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Hi π€ In this video, you'll build a full-stack ElevenLabs clone with text-to-speech, voice conversion, and audio generation. Instead of external API services, you'll self-host three AI models (StyleTTS2, Seed-VC, and Make-An-Audio) from GitHub, fine-tune them to specific voices, then containerize them with Docker and expose inference endpoints via FastAPI. The AI backend will be built using Python and PyTorch. You'll create a Next.js application where users can use the AI models to generate audio, and also switch between voices and view previously generated audio files, stored in an S3 bucket. The project includes user authentication, a credit system, and an Inngest queue to prevent overloading of the server hosting the AI models. The web application is built on the T3 Stack with Next.js, React, Tailwind, and Auth.js. Follow along for the entire process from development to deployment.
Features:
Follow these steps to install and set up the project.
git clone https://github.com/Andreaswt/elevenlabs-clone.git
cd elevenlabs-clone
Download and install Python if not already installed. Use the link below for guidance on installation: Python Download
Create a virtual environment for each folder, except elevenlabs-clone-frontend, with Python 3.10.
Next.js frontend:
cd elevenlabs-clone-frontend
npm i
Folders with AI models:
cd seed-vc # For example
pip install -r requirements.txt
Add custom policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:PutObject",
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::elevenlabs-clone",
"arn:aws:s3:::elevenlabs-clone/*"
]
}
]
}
Permissions:
Add custom policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:PutObject",
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::elevenlabs-clone",
"arn:aws:s3:::elevenlabs-clone/*"
]
}
]
}
6 commits
Python
79.2%
Jupyter Notebook
15.2%
TypeScript
4.7%
![]()
Hi π€ In this video, you'll build a full-stack ElevenLabs clone with text-to-speech, voice conversion, and audio generation. Instead of external API services, you'll self-host three AI models (StyleTTS2, Seed-VC, and Make-An-Audio) from GitHub, fine-tune them to specific voices, then containerize them with Docker and expose inference endpoints via FastAPI. The AI backend will be built using Python and PyTorch. You'll create a Next.js application where users can use the AI models to generate audio, and also switch between voices and view previously generated audio files, stored in an S3 bucket. The project includes user authentication, a credit system, and an Inngest queue to prevent overloading of the server hosting the AI models. The web application is built on the T3 Stack with Next.js, React, Tailwind, and Auth.js. Follow along for the entire process from development to deployment.
Features:
Follow these steps to install and set up the project.
git clone https://github.com/Andreaswt/elevenlabs-clone.git
cd elevenlabs-clone
Download and install Python if not already installed. Use the link below for guidance on installation: Python Download
Create a virtual environment for each folder, except elevenlabs-clone-frontend, with Python 3.10.
Next.js frontend:
cd elevenlabs-clone-frontend
npm i
Folders with AI models:
cd seed-vc # For example
pip install -r requirements.txt
Add custom policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:PutObject",
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::elevenlabs-clone",
"arn:aws:s3:::elevenlabs-clone/*"
]
}
]
}
Permissions:
Add custom policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:PutObject",
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::elevenlabs-clone",
"arn:aws:s3:::elevenlabs-clone/*"
]
}
]
}
6 commits
Python
79.2%
Jupyter Notebook
15.2%
TypeScript
4.7%