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Expand Up @@ -4,141 +4,128 @@ BBB-Translation-Bot is a tool designed to enhance communication in BigBlueButton

# Getting Started

Follow these simple steps to quickly set up the BBB-Translation-Bot.
First of all you need the right Haardware. Im using a [NVIDIA RTX4090](https://www.nvidia.com/de-de/geforce/graphics-cards/40-series/rtx-4090/). Where I get a stable transcription speed of 2,1 seconds in english with the large-v3 version of Whisper. And thats it. Now lets staart with the softwaare part. Dont worry. I will go through the whole process stepo by step. Also you dont have to install any drivers. I will show you how to do this. You just need a fresh installation of Ubuntu22 and root access. And dont be to ambitious and clone this project in advance. We will do this together. Just keep reading and dont skip any steps.

## Prerequisites
For both parth I recomend using [Ubuntu22](https://releases.ubuntu.com/jammy/). At the moment Im using Faster Whisper which needs cuDNN 8.x. And this is only supported on Ubuntu22. I also recomend using Proxmox to setup a virtual mashin with ubuntu22. Im showing you how to to this with GPU pathtrough on this README-proxmox.md

### Hardware
This setup was testet with a Nvidia RTX 2070 and RTX 3070 GPU. The GPU is used for the transcription and translation of the audio stream.
You now have to options. You can get this up and running using docker, or setup an dev environment and run it with docker or normal in multiple screen seasions.

There are two ways in getting started. If you just want to get this project up and running continue reading here.
If you want to setup aa developer environment continue reaading here.

## Simple setup (no dev!!!)

I have provided a makefile to make the setup process easier. You can run the following commands to get the project up and running.

### Install Nvidia drivers for Ubuntu:
Refer to the official [Nvidia documentation](https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html#ubuntu-lts):
```bash
sudo apt update
sudo apt install linux-headers-$(uname -r)
distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt update
sudo apt -y install cuda-drivers
sudo reboot now
git clone https://github.com/bigbluebutton-bot/bbb-translation-bot
cd bbb-translation-bot
sudo make simple-setup
```

### Docker with GPU support
Ensure Docker with GPU support is installed on your system:
Refer to the official [Nvidia documentation](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
This will do the following:
- Update System Packages
- Install nvidia drivers
- Reboot the system
- Install docker
- Install docker with nvidia support

The script will reboot the system. Dont worry. It will automaticly continue after the reboot. To see the status of the script after the reboot you can run the following command:

```bash
sudo apt update
sudo su
screen -r
```

curl -sSL https://get.docker.com | sh
If the setup script is completed you can run the following command to start the bot:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
&& \
sudo apt-get update
```bash
make start
```

sudo apt install nvidia-container-runtime
Because this will be the first time starting the bot it will detect, there is no '.env' file. So it will ask you some questions to be able to connect to your BigBlueButton server. You will need the domain and the BBB sectet. To get the secret ssh into you BBB server and run:

```bash
sudo bbb-conf --secret
```

which nvidia-container-runtime-hook
## Developer setup

sudo systemctl restart docker
If you want to develop on Windows WSL. This is ppossible. Continue reading at the WSL section.

docker run -it --rm --gpus all ubuntu nvidia-smi # Test GPU support
If you want to setup a developer environment you can run the following commands:

```bash
git clone --recurse-submodules -j8 https://github.com/bigbluebutton-bot/bbb-translation-bot
cd bbb-translation-bot
sudo make dev-setup
```

### Installation
1. Clone the repository:
```bash
git clone https://github.com/bigbluebutton-bot/bbb-translation-bot
cd bbb-translation-bot
```
This will do the following:
- Update System Packages
- Install NVIDIA Drivers
- Install NVIDIA CUDA
- Reboot
- Install NVIDIA cuDNN 8.9.7
- Install Docker
- Install golang
- Install python3

2. Configure the bot:
Copy the example configuration file and modify it according to your preferences:
```bash
cp .env_example .env
```
The script will reboot the system. Dont worry. It will automaticly continue after the reboot. To see the status of the script after the reboot you can run the following command:

