Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis

Overview

Chunked Autoregressive GAN (CARGAN)

PyPI License Downloads

Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis [paper] [companion website]

Table of contents

Installation

pip install cargan

Configuration

All configuration is performed in cargan/constants.py. The default configuration is CARGAN. Additional configuration files for experiments described in our paper can be found in config/.

Inference

CLI

Infer from an audio files on disk. audio_files and output_files can be lists of files to perform batch inference.

python -m cargan \
    --audio_files 
   
     \
    --output_files 
    
      \
    --checkpoint 
     
       \
    --gpu 
      

      
     
    
   

Infer from files of features on disk. feature_files and output_files can be lists of files to perform batch inference.

python -m cargan \
    --feature_files 
   
     \
    --output_files 
    
      \
    --checkpoint 
     
       \
    --gpu 
      

      
     
    
   

API

cargan.from_audio

"""Perform vocoding from audio

Arguments
    audio : torch.Tensor(shape=(1, samples))
        The audio to vocode
    sample_rate : int
        The audio sample rate
    gpu : int or None
        The index of the gpu to use

Returns
    vocoded : torch.Tensor(shape=(1, samples))
        The vocoded audio
"""

cargan.from_audio_file_to_file

"""Perform vocoding from audio file and save to file

Arguments
    audio_file : Path
        The audio file to vocode
    output_file : Path
        The location to save the vocoded audio
    checkpoint : Path
        The generator checkpoint
    gpu : int or None
        The index of the gpu to use
"""

cargan.from_audio_files_to_files

"""Perform vocoding from audio files and save to files

Arguments
    audio_files : list(Path)
        The audio files to vocode
    output_files : list(Path)
        The locations to save the vocoded audio
    checkpoint : Path
        The generator checkpoint
    gpu : int or None
        The index of the gpu to use
"""

cargan.from_features

"""Perform vocoding from features

Arguments
    features : torch.Tensor(shape=(1, cargan.NUM_FEATURES, frames)
        The features to vocode
    gpu : int or None
        The index of the gpu to use

Returns
    vocoded : torch.Tensor(shape=(1, cargan.HOPSIZE * frames))
        The vocoded audio
"""

cargan.from_feature_file_to_file

"""Perform vocoding from feature file and save to disk

Arguments
    feature_file : Path
        The feature file to vocode
    output_file : Path
        The location to save the vocoded audio
    checkpoint : Path
        The generator checkpoint
    gpu : int or None
        The index of the gpu to use
"""

cargan.from_feature_files_to_files

"""Perform vocoding from feature files and save to disk

Arguments
    feature_files : list(Path)
        The feature files to vocode
    output_files : list(Path)
        The locations to save the vocoded audio
    checkpoint : Path
        The generator checkpoint
    gpu : int or None
        The index of the gpu to use
"""

Reproducing results

For the following subsections, the arguments are as follows

  • checkpoint - Path to an existing checkpoint on disk
  • datasets - A list of datasets to use. Supported datasets are vctk, daps, cumsum, and musdb.
  • gpu - The index of the gpu to use
  • gpus - A list of indices of gpus to use for distributed data parallelism (DDP)
  • name - The name to give to an experiment or evaluation
  • num - The number of samples to evaluate

Download

Downloads, unzips, and formats datasets. Stores datasets in data/datasets/. Stores formatted datasets in data/cache/.

python -m cargan.data.download --datasets 
   

   

vctk must be downloaded before cumsum.

Preprocess

Prepares features for training. Features are stored in data/cache/.

python -m cargan.preprocess --datasets 
   
     --gpu 
    

    
   

Running this step is not required for the cumsum experiment.

Partition

Partitions a dataset into training, validation, and testing partitions. You should not need to run this, as the partitions used in our work are provided for each dataset in cargan/assets/partitions/.

python -m cargan.partition --datasets 
   

   

The optional --overwrite flag forces the existing partition to be overwritten.

Train

Trains a model. Checkpoints and logs are stored in runs/.

python -m cargan.train \
    --name 
   
     \
    --datasets 
    
      \
    --gpus 
     

     
    
   

You can optionally specify a --checkpoint option pointing to the directory of a previous run. The most recent checkpoint will automatically be loaded and training will resume from that checkpoint. You can overwrite a previous training by passing the --overwrite flag.

