Metadata-Version: 2.1
Name: qppwg
Version: 0.1.1
Summary: Quasi-Periodic Parallel WaveGAN implementation
Home-page: http://github.com/bigpon/QPPWG
Author: Yi-Chiao Wu
Author-email: yichiao.wu@g.sp.m.is.nagoya-u.ac.jp
License: MIT License
Description: 
        # Quasi-Periodic Parallel WaveGAN (QPPWG)
        
        [![](https://img.shields.io/pypi/v/qppwg)](https://pypi.org/project/qppwg/) ![](https://img.shields.io/pypi/pyversions/qppwg) ![](https://img.shields.io/pypi/l/qppwg)
        
        This is official [QPPWG](https://arxiv.org/abs/2005.08654) PyTorch implementation.
        QPPWG is a non-autoregressive neural speech generation model developed based on [PWG](https://ieeexplore.ieee.org/abstract/document/9053795) and a [QP](https://bigpon.github.io/QuasiPeriodicWaveNet_demo) structure.
        
        <!-- <center> <img src="https://user-images.githubusercontent.com/10822486/82352944-af1dca80-9a39-11ea-806d-1aa6a91d2773.png" style="display:block;width:370px;height:320px"> </center> -->
        
        ![](https://user-images.githubusercontent.com/10822486/82352944-af1dca80-9a39-11ea-806d-1aa6a91d2773.png)
        
        In this repo, we provide an example to train and test QPPWG as a vocoder for [WORLD](https://doi.org/10.1587/transinf.2015EDP7457) acoustic features.
        More details can be found on our [Demo](https://bigpon.github.io/QuasiPeriodicParallelWaveGAN_demo) page.
        
        
        ## News
        - **2020/5/20** release the first version.
        
        
        ## Requirements
        
        This repository is tested on Ubuntu 16.04 with a GPU Titan V.
        
        - Python 3.6+
        - Cuda 10.0
        - CuDNN 7+
        - PyTorch 1.0.1+
        
        
        ## Environment setup
        
        The code works with both anaconda and virtualenv.
        The following example uses anaconda.
        
        ```bash
        $ conda create -n venvQPPWG python=3.6
        $ source activate venvQPPWG
        $ git clone https://github.com/bigpon/QPPWG.git
        $ cd QPPWG
        $ pip install -e .
        ```
        
        More details can refer to the [PWG](https://github.com/kan-bayashi/ParallelWaveGAN) repo.
        
        
        ## Folder architecture
        - **egs**
        the folder for projects.
        - **egs/vcc18**
        the folder of the VCC2018 project.
        - **egs/vcc18/exp**
        the folder for trained models.
        - **egs/vcc18/conf**
        the folder for configs.
        - **egs/vcc18/data**
        the folder for corpus related files (wav, feature, list ...).
        - **qppwg**
        the folder of the source codes.
        
        
        ## Run
        
        ### Corpus and path setup
        
        - Modify the corresponding CUDA paths in `egs/vcc18/run.py`.
        - Download the [Voice Conversion Challenge 2018](https://datashare.is.ed.ac.uk/handle/10283/3061) (VCC2018) corpus to run the QPPWG example
        
        ```bash
        $ cd egs/vcc18
        # Download training and validation corpus
        $ wget -o train.log -O data/wav/train.zip https://datashare.is.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_training.zip
        # Download evaluation corpus
        $ wget -o eval.log -O data/wav/eval.zip https://datashare.is.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_evaluation.zip
        # unzip corpus
        $ unzip data/wav/train.zip -d data/wav/
        $ unzip data/wav/eval.zip -d data/wav/
        ```
        
        - **Training wav lists**: `data/scp/vcc18_train_22kHz.scp`.
        - **Validation wav lists**: `data/scp/vcc18_valid_22kHz.scp`.
        - **Testing wav list**: `data/scp/vcc18_eval_22kHz.scp`.
        
