alltalk_tts/DOCKER_README.md

Docker

The Docker image currently works on Windows and Linux supporting NVIDIA GPUs.

System requirements

• AMD compatible Windows or Linux system
• CUDA 12.8
• Nvidia Docker Container Toolkit
• At least 25GB of free disk space

Quickstart

Use docker-start.sh to pull the latest docker image using the XTTS model from Docker Hub and visit http://localhost:7851/. See instructions below for passing more arguments to the start script.

Installing Prerequisites

Docker for Linux

Ubuntu Specific Setup for GPUs

1. Make sure the latest nvidia drivers are installed: sudo ubuntu-drivers install
2. Install Docker your preferred way. One way to do it is to follow the official documentation here.
   • Start by uninstalling the old versions
   • Follow the "apt" repository installation method
   • Check that everything is working with the "hello-world" container
3. If, when launching the docker contain, you have an error message saying that the GPU cannot be used, you might have to install Nvidia Docker Container Toolkit.
   • Install with the "apt" method
   • Run the docker configuration command sudo nvidia-ctk runtime configure --runtime=docker
   • Restart docker

Docker for Windows (WSL2)

Windows Specific Setup for GPUs

Make sure your Nvidia drivers are up to date: https://www.nvidia.com/download/index.aspx

1. Install WSL2 in PowerShell with wsl --install and restart
2. Open PowerShell, type and enter ubuntu. It should now load you into wsl2
3. Remove the original nvidia cache key: sudo apt-key del 7fa2af80
4. Download CUDA toolkit keyring: wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
5. Install keyring: sudo dpkg -i cuda-keyring_1.1-1_all.deb
6. Update package list: sudo apt-get update
7. Install CUDA toolkit: sudo apt-get -y install cuda-toolkit-12-8
8. Install Docker Desktop using WSL2 as the backend
9. Restart
10. If you wish to monitor the terminal remotely via SSH, follow this guide.
11. Open PowerShell, type ubuntu, then follow below

Arguments for starting the Docker container

To make it as simple as possible to use Docker, the script docker-start.sh can be used. It provides the following optional arguments:

• --config lets you choose a config JSON file which can subset of confignew.json. This allows you to change only few values and leave the rest as defined in the default confignew.json file.
  • Example: docker-start.sh --config /my/config/file.json with content {"branding": "My Brand "} will just change the branding in confignew.json.
• --voices lets you add voices for the TTS engine in WAV format. You have to specify the folder containing all voice files.
  • Example: docker-start.sh --voices /my/voices/dir
• --rvc-voices similar to voices, this option lets you pick the folder containing the RVC models.
  • Example: docker-start.sh --rvc_voices /my/rvc/voices/dir
• --no-ui allows you to not expose port 7852 for the gradio interface and sets launch_gradio and gradio_interface to false.
• --auto-cleanup periodically deletes all generated WAV files that are older than 1min.
• --with-multi-engine-manager enables the use of the multi engine manager (MEM) which allows for more parallel requests. By default, one TTS engine is started. Optionally, you can pass the file path of a JSON file which can be a subset of mem_config.json with more fine-grained configuration options.
  • Example: docker-start.sh --with-multi_engine_manager to use MEM with default settings or docker-start.sh --with-multi_engine_manager /my/config/file.json to pass a JSON file containing more settings.
• --tag allows to choose the docker tag of the image to run. Defaults to latest-xtts.
  • Example: docker-start.sh --tag mytag
• --docker-repository allows to choose another Docker repository for pulling the image from. Use an empty string for the local repo.
• Since the above commands only address the most important options, you might pass additional arbitrary docker arguments to the docker-start.sh.

Of course, like for any other Docker image, you can also directly use docker run directly and use docker-start.sh as an inspiration.

Using environment variables for setting config values

Instead of using config JSON files, there are config settings that can be directly set using environment (ENV) variables when starting the application. This is also what is used internally in docker-start.sh.

Since docker-start.sh allows to pass arbitrary params, it can also be used to set env variables, e.g:

./docker-start.sh -e ALLTALK_BRANDING="My Brand " -e ALLTALK_API_PORT_NUMBER=9876

or when using Docker directly:

docker run -e ALLTALK_BRANDING="My Brand " -e ALLTALK_API_PORT_NUMBER=9876 ...

Consult docker_default_confignew.json and docker_default_mem_config.json for a list of all available env variables.

Building your own images (advanced)

Be aware that building the images requires good hardware and bandwidth. Expect this process to require > 40 GB of disk space and > 30min execution time depending on your hardware.

Under normal circumstances, there should be no need to build the Docker images locally. However, if needed for some reason, you may want to use docker-build.sh with the following arguments:

• --tts_model allows to choose the TTS model that is used by default. Valid values are piper, vits, xtts. Defaults to xtts.
  • Example: docker-build.sh --tts_model piper
• --tag allows to choose the docker tag. Defaults to latest-xtts.
  • Example: docker-build.sh --tag mytag
• --docker-repository allows to choose another Docker repository for tagging the image from. Use an empty string for the local repo.
• --local-deepspeed-build should only be used in rare cases where DeepSpeed needs to be rebuilt. Defaults to false.
• --clean allows remove existing dependency build like conda environment or DeepSpeed. Defaults to false.
  • Example: docker-build.sh --clean

Older images

• CUDA 12.6:
  • XTTS Dockerhub hash: sha256:fd4741714af0c82e4e7a66bf9ae47c98f3e51ebefdb3af5795b47d41ca5fb159
  • VITS Dockerhub hash: sha256:9f411e8fdb997486bc217fcd316bf6b163c39736655ea3400028ad4fcc5c60f9
  • Piper: Dockerhub hash: sha256:6ae2520051bb00a0356dc4ed80a33bf6784cb567b59759af46046ddcf356b765