Bark AI MicroService

A micro service for running Suno's Bark model for text to speech generation. Built to interface with the acai.so AI toolkit for powering text to speech capabilities from your own compute.

You can run this via API for any UI you'd like to build out

Prerequisites

You will need the following tools:

• Python 3.9
• pip (Python package installer)
• Miniconda (Recommended)

Setup

Step 1

Clone the repository

git clone bark-service

Step 2

Navigate to the project directory

cd bark-service

Step 3 (Recommended)

We recommend using Miniconda to create an isolated Python environment for this project. If you haven't installed Miniconda yet, you can download it from here.

Create a new conda environment with Python 3.9

conda create -n myenv python=3.9

Activate the conda environment

conda activate myenv

Step 4

Install the required packages using pip

pip install --no-cache-dir -r requirements.txt

Docker Compose Setup

This project includes a docker-compose.yml file that can be used to create a Docker container for the application. Here are the steps to use Docker Compose:

Prerequisites

You will need the following tools:

• Docker
• Docker Compose

Step 1

Clone the repository

git clone <repository-url>

Step 2

Navigate to the project directory

cd /path/to/project

Step 3

Build and run the Docker container using Docker Compose

docker-compose up --build

The application will start running at http://0.0.0.0:5000

To stop the application, press Ctrl+C. To remove the Docker container, run docker-compose down.

Running the Application

Run the application with the following command:

python -m uvicorn main:app --host 0.0.0.0 --port 5000

The application will start running at http://0.0.0.0:5000

Bark Model

The suno bark model will begin downloading from Huggingface when the server is started if you don't have them cached

Once downloaded they will load into GPU memory. The suno/bark model uses ~5.5gb of VRAM while the suno/bark-small uses ~2.25gb.

We recommend the standard model for best quality, but if you'd like to try the small model then you can adjust the model name in main.py and restart the server

Calling the API

This application provides an API endpoint that you can use to run inference and get an audio response. Here is how you can call it:

POST /bark-inference

This endpoint accepts a JSON object with the following properties:

• text (string): The text to be converted to speech.
• voice (string, optional): The voice preset to be used in the speech synthesis

The endpoint returns an audio file in WAV format.

Example

Here is an example of how to call this endpoint using Python's requests library:

import requests
import json

url = "http://0.0.0.0:5000/bark-inference"
data = {
    "text": "Hello, world!",
    "voice": "en-US"
}
headers = {'Content-Type': 'application/json'}

response = requests.post(url, data=json.dumps(data), headers=headers)

# The response will be an audio file in WAV format.
with open('output.wav', 'wb') as out_file:
    out_file.write(response.content)

You can then play the output.wav file to hear the synthesized speech.

Using Axios (Node.js)

You can use the axios package in Node.js to make a POST request to the API:

const axios = require('axios');
const fs = require('fs');

const data = {
  text: 'Hello, world!',
  voice: 'en-US'
};

axios.post('http://0.0.0.0:5000/bark-inference', data, { responseType: 'arraybuffer' })
  .then((response) => {
    fs.writeFileSync('output.wav', response.data);
  })
  .catch((error) => {
    console.error(error);
  });

Using cURL

You can use curl in the command line to make a POST request to the API:

curl -X POST -H "Content-Type: application/json" -d '{"text":"Hello, world!","voice":"en-US"}' http://0.0.0.0:5000/bark-inference --output output.wav

This will save the response as a WAV file named output.wav.

License

This project is licensed under the MIT License.