        ### Preprocessing
        
        ```bash
        # Extract WORLD acoustic features and statistics of training and testing data
        $ bash run.sh --stage 0 --config PWG_30
        ```
        
        - WORLD-related settings can be changed in `egs/vcc18/conf/vcc18.PWG_30.yaml`.
        - If you want to extract other corpus, please create a corresponding config and a file including power thresholds and f0 ranges like `egs/vcc18/data/pow_f0_dict.yml`.
        - More details about feature extraction can refer to the [QPNet](https://github.com/bigpon/QPNet) repo.
        - The lists of auxiliary features will be automatically generated.
        - **Training aux lists**: `data/scp/vcc18_train_22kHz.list`.
        - **Validation aux lists**: `data/scp/vcc18_valid_22kHz.list`.
        - **Testing aux list**: `data/scp/vcc18_eval_22kHz.list`.
        
        
        ### QPPWG/PWG training
        
        ```bash
        # Training a QPPWG model with the 'QPPWGaf_20' config and the 'vcc18_train_22kHz' and 'vcc18_valid_22kHz' sets.
        $ bash run.sh --gpu 0 --stage 1 --conf QPPWGaf_20 \
        --trainset vcc18_train_22kHz --validset vcc18_valid_22kHz
        ```
        
        - The gpu ID can be set by --gpu GPU_ID (default: 0)
        - The model architecture can be set by --conf CONFIG (default: PWG_30)
        - The trained model resume can be set by --resume NUM (default: None)
        
        
        ### QPPWG/PWG testing
        
        ```bash
        # QPPWG/PWG decoding w/ natural acoustic features
        $ bash run.sh --gpu 0 --stage 2 --conf QPPWGaf_20 \
        --iter 400000 --trainset vcc18_train_22kHz --evalset vcc18_eval_22kHz
        # QPPWG/PWG decoding w/ scaled f0 (ex: halved f0).
        $ bash run.sh --gpu 0 --stage 3 --conf QPPWGaf_20 --scaled 0.50 \
        --iter 400000 --trainset vcc18_train_22kHz --evalset vcc18_eval_22kHz
        ```
        
        ### Monitor training progress
        
        ```bash
        $ tensorboard --logdir exp
        ```
        
        - The RTF of PWG_30 decoding with a TITAN V is **0.016**.
        - The RTF of PWG_20 decoding with a TITAN V is **0.011**.
        - The RTF of QPPWGaf_20 decoding with a TITAN V is **0.018**.
        - The training time of PWG_30 with a TITAN V is around 3 days.
        - The training time of QPPWGaf_20 with a TITAN V is around 5 days.
        
        
        ## Results
        [TODO] We will release the pre-trained models and all generated samples around June 2020.
        
        
        ## References
        The QPPWG repository is developed based on the following repositories and paper.
        
        - [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)
        - [bigpon/QPNet](https://github.com/bigpon/QPNet)
        - [k2kobayashi/sprocket](https://github.com/k2kobayashi/sprocket)
        - [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder)
        - [Parallel WaveGAN](https://arxiv.org/abs/1910.11480)
        
        
        
        ## Citation
        
        If you find the code is helpful, please cite the following article.
        
        ```
        @article{wu2020qppwg,
        title={Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent   Dilated Convolution Model for Parametric Speech Generation},
        author={Wu, Yi-Chiao and Hayashi, Tomoki and Okamoto, Takuma and Kawai, Hisashi and Toda, Tomoki},
        journal={arXiv preprint arXiv:2005.08654},
        year={2020}
        }
        ```
        
        
        ## Authors
        
        Development:
        Yi-Chiao Wu @ Nagoya University ([@bigpon](https://github.com/bigpon))
        E-mail: `yichiao.wu@g.sp.m.is.nagoya-u.ac.jp`
        
        Advisor:
        Tomoki Toda @ Nagoya University
        E-mail: `tomoki@icts.nagoya-u.ac.jp`
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: test
