cargan 0.0.4

Chunked Autoregressive GAN for Conditional Waveform Synthesis

Chunked Autoregressive GAN (CARGAN)

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

Table of contents

• Installation
• Configuration
• Inference
  • CLI
  • API
    • cargan.from_audio
    • cargan.from_audio_file_to_file
    • cargan.from_audio_files_to_files
    • cargan.from_features
    • cargan.from_feature_file_to_file
    • cargan.from_feature_files_to_files
• Reproducing results
  • Download
  • Partition
  • Preprocess
  • Train
  • Evaluate
    • Objective
    • Subjective
    • Receptive field
• Running tests
• Citation

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 <audio_files> \
    --output_files <output_files> \
    --checkpoint <checkpoint> \
    --gpu <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 <feature_files> \
    --output_files <output_files> \
    --checkpoint <checkpoint> \
    --gpu <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 <datasets>

vctk must be downloaded before cumsum.

Preprocess

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

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

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

Train

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

python -m cargan.train \
    --name <name> \
    --datasets <datasets> \
    --gpus <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 <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 <name> \
    --datasets <datasets> \
    --checkpoint <checkpoint> \
    --num <num> \
    --gpu <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 <name> \
    --datasets <datasets> \
    --checkpoint <checkpoint> \
    --num <num> \
    --gpu <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}
}