#voice conversion

Applio is a user-friendly voice conversion tool for artists, developers, and researchers, delivering high performance and quality transformations. It supports extensive customization options through plugins and is compatible with Windows, Linux, and macOS. The tool adheres to the MIT license for commercial use, encouraging ethical practices. Users can contribute to its development and explore in-depth features such as TensorBoard monitoring. Comprehensive documentation and community support are available on Discord.

Diff-HierVC is an advanced voice conversion system utilizing diffusion models to enhance pitch accuracy and speaker adaptation. Featuring DiffPitch and DiffVoice components, it achieves precise F0 generation and effective voice style transfer. The system incorporates a source-filter encoder and a data-driven Mel-spectrogram prior to boost conversion quality. In zero-shot adaptation scenarios, it delivers a 0.83% CER and 3.29% EER, offering versatile solutions for voice conversion challenges across diverse datasets.

The VITS API provides text-to-speech and voice conversion solutions with features like automatic language recognition, multi-model support, and GPU acceleration. It includes advanced models such as HuBert-VITS and Bert-VITS2, and supports convenient deployment through Docker or virtual environments. The WebUI interface facilitates management and the API supports SSML and customizable defaults, making it suitable for scalable applications.

HierSpeech++ employs hierarchical variational inference to advance zero-shot speech synthesis, enhancing robustness and expressiveness. It efficiently bridges semantic and acoustic gaps, significantly boosting naturalness and speaker similarity in TTS and voice conversion. This project includes a text-to-vec framework and a high-efficiency super-resolution process, enhancing audio from 16kHz to 48kHz. Built on PyTorch, it offers pre-trained models for further exploration, outperforming LLM-based and diffusion models in human-level quality synthesis.

Discover a refined SVC framework that emphasizes advanced f0 estimation techniques and comprehensive CLI support. This project accommodates version 2 pre-trained models and offers optimal compatibility with paperspace setups, providing a versatile solution for audio AI researchers. Experience an updated interface featuring user-friendly options like formant shift and hybrid f0 systems, engineered for high-quality voice conversion. The CLI enables reliable inferencing and training processes with hybrid methods, ensuring enhanced pitch consistency for various applications.

The project provides a user-friendly voice conversion framework utilizing the VITS model, ensuring high-quality outputs even on lower-end GPUs. It addresses timbre leakage with top-1 feature replacement and enhances vocal pitch accuracy using the RMVPE algorithm. Suitable for quick training with minimal data, it efficiently supports voice conversion and model fusion. Additionally, it facilitates low-latency real-time processing compatible with ASIO hardware for precise voice modifications.