You can monitor training via tensorboard as follows.

tensorboard --logdir runs/ --port 
   

   

Evaluate

Objective

Reports the pitch RMSE (in cents), periodicity RMSE, and voiced/unvoiced F1 score. Results are both printed and stored in eval/objective/.

python -m cargan.evaluate.objective \
    --name 
   
     \
    --datasets 
    
      \
    --checkpoint 
     
       \
    --num 
      
        \
    --gpu 
        
       
      
     
    
   

Subjective

Generates samples for subjective evaluation. Also performs benchmarking of inference speed. Results are stored in eval/subjective/.

python -m cargan.evaluate.subjective \
    --name 
   
     \
    --datasets 
    
      \
    --checkpoint 
     
       \
    --num 
      
        \
    --gpu 
        
       
      
     
    
   

Receptive field

Get the size of the (non-causal) receptive field of the generator. cargan.AUTOREGRESSIVE must be False to use this.

python -m cargan.evaluate.receptive_field

Running tests

pip install pytest
pytest

Citation

IEEE

M. Morrison, R. Kumar, K. Kumar, P. Seetharaman, A. Courville, and Y. Bengio, "Chunked Autoregressive GAN for Conditional Waveform Synthesis," Submitted to ICLR 2022, April 2022.

BibTex

@inproceedings{morrison2022chunked,
    title={Chunked Autoregressive GAN for Conditional Waveform Synthesis},
    author={Morrison, Max and Kumar, Rithesh and Kumar, Kundan and Seetharaman, Prem and Courville, Aaron and Bengio, Yoshua},
    booktitle={Submitted to ICLR 2022},
    month={April},
    year={2022}
}
Unit-Convertor - Unit Convertor Built With Python

Python Unit Converter This project can convert Weigth,length and ... units for y

Mahdis Esmaeelian 1 May 31, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Official implement of Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images 深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测 Of

Chenxiao Zhang 135 Dec 19, 2022
Code repository for the work "Multi-Domain Incremental Learning for Semantic Segmentation", accepted at WACV 2022

Multi-Domain Incremental Learning for Semantic Segmentation This is the Pytorch implementation of our work "Multi-Domain Incremental Learning for Sema

Pgxo20 24 Jan 02, 2023
The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

FFA-IR The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark." The framework is inheri

Mingjie 28 Dec 16, 2022
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

Kim Seonghyeon 502 Jan 03, 2023
Statistical and Algorithmic Investing Strategies for Everyone

Eiten - Algorithmic Investing Strategies for Everyone Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic

Tradytics 2.5k Jan 02, 2023
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

DLR-RM 4.7k Jan 01, 2023
TensorFlow implementation of Elastic Weight Consolidation

Elastic weight consolidation Introduction A TensorFlow implementation of elastic weight consolidation as presented in Overcoming catastrophic forgetti

James Stokes 67 Oct 11, 2022
retweet 4 satoshi ⚡️

rt4sat retweet 4 satoshi This bot is the codebase for https://twitter.com/rt4sat please feel free to create an issue if you saw any bugs basically thi

6 Sep 30, 2022
PyTorch implementation of 'Gen-LaneNet: a generalized and scalable approach for 3D lane detection'

(pytorch) Gen-LaneNet: a generalized and scalable approach for 3D lane detection Introduction This is a pytorch implementation of Gen-LaneNet, which p

Yuliang Guo 233 Jan 06, 2023
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization

University1652-Baseline [Paper] [Slide] [Explore Drone-view Data] [Explore Satellite-view Data] [Explore Street-view Data] [Video Sample] [中文介绍] This

Zhedong Zheng 335 Jan 06, 2023
天勤量化开发包, 期货量化, 实时行情/历史数据/实盘交易

TqSdk 天勤量化交易策略程序开发包 TqSdk 是一个由信易科技发起并贡献主要代码的开源 python 库. 依托快期多年积累成熟的交易及行情服务器体系, TqSdk 支持用户使用极少的代码量构建各种类型的量化交易策略程序, 并提供包含期货、期权、股票的 历史数据-实时数据-开发调试-策略回测-

信易科技 2.8k Dec 30, 2022
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (SDPoint) This repository contains the cod

Jason Kuen 17 Jul 04, 2022
Code for GNMR in ICDE 2021

GNMR Code for GNMR in ICDE 2021 Please unzip data files in Datasets/MultiInt-ML10M first. Run labcode_preSamp.py (with graph sampling) for ECommerce-c

7 Oct 27, 2022
[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

TransMaS This repository is the official pytorch implementation of the following paper: NIPS2021 Mixed Supervised Object Detection by TransferringMask

BCMI 49 Jul 27, 2022
Hand gesture recognition model that can be used as a remote control for a smart tv.

Gesture_recognition The training data consists of a few hundred videos categorised into one of the five classes. Each video (typically 2-3 seconds lon

Pratyush Negi 1 Aug 11, 2022
Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection PyTorch code release of the paper "Attentive Prototypes for Sour

Deepti Hegde 23 Oct 17, 2022
MEDS: Enhancing Memory Error Detection for Large-Scale Applications

MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

Secomp Lab at Purdue University 34 Dec 14, 2022
PyTorch implementation of SwAV (Swapping Assignments between Views)

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments This code provides a PyTorch implementation and pretrained models for SwAV

Meta Research 1.7k Jan 04, 2023