Stars
A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
A generative world for general-purpose robotics & embodied AI learning.
Code for NeurIPS 2024 paper - The GAN is dead; long live the GAN! A Modern Baseline GAN - by Huang et al.
Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation
Moshi is a speech-text foundation model and full-duplex spoken dialogue framework. It uses Mimi, a state-of-the-art streaming neural audio codec.
Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
A novel human-interaction method for real-time speech extraction on headphones.
Fast and differentiable time domain all-pole filter in PyTorch.
Generative models for conditional audio generation
Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.
Official repo for consistency models.
Official implementation of SawSing (ISMIR'22)
List of Computer Science courses with video lectures.
Official repository for the paper "Chunked Autoregressive GAN for Conditional Waveform Synthesis"
Official implementation of "Implicit Neural Representations with Periodic Activation Functions"
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch
Authors' implementation of DeepSpeech Distances.
Master programming by recreating your favorite technologies from scratch.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
PyTorch implementations of Generative Adversarial Networks.
Implementation of Differentiable Digital Signal Processing (DDSP) in Pytorch