boriq

boriq's Stars

• vinta/awesome-python

  An opinionated list of awesome Python frameworks, libraries, software and resources.

  Language:Python229k6.1k025.1k

• Lightning-AI/pytorch-lightning

  Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

  Language:Python28.7k2527.2k3.4k

• iterative/dvc

  🦉 Data Versioning and ML Experiments

  Language:Python14k1354.7k1.2k

• kkroening/ffmpeg-python

  Python bindings for FFmpeg - with complex filtering support

  Language:Python10.2k113714895

• spotify/pedalboard

  🎛 🔊 A Python library for audio.

  Language:C++5.3k60193268

• Lyken17/pytorch-OpCounter

  Count the MACs / FLOPs of your PyTorch model.

  Language:Python4.9k29171528

• sovrasov/flops-counter.pytorch

  Flops counter for convolutional networks in pytorch framework

  Language:Python2.8k1598305

• asteroid-team/asteroid

  The PyTorch-based audio source separation toolkit for researchers

  Language:Python2.3k52222425

• facebookresearch/svoice

  We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

  Language:Python1.3k2496185

• JusperLee/Speech-Separation-Paper-Tutorial

  A must-read paper for speech separation based on neural networks

  764272137

• NVIDIA/nv-wavenet

  Reference implementation of real-time autoregressive wavenet inference

  Language:Cuda7364775126

• kaituoxu/Conv-TasNet

  A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).

  Language:Python6871246156

• nussl/nussl

  A flexible source separation library in Python

  Language:Python6242217392

• Audio-WestlakeU/FullSubNet

  PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

  Language:Python5581062158

• facebookresearch/music-translation

  A UNIVERSAL MUSIC TRANSLATION NETWORK - a method for translating music across musical instruments and styles.

  Language:Cuda461211971

• etzinis/sudo_rm_rf

  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 separating sources from mixtures.

  Language:Jupyter Notebook31282234

• tky823/DNN-based_source_separation

  A PyTorch implementation of DNN-based source separation.

  Language:Python29372551

• AvivBick/awesome-ssm-ml

  Reading list for research topics in state-space models

  25113025

• danmic/av-se

  Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

  20412222

• xcmyz/FastVocoder

  Include Basis-MelGAN, MelGAN, HifiGAN and Multiband-HifiGAN, maybe NHV in the future.

  Language:Python15431119

• mbinkowski/DeepSpeechDistances

  Authors' implementation of DeepSpeech Distances.

  Language:Jupyter Notebook1297412

• afourast/avobjects

  Implementation for ECCV20 paper "Self-Supervised Learning of audio-visual objects from video"

  Language:Python11111926

• SRPOL-AUI/storir

  Language:Python4310411

• anton-jeran/IR-GAN

  Augmenting Room Impulse Response

  Language:MATLAB393015

• fgnt/graph_pit

  Language:Python33638

• JarCme/MIRaGe_Utils

  Language:MATLAB6001