#FastSpeech2

The project uses an improved FastSpeech2 model for Chinese speech synthesis, focusing on vibrant and rhythmic pronunciation. It includes prosody representation and prediction enhancements. Recent updates feature prosody model training code and data preprocessing for Biaobei data. The architecture integrates FastSpeech2 and HifiGAN, utilizing a prosody vector to form three models: fastspeech_model, hifigan_model, and prosody_model. It supports both command-line and API-based text-to-speech predictions and welcomes community input and feedback.

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.

Discover the PyTorch implementation for high-quality and efficient text-to-speech synthesis using Denoising Diffusion GANs. This architecture supports single and multi-speaker capabilities across datasets such as LJSpeech and VCTK. Using a dual-stage diffusion process, it offers improved audio fidelity and allows control over aspects like pitch, volume, and speech rate. Utilizing pre-trained FastSpeech2 models provides strong support for both naive and shallow model training. The framework includes TensorBoard integration, facilitating comprehensive audio analysis and performance monitoring.

Explore TensorFlow 2's capabilities for state-of-the-art speech synthesis with models like Tacotron-2, FastSpeech, and MelGAN. The project enhances training efficiency and inference speed, making it suitable for real-time use on mobile and embedded systems. It supports multiple languages and offers comprehensive documentation for easy integration. Learn more about innovations such as the HiFi-GAN vocoder and guided attention loss for high-quality speech synthesis.