This repository contains the implementation of an Accent Conversion system, which transforms speech from one accent to another while preserving the speaker's identity. The project utilizes advanced deep learning techniques to achieve high-quality and natural-sounding accent conversion.
Accent conversion is a challenging task in the field of speech synthesis. This project aims to:
- Convert speech from one accent to another while retaining the speaker's unique voice characteristics.
- Provide tools and models for high-quality accent conversion.
- Speaker preservation: Maintains the speaker's original voice.
- Multiple accents: Supports conversion between multiple accents.
- Modular design: Easy to extend and customize for new datasets or architectures.
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Clone the repository:
git clone https://github.com/yourusername/accent-conversion.git cd accent-conversion -
Install dependencies:
pip install -r requirements.txt
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(Optional) Set up GPU acceleration for faster training and inference.
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Preprocess your dataset:
python preprocess.py --data_dir <path_to_dataset>
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Train the model:
python train.py --config config.yaml
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Perform inference:
python infer.py --input <input_audio_path> --output <output_audio_path>
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Evaluate the model's performance:
python evaluate.py --results_dir <path_to_results>
The core model is based on a neural network architecture with the following components:
- Encoder-Decoder: Captures the content of speech while normalizing accent.
- Style Transfer Module: Transfers accent features from target to source.
- Adversarial Loss: Ensures the converted speech matches the target accent.
Contributions are welcome! To contribute:
- Fork the repository.
- Create a feature branch.
- Submit a pull request.
Please read the Contributing Guidelines for more details.
This project is licensed under the MIT License. See the LICENSE file for details.