml
+environment:
+  - CUDA_VISIBLE_DEVICES=0
+  - OLLAMA_NUM_GPU=1
+```
+
+### 3. Concurrent Requests
+
+```yaml
+# In docker-compose.yml
+environment:
+  - OLLAMA_MAX_LOADED_MODELS=3
+  - OLLAMA_NUM_PARALLEL=4
+```
+
+### 4. Context Window
+
+```bash
+# Reduce for faster responses
+docker exec ollama ollama run llama3.2 --num-ctx 2048
+
+# Increase for longer conversations
+docker exec ollama ollama run llama3.2 --num-ctx 8192
+```
+
+### 5. Model Quantization
+
+Use quantized models for better performance:
+```bash
+# 4-bit quantization (faster, less accurate)
+docker exec ollama ollama pull llama3.2:q4_0
+
+# 8-bit quantization (balanced)
+docker exec ollama ollama pull llama3.2:q8_0
+```
+
+### 6. Store Models on SSD
+
+For best performance, ensure `E:\volumes` is on an SSD, not HDD.
+
+## 📊 Monitoring
+
+### Check Resource Usage
+
+```bash
+# Container stats
+docker stats ollama
+
+# GPU utilization (from WSL2 or Windows)
+nvidia-smi
+
+# Continuous monitoring
+watch -n 1 nvidia-smi
+```
+
+### Model Status
+
+```bash
+# Show running models
+docker exec ollama ollama ps
+
+# Model information
+docker exec ollama ollama show llama3.2
+```
+
+### WSL2 Resource Usage
+
+```powershell
+# From Windows PowerShell
+wsl --list --verbose
+```
+
+## 🛑 Stopping and Cleanup
+
+```bash
+# Stop service
+docker compose down
+
+# Stop and remove volumes
+docker compose down -v
+
+# Remove all models
+docker exec ollama sh -c "rm -rf /root/.ollama/models/*"
+
+# Shutdown WSL2 (from Windows PowerShell)
+wsl --shutdown
+```
+
+## 🔗 Useful Links
+
+- [Ollama Official Documentation](https://github.com/ollama/ollama)
+- [Ollama Model Library](https://ollama.com/library)
+- [API Documentation](https://github.com/ollama/ollama/blob/main/docs/api.md)
+- [WSL2 GPU Documentation](https://learn.microsoft.com/en-us/windows/wsl/tutorials/gpu-compute)
+- [Docker Desktop WSL2 Backend](https://docs.docker.com/desktop/wsl/)
+
+## 🎯 Quick Reference
+
+### Common Commands
+
+```bash
+# Start Ollama
+docker compose up -d
+
+# Pull a model
+docker exec -it ollama ollama pull llama3.2
+
+# Run interactive chat
+docker exec -it ollama ollama run llama3.2
+
+# List models
+docker exec -it ollama ollama list
+
+# Check GPU
+nvidia-smi
+
+# Stop Ollama
+docker compose down
+```
+
+## 📝 Notes for WSL2 Users
+
+- **Path Conversion**: Windows `E:\folder` = WSL2 `/mnt/e/folder`
+- **Performance**: Models stored on Windows drives are accessible but slightly slower
+- **GPU Passthrough**: Handled automatically by Docker Desktop
+- **Networking**: `localhost` works from both Windows and WSL2
+- **Memory**: Configure WSL2 memory in `.wslconfig` for large models
+
+---
+
+**Need help?** Open an issue or check the [Ollama Discord](https://discord.gg/ollama)
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,51 @@
+services:
+  ollama:
+    image: ollama/ollama:latest
+    container_name: ollama
+    restart: unless-stopped
+    ports:
+      - "11434:11434"
+    volumes:
+      - /mnt/e/volumes/ollama/data:/root/.ollama
+    environment:
+      - OLLAMA_HOST=0.0.0.0:11434
+      # Optional: Set GPU device if you have multiple GPUs
+      # - NVIDIA_VISIBLE_DEVICES=0
+    command: serve
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    networks:
+      - app-network
+    healthcheck:
+      test: ["CMD", "ollama", "list"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s
+
+  faster-whisper:
+    image: lscr.io/linuxserver/faster-whisper:gpu-legacy
+    container_name: faster-whisper
+    gpus: all
+    environment:
+      - PUID=1000
+      - PGID=1000
+      - TZ=Europe/Paris
+      - WHISPER_MODEL=turbo    # bon compromis pour RTX 4060 Ti
+      - WHISPER_LANG=fr
+      - WHISPER_BEAM=5         # précision vs rapidité
+    volumes:
+      - /mnt/e/volumes/faster-whisper/audio:/app
+      - /mnt/e/volumes/faster-whisper/models:/root/.cache/whisper
+    ports:
+      - 10300:10300
+    restart: unless-stopped
+
+networks:
+  app-network:
+    driver: bridge