Andong-Li-speech
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Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…
Zero-Shot Speech Editing and Text-to-Speech in the Wild
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
Noise reduction in python using spectral gating (speech, bioacoustics, audio, time-domain signals)
[ICASSP 2024] 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
Deep learning based speech source separation using Pytorch
Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of sep…
[NeurIPS 2023] UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
Tutorial for surrogate gradient learning in spiking neural networks
Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the hea…
Intel Neuromorphic DNS Challenge
Cascade of Asymmetric Resonators with Fast-Acting Compression (CARFAC) cochlear model.
[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression
Code to accompany our paper "Continual Learning Through Synaptic Intelligence" ICML 2017
Memory Aware Synapses method implementation code
High fidelity, lightweight, end-to-end, streaming, convolution-based neural audio codec
Audio Coding Notebooks and Tutorials
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
This repository contains the audio samples and the source code that accompany the paper: "MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training"