#neural vocoder

VoiceFixer is a neural vocoder tool designed to restore clarity and quality to degraded human speech. It effectively handles noise, reverberation, and clipping across various audio resolutions. Suitable for use via command line, desktop app, or Docker, VoiceFixer offers versatile solutions for developers and audio specialists. The tool includes comprehensive guides for implementation and demonstration, allowing for efficient use of pre-built features or customization for specific restoration requirements.

VoiceFixer is a framework for restoring severely degraded and historical speech, utilizing neural vocoder technology to tackle various distortion scenarios. It includes training tools, evaluation methods, and a PyPI package for user integration. The demos and datasets illustrate its capabilities in improving audio quality in challenging conditions, making it suitable for researchers and developers focusing on speech clarity in difficult environments.

DiffWave is a diffusion-based neural vocoder known for transforming Gaussian noise into high-quality speech through iterative refinement. It utilizes log-scaled Mel spectrograms for precise control, and supports features such as fast inference, multi-GPU training, and mixed-precision training. Recent updates include unconditional waveform synthesis and a fast sampling algorithm. With pretrained models and audio samples readily available, DiffWave offers a robust solution for both research and practical speech synthesis tasks.

Vocos utilizes GAN architecture to efficiently synthesize high-fidelity audio from acoustic features, reconstructing sound rapidly through inverse Fourier transform by generating spectral coefficients. Compatible with mel-spectrograms and EnCodec tokens, Vocos ensures easy integration into existing systems and supports pre-trained models for different datasets. Perfect for developers seeking reliable audio synthesis tools with seamless integration options to text-to-audio frameworks like Bark.

WaveGrad, from Google Brain, is a neural vocoder converting Mel spectrograms to waveforms efficiently. It supports multi-GPU and mixed precision training, offers both command-line and API inference, and allows noise schedule customization. With options for fast synthesis and pre-trained models, it's accessible via PyPI and GitHub. Suitable for developers needing a flexible vocoding solution for dynamic audio synthesis.

Discover advanced non-autoregressive models in this unofficial Pytorch implementation for creating customized neural vocoders. Utilize models such as ParallelWaveGAN, MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN for real-time speech and singing synthesis. The repository is compatible with ESPnet and NVIDIA Tacotron2 and offers a variety of language recipes along with pre-trained models. Experience efficient decoding on both CPU and GPU, and explore Tensorflow conversions for enhanced performance. Stay informed on updates including new LIBRITTS-R recipes and singing voice vocoder support.