#LJSpeech

DiffSinger provides a PyTorch-based implementation for singing voice synthesis, using a shallow diffusion mechanism and pre-trained FastSpeech2 Auxiliary Decoder. It supports multiple model configurations—naive, auxiliary, and shallow—with TTS features like pitch, volume, and rate control. Designed for single and batch inference with pretrained models, the repository offers detailed guidance for comprehensive synthesis and training, while still in development for multi-speaker training.

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.

The FCH-TTS project enhances parallel speech synthesis by integrating advanced vocoder models such as MelGAN and incorporating SoftDTW for effective loss training. It is capable of achieving rapid synthesis on both CPU and GPU platforms. This project emphasizes voice style transfer, utilizing models that perform adeptly on datasets such as LJSpeech and LibriSpeech. The environment can be easily set up to synthesize high-quality speech, with comprehensive documentation and pretrained models available. An active community supports ongoing improvements, with detailed logging via TensorBoard and Wandb. Experience optimized configurations for efficient audio synthesis.

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.

Discover the Pytorch implementation of the Tacotron model, a thorough end-to-end text-to-speech synthesis method. Utilizing the LJSpeech dataset, the project details steps from data preprocessing to audio synthesis. Aimed at researchers and developers in TTS technology, it allows hyperparameter adjustments to efficiently convert text to speech. Features include encoder, decoder, and post-processing networks essential for speech generation. The project is in early development stages, providing sample outputs and inviting community feedback for ongoing enhancement.