voice filter

Introduction
The ConVoiFilter model is developed to filter target speaker voices effectively. It is a multilingual model built using PyTorch and is part of the Transformers library. The model's detailed methodology is described in the associated research paper available on arXiv.

Architecture
ConVoiFilter is a voice filtering model designed to isolate the voice of a target speaker from a mixture of voices. The model leverages advanced AI techniques to perform this function and is compatible with inference endpoints for deployment.

Training
While specific training details are not provided, the model is built using PyTorch, indicating the use of neural network-based training techniques common in Transformer models. The training utilizes multilingual datasets to enhance the model's versatility across different languages.

Guide: Running Locally
To run the ConVoiFilter model locally, follow these steps:

1. Clone the Repository: First, clone the repository from Hugging Face to your local machine.
2. Install Dependencies: Ensure you have Python and PyTorch installed. Install the necessary libraries using a package manager like pip.
3. Load the Model: Use the provided scripts to load the model into your environment.
4. Inference: Utilize the model for inference to filter target speaker voices from audio data.

For enhanced performance, consider using cloud GPUs such as those offered by Google Colab, where the model can be executed with GPU acceleration.

License
The ConVoiFilter model is released under the Apache 2.0 license, allowing for wide use and modification with proper attribution.