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Non-parallel Voice Conversion called crank

ویژگی	مقدار
سیستم عامل	-
نام فایل	crank-vc-0.4.1
نام	crank-vc
نسخه کتابخانه	0.4.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	K. KOBAYASHI
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/k2kobayashi/crank
آدرس اینترنتی	https://pypi.org/project/crank-vc/
مجوز	MIT

# crank Non-parallel voice conversion based on vector-quantized variational autoencoder with adversarial learning ## Setup - Install Python dependency ```sh $ git clone https://github.com/k2kobayashi/crank.git $ cd crank/tools $ make ``` - install dependency for mosnet ```sh $ sudo apt install ffmpeg # mosnet dependency ``` ## Recipes - English - VCC2020 - VCC2018 (Thanks to [@unilight](https://github.com/unilight)) - Japanese - jsv_ver1 ### Conversion samples You can access several converted audio samples of VCC 2018 dataset in the [URL](https://k2kobayashi.github.io/crankSamples/). - [vcc2020v1](https://drive.google.com/file/d/1uInvCwggpBYmpplYxuIOidvJkPmav8kE/view?usp=sharing) - [vcc2018v1](https://drive.google.com/file/d/1-Z_Y9pahPQcKR0rqdhu4elI6Hz686qX6/view?usp=sharing) ## Run VCC2020 recipe crank has prepared recipe for Voice Conversion Challenge 2020. In crank recipe, there are 7 stages to implement non-parallel voice conversion. - stage 0 - download dataset - stage 1 - initialization - generate scp files and figures to be determine speaker-dependent parameters - stage 2 - feature extraction - extract mlfb and mcep features - stage 3 - training - stage 4 - reconstuction - generate reconstructed feature for fine-tuning of neural vocoder - stage 5 - evaluation - convert evaluation waveform - stage 6 - synthesis - synthesis waveform by pre-trained [ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) - synthesis waveform by GriffinLim - stage 7 - objective evalution - mel-cepstrum distortion - mosnet ### Put dataset to downloads Note that dataset is only released for the participants (2020/05/26). ``` $ cd egs/vaevc/vcc2020v1 $ mkdir downloads && cd downloads $ mv <path_to_zip>/vcc2020_{training,evaluation}.zip downloads $ unzip vcc2020_training.zip $ unzip vcc2020_evaluation.zip ``` ### Run feature extraction and model training Because the challenge defines its training and evaluation set, we have initially put configuration files. So, you need to run from 2nd stage. ```sh $ ./run.sh --n_jobs 10 --stage 2 --stop_stage 5 ``` where the ```n_jobs``` indicates the number of CPU cores used in the training. ## Configuration Configurations are defined in ```conf/mlfb_vqvae.yml```. Followings are explanation of representative parameters. - feature When you create your own recipe, be carefull to set parameters for feature extraction such as ```fs```, ```fftl```, ```hop_size```, ```framems```, ```shiftms```, and ```mcep_alpha```. These parameters depend on sampling frequency. - feat_type You can choose ```feat_type``` either ```mlfb``` or ```mcep```. If you choose ```mlfb```, the converted waveforms are generated by either GllifinLim vocoder or ParallelWaveGAN vocoder. If you choose ```mcep```, the converted waveforms are generated by world vocoder (i.e., excitation generation and MLSA filtering). - trainer_type We support training with ```vqvae```, ```lsgan```, ```cyclegan```, and ```stargan``` using same generator network. - ```vqvae```: default vqvae setting - ```lsgan```: vqvae with adversarial learning - ```cyclegan```: vqvae with adevesarial learning and cyclic constraints - ```stargan```: vqvae with adevesarial learning similar to cyclegan ## Create your recipe ### Copy recipe template Please copy template directory to start creation of your recipe. ```sh $ cp -r egs/vaevc/template egs/vaevc/<new_recipe> $ cd egs/vaevc/<new_recipe> ``` ### Put .wav files You need to put wav files appropriate directory. You can choose either modifying ```download.sh``` or putting wav files. In either case, the wav files should be located in each speaker like following ```<new_recipe>/downloads/wav/{spkr1, spkr2, ..., spkr3}/*.wav```. If you modify ```downaload.sh```, ```sh $ vim local/download.sh ``` If you put wav files, ```sh $ mkdir downloads $ mv <path_to_your_wav_directory> downloads/wav $ touch downloads/.done ``` ### Run initialization The initialization process generates kaldi-like scp files. ```sh $ ./run.sh --stage 0 --stop_stage 1 ``` Then you modify speaker-dependent parameters in ```conf/spkr.yml``` using generated figures. Page 20~22 in [slide](https://www.slideshare.net/NU_I_TODALAB/hands-on-voice-conversion) help you how to set these parameters. ### Run feature extraction, train, reconstruction, and evaluation After preparing configuration, you run it. ```sh $ ./run.sh --stage 2 --stop_stage 7 ``` ## Citation Please cite this paper when you use crank. ``` K. Kobayashi, W-C. Huang, Y-C. Wu, P.L. Tobing, T. Hayashi, T. Toda, "crank: an open-source software for nonparallel voice conversion based on vector-quantized variational autoencoder", Proc. ICASSP, 2021. (accepted) ``` ## Achknowledgements Thank you [@kan-bayashi](https://github.com/kan-bayashi) for lots of contributions and encouragement helps. ## Who we are - Kazuhiro Kobayashi [@k2kobayashi](https://github.com/k2kobayashi) [maintainer, design and development] - Wen-Chin Huang [@unilight](https://github.com/unilight) [maintainer, design and development] - [Tomoki Toda](https://sites.google.com/site/tomokitoda/) [advisor]

نیازمندی

مقدار	نام
==1.7.1	torch
==0.8.2	torchvision
-	pillow
-	numpy
-	scipy
-	joblib
-	matplotlib
-	sprocket-vc
-	parallel-wavegan
-	tensorboardX
-	torch-optimizer
-	pytorch-lamb
-	gdown
-	museval
-	typeguard

نحوه نصب

نصب پکیج whl crank-vc-0.4.1:

pip install crank-vc-0.4.1.whl

نصب پکیج tar.gz crank-vc-0.4.1:

pip install crank-vc-0.4.1.tar.gz