Introduction
OmniVoice Studio is an open-source, local alternative to cloud-based voice cloning services like ElevenLabs. It enables you to perform voice cloning, video dubbing, real-time dictation, and speaker diarization entirely on your own hardware. This tutorial will guide you through setting up OmniVoice Studio on your local machine, demonstrating how to use its core features including text-to-speech (TTS) and integration with MCP clients like Claude or Cursor.
Prerequisites
To follow along with this tutorial, you should have:
- A computer running Linux or Windows (Mac support may be limited)
- Python 3.8 or higher installed
- Basic understanding of command-line interfaces
- At least 8GB of RAM (16GB recommended for optimal performance)
- Approximately 10GB of free disk space for models and dependencies
Step-by-Step Instructions
1. Clone the OmniVoice Studio Repository
The first step is to get the source code from the GitHub repository. Open your terminal or command prompt and run the following command:
git clone https://github.com/omnivoice/omnivoice-studio.git
Why? This downloads the complete source code of OmniVoice Studio, including all necessary scripts, models, and configuration files required to run the application locally.
2. Navigate to the Project Directory
After cloning, navigate into the project directory:
cd omnivoice-studio
Why? You need to be in the project directory to execute the setup scripts and run the application properly.
3. Install Required Dependencies
OmniVoice Studio uses a requirements file to manage dependencies. Install them using pip:
pip install -r requirements.txt
Why? This ensures that all required Python packages and libraries are installed, including those for TTS, audio processing, and model inference.
4. Download Pre-trained Models
OmniVoice Studio requires several pre-trained models for voice cloning and TTS. Run the following command to download them:
python download_models.py
Why? These models are essential for voice cloning and TTS functionality. The script downloads models for multiple languages and voice types, ensuring compatibility with a wide range of use cases.
5. Run the Local Server
Start the OmniVoice Studio server with the following command:
python server.py
Why? This command launches the MCP server, which allows integration with tools like Claude or Cursor. The server listens on a local port, enabling local access without any cloud dependency.
6. Test the TTS Functionality
Once the server is running, you can test the TTS functionality by sending a request to the server. Create a simple Python script to do this:
import requests
# Define the TTS endpoint
url = "http://localhost:8000/tts"
# Define the payload
payload = {
"text": "Hello, this is a test of OmniVoice Studio's TTS feature.",
"language": "en",
"voice": "default"
}
# Send the request
response = requests.post(url, json=payload)
# Save the audio file
with open("output.wav", "wb") as f:
f.write(response.content)
print("Audio saved as output.wav")
Why? This script sends a text input to the TTS endpoint, receives an audio file in response, and saves it locally. It demonstrates how you can programmatically interact with the local TTS engine.
7. Integrate with Claude or Cursor
OmniVoice Studio exposes an MCP server, which allows integration with tools like Claude or Cursor. To use it with Claude:
- Open Claude in your browser or desktop app
- Go to the MCP settings or integrations section
- Add a new MCP server with the URL:
http://localhost:8000 - Configure any necessary authentication (if required)
Why? This integration allows Claude to leverage OmniVoice Studio's local TTS capabilities, enabling voice output without relying on cloud APIs. It's a powerful way to maintain privacy while using AI voice features.
8. Customize Voice Cloning
To clone a voice, you need to provide a sample audio file. Create a directory called voice_samples and place your audio file inside it. Then, use the following command to train a voice clone:
python train_voice.py --audio_path voice_samples/sample.wav --voice_name my_voice
Why? This command trains a voice model based on the sample audio file. The trained voice can then be used for TTS or dubbing tasks, allowing for personalized voice outputs.
Summary
In this tutorial, we walked through setting up OmniVoice Studio, a local, open-source alternative to ElevenLabs. We covered how to install dependencies, download models, run the local server, test TTS functionality, and integrate with tools like Claude. By following these steps, you now have a fully functional local voice cloning and TTS system that doesn't require any cloud services or subscriptions.
OmniVoice Studio is a powerful tool for privacy-conscious developers and content creators who want to leverage voice technologies without compromising user data. Its support for 646 languages and MCP integration makes it a versatile solution for a wide range of applications.
