#Vocoder

Explore a versatile Text-to-Speech library focused on ease of training, speed, and quality, with over 20 languages supported through pre-trained models. Featuring tools for dataset analysis and multi-speaker TTS capabilities, this platform efficiently trains models using advanced deep learning techniques. It supports key models like Tacotron and Glow-TTS in PyTorch, Tensorflow, and TFLite, offering extensive documentation and community support.

Amphion is an open-source toolkit designed to enhance reproducible research in audio, music, and speech generation. It offers essential features such as text-to-speech and singing voice conversion, making it a useful tool for researchers. The toolkit includes high-quality vocoders and evaluation metrics, facilitating advanced audio signal production. Amphion's platform enables the transformation of diverse inputs into audio and aids in developing large-scale speech synthesis datasets.

The iSTFTNet-pytorch project provides an implementation of a fast and lightweight Mel-spectrogram vocoder that employs inverse short-time Fourier transform for enhanced efficiency. It reduces training time by 30% compared to the HiFi-GAN model while delivering quality audio output. Though designed primarily for testing rather than performance optimization, the model exhibits a speed increase of approximately 60%, demonstrating its potential for efficient audio processing. The project supports configuration-based training and integrates with existing vocoder technologies.

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.

CTX-vec2wav is a vocoder from the AAAI-2024 paper 'UniCATS: A Unified Context-Aware Text-to-Speech Framework,' offering an advanced approach to text-to-speech enhancement through contextual VQ-diffusion and vocoding. Compatible with Linux and optimized for Python 3.9, this project provides clear guidance for both inference and training, suitable for various datasets and conditions. It supports high-fidelity output at 16kHz and 24kHz, utilizing resources such as ESPnet, Kaldi, and ParallelWaveGAN, and offers pre-trained models to advance speech synthesis development.