#Audio Synthesis

This project utilizes the SoftVC content encoder paired with the VITS model for singing voice conversion, maintaining original pitch and intonations without text conversion. Key features include a visible f0 editor and speaker mix timeline editor, ensuring uninterrupted sound with NSF HiFiGAN vocoder integration. Tailored for offline purposes, it is intended for converting fictional character voices without real-time application support. Its academic focus emphasizes the user's responsibility for dataset authorization. The 4.1-Stable update offers enhanced sound quality and dynamic fusion capabilities.

This PyTorch-based implementation of MelGAN provides an efficient solution for lightweight and swift audio generation. It leverages the same mel-spectrogram function as NVIDIA's Tacotron2, ensuring seamless conversion into raw audio. Features highlight improved adaptability to new speakers versus WaveGlow and include a pretrained model on PyTorch Hub. Suitable for those seeking efficient audio synthesis in projects, it supports dataset preparation, model training with Tensorboard, and inference, tested on Python 3.6 using sets like LJSpeech-1.1.

PortaSpeech delivers a PyTorch-based generative text-to-speech system known for its compact model size and flexibility. It allows exploration of audio samples and employs pretrained models for single and batch inference. Featuring TTS controllability and supporting datasets like LJSpeech, it is designed with concise preprocessing and training guidance. It integrates vocoder options via HiFi-GAN and MelGAN for quality synthesis, making it a versatile choice for developers interested in speech synthesis. Moreover, it accommodates custom datasets and enhances alignment configurations, all while providing real-time functionality exemplified by TensorBoard.