3. Launch the bot:
With Docker, you can easily start the bot:
```bash
docker-compose up -d
```
```bash
sudo su
screen -r
```

# Development Setup
To set up the environment for development purposes, follow the instructions below.
## Server Setup
1. Install Nvidia drivers for Ubuntu:
Refer to the official [Nvidia documentation](https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html#ubuntu-lts):
```bash
sudo apt install linux-headers-$(uname -r)
distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt update
sudo apt -y install cuda-drivers
sudo reboot now
```

2. Install Python dependencies:
```bash
cd server
sudo apt update
sudo apt install python3-pip python3-dev ffmpeg -y
python3 -m venv .translation-server
source .translation-server/bin/activate
pip3 install -r requirements-server.txt --no-cache-dir
```

3. Run the server:
```bash
python3 server.py
```

4. Exit the virtual environment:
```bash
deactivate
```
## WSL

5. Automate the creation of a RAM disk on system startup:
Add the following line to your `/etc/fstab` file to set up a tmpfs RAM disk automatically on system boot. This allows for faster data processing and reduces wear on physical drives.
```bash
sudo nano /etc/fstab
# Add this line at the end of the file:
tmpfs /mnt/ramdisk tmpfs nodev,nosuid,noexec,nodiratime,size=512M 0 0
# Create the mount directory if it does not exist:
sudo mkdir -p /mnt/ramdisk
```
If you want to develop on Windows WSL. This is possible. You can follow the following steps to get the project up and running.

1. Install your [NVIDIA drivers](https://www.nvidia.com/en-us/drivers/) on your Windows machine. Accouding to this [article](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#getting-started-with-cuda-on-wsl) WSL 2 should then have access to the drivers.
2. Install WSL2. Fo that open PowerShell as an administrator and run the following command:

## Client Setup
1. Install Golang:
Follow the official [Golang installation guide](https://go.dev/doc/install):
```bash
cd client
sudo apt update
wget https://go.dev/dl/go1.21.3.linux-amd64.tar.gz
sudo rm -rf /usr/local/go && sudo tar -C /usr/local -xzf go1.21.3.linux-amd64.tar.gz
export PATH=$PATH:/usr/local/go/bin
wsl --install
```

2. Install node.js:
Use [Node Version Manager](https://github.com/nvm-sh/nvm) to install Node.js:
```bash
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.5/install.sh | bash
export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" # This loads nvm
nvm install node
```
3. Install Ubuntu22. You can do this by opening the [Microsoft Store](https://apps.microsoft.com/detail/9pn20msr04dw) and searching for Ubuntu22. Click on install and wait for the installation to complete.
4. Go and downloade docker desktop from the [docker website](https://www.docker.com/products/docker-desktop). Install it and make sure to enable the WSL2 integration. Now open the Ubuntu22 terminal and try running the `docker` command.

<details>
<summary>Fix for error: `The command 'docker' could not be found in this WSL 2 distro.`</summary>
If you get an error like this:
3. Install Go dependencies:
```bash
go get .
$ docker

The command 'docker' could not be found in this WSL 2 distro.
We recommend to activate the WSL integration in Docker Desktop settings.

For details about using Docker Desktop with WSL 2, visit:

https://docs.docker.com/go/wsl2/
```
4. Run the client:
Open docker desktop and go to settings. Then go to the resources tab and click on WSL integration. Enable the integration for Ubuntu22.
![docker-settings](docs/imgs/enable-docker-in-wsl.png)
Thanks to this [post](https://stackoverflow.com/questions/63497928/ubuntu-wsl-with-docker-could-not-be-found).
</details>
5. Open the Ubuntu22 terminal and run the following command to update the system:
```bash
go run .
```
git clone --recurse-submodules -j8 https://github.com/bigbluebutton-bot/bbb-translation-bot
cd bbb-translation-bot
sudo make dev-setup-wsl


sudo apt install nvidia-cuda-dev -y
sudo apt install nvidia-cuda-toolkit -y

```
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