speecht5 vc

Introduction

SpeechT5-VC is a model designed for voice conversion tasks, leveraging a unified-model approach and self-supervised learning. It utilizes datasets such as CMU ARCTIC to train on various speakers, providing capabilities for speech and text processing.

Architecture

The SpeechT5-VC model is based on a unified-modal encoder-decoder architecture, which facilitates the conversion and processing of spoken language. It incorporates SpeechBrain for speaker embedding extraction and Parallel WaveGAN for vocoder implementation, ensuring quality in voice conversion outputs.

Training

Training involves using the CMU ARCTIC dataset, which includes data from four speakers: bdl, clb, rms, and slt. The dataset is split into 932 utterances for training, 100 for validation, and 100 for evaluation. The model fine-tuning is performed on a manifest using a smaller batch size or max updates for efficiency.

Guide: Running Locally

1. Install Dependencies: Ensure you have SpeechBrain and Parallel WaveGAN installed to handle speaker embedding and vocoder tasks.
2. Download Model: Acquire the speecht5_vc.pt file for the fine-tuned voice conversion model.
3. Run Conversion: Use the tools in manifest/utils to extract speaker embeddings and apply the vocoder to generate results.
4. Cloud GPUs: For optimal performance, consider using cloud GPU services like AWS, Google Cloud, or Azure to handle the computation-intensive tasks efficiently.

License

SpeechT5-VC is released under the MIT License, allowing for free use, modification, and distribution in both personal and commercial projects.