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Simple wrapper for Continuous Diffusion for Mixed-Type Tabular Data [ICLR 2025]
This repository contains the training code from paper "SpidR Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision". SpidR is a self-supervised speech representat…
💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies
Count the MACs / FLOPs of your PyTorch model.
[ACM CSUR 2025] Understanding World or Predicting Future? A Comprehensive Survey of World Models
A toolkit for benchmarking on a wide variety of audio deepfake datasets.
The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.
A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites
Speed up model training by fixing data loading.
Wavelet scattering transforms in Python with GPU acceleration
[NAACL 2025] WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching
This repository explains various signal processing concepts through visualization of spectrums etc.
A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond
Unofficial pytorch implementation of Born-Again Neural Networks.
A simple, hackable text-to-speech system in PyTorch and MLX
AVES: Animal Vocalization Encoder based on Self-Supervision
Various Slurm utilities scripts for Compute Canada staff and users.
A library built for easier audio self-supervised training, downstream tasks evaluation
The official repository of Dynamic-SUPERB.
This Repository surveys the paper focusing on Prompting and Adapters for Speech Processing.
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Audio Codec Speech processing Universal PERformance Benchmark
Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".
TorchEEG is a library built on PyTorch for EEG signal analysis.
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.