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توضیحات

Chunked Autoregressive GAN for Conditional Waveform Synthesis

ویژگی	مقدار
سیستم عامل	-
نام فایل	cargan-0.0.4
نام	cargan
نسخه کتابخانه	0.0.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Max Morrison
ایمیل نویسنده	maxrmorrison@gmail.com
آدرس صفحه اصلی	https://github.com/descriptinc/cargan
آدرس اینترنتی	https://pypi.org/project/cargan/
مجوز	-

# Chunked Autoregressive GAN (CARGAN) [![PyPI](https://img.shields.io/pypi/v/cargan.svg)](https://pypi.python.org/pypi/cargan) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)  Official implementation of the paper _Chunked Autoregressive GAN for Conditional Waveform Synthesis_ [[paper]](https://www.maxrmorrison.com/pdfs/morrison2022chunked.pdf) [[companion website]](https://www.maxrmorrison.com/sites/cargan/) ## Table of contents - [Installation](#installation) - [Configuration](#configuration) - [Inference](#inference) * [CLI](#cli) * [API](#api) * [`cargan.from_audio`](#carganfrom_audio) * [`cargan.from_audio_file_to_file`](#carganfrom_audio_file_to_file) * [`cargan.from_audio_files_to_files`](#carganfrom_audio_files_to_files) * [`cargan.from_features`](#carganfrom_features) * [`cargan.from_feature_file_to_file`](#carganfrom_feature_file_to_file) * [`cargan.from_feature_files_to_files`](#carganfrom_feature_files_to_files) - [Reproducing results](#reproducing-results) * [Download](#download) * [Partition](#partition) * [Preprocess](#preprocess) * [Train](#train) * [Evaluate](#evaluate) * [Objective](#objective) * [Subjective](#subjective) * [Receptive field](#receptive-field) - [Running tests](#running-tests) - [Citation](#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} } ```

نیازمندی

مقدار	نام
-	librosa
-	numpy
-	torch
-	torchaudio
-	torchcrepe
-	tqdm

نحوه نصب

نصب پکیج whl cargan-0.0.4:

pip install cargan-0.0.4.whl

نصب پکیج tar.gz cargan-0.0.4:

pip install cargan-0.0.4.tar.gz