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@@ -0,0 +1 @@ +/parallel_wavegan-0.5.5.tar.gz diff --git a/python-parallel-wavegan.spec b/python-parallel-wavegan.spec new file mode 100644 index 0000000..819bf87 --- /dev/null +++ b/python-parallel-wavegan.spec @@ -0,0 +1,1530 @@ +%global _empty_manifest_terminate_build 0 +Name: python-parallel-wavegan +Version: 0.5.5 +Release: 1 +Summary: Parallel WaveGAN implementation +License: MIT License +URL: http://github.com/kan-bayashi/ParallelWaveGAN +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/16/5b/a1b594076db3883a798d721293ed21e20fc571e0d576c1203924648567c0/parallel_wavegan-0.5.5.tar.gz +BuildArch: noarch + + +%description +# Parallel WaveGAN implementation with Pytorch + + [](https://pypi.org/project/parallel-wavegan/)   [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) + +This repository provides **UNOFFICIAL** pytorch implementations of the following models: +- [Parallel WaveGAN](https://arxiv.org/abs/1910.11480) +- [MelGAN](https://arxiv.org/abs/1910.06711) +- [Multiband-MelGAN](https://arxiv.org/abs/2005.05106) +- [HiFi-GAN](https://arxiv.org/abs/2010.05646) +- [StyleMelGAN](https://arxiv.org/abs/2011.01557) + +You can combine these state-of-the-art non-autoregressive models to build your own great vocoder! + +Please check our samples in [our demo HP](https://kan-bayashi.github.io/ParallelWaveGAN). + + + +> Source of the figure: https://arxiv.org/pdf/1910.11480.pdf + +The goal of this repository is to provide real-time neural vocoder, which is compatible with [ESPnet-TTS](https://github.com/espnet/espnet). +Also, this repository can be combined with [NVIDIA/tacotron2](https://github.com/NVIDIA/tacotron2)-based implementation (See [this comment](https://github.com/kan-bayashi/ParallelWaveGAN/issues/169#issuecomment-649320778)). + +You can try the real-time end-to-end text-to-speech and singing voice synthesis demonstration in Google Colab! +- Real-time demonstration with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) +- Real-time demonstration with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb) +- Real-time demonstration with Muskits [](https://colab.research.google.com/github/SJTMusicTeam/svs_demo/blob/master/muskit_svs_realtime.ipynb) + +## What's new + +- 2022/02/27 Support singing voice vocoder [egs/{kiritan, opencpop, oniku\_kurumi\_utagoe\_db, ofuton\_p\_utagoe\_db, csd, kising}/voc1] +- 2021/10/21 Single-speaker Korean recipe [egs/kss/voc1] is available. +- 2021/08/24 Add more pretrained models of StyleMelGAN and HiFi-GAN. +- 2021/08/07 Add initial pretrained models of StyleMelGAN and HiFi-GAN. +- 2021/08/03 Support [StyleMelGAN](https://arxiv.org/abs/2011.01557) generator and discriminator! +- 2021/08/02 Support [HiFi-GAN](https://arxiv.org/abs/2010.05646) generator and discriminator! +- 2020/10/07 [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus) recipe is available! +- 2020/08/19 [Real-time demo with ESPnet2](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) is available! +- 2020/05/29 [VCTK, JSUT, and CSMSC multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/27 [New LJSpeech multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/24 [LJSpeech full-band MelGAN pretrained model](#Results) is available! +- 2020/05/22 [LJSpeech multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/16 [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) is available! +- 2020/03/25 [LibriTTS pretrained models](#Results) are available! +- 2020/03/17 [Tensorflow conversion example notebook](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/notebooks/convert_melgan_from_pytorch_to_tensorflow.ipynb) is available (Thanks, [@dathudeptrai](https://github.com/dathudeptrai))! +- 2020/03/16 [LibriTTS recipe](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1) is available! +- 2020/03/12 [PWG G + MelGAN D + STFT-loss samples](#Results) are available! +- 2020/03/12 Multi-speaker English recipe [egs/vctk/voc1](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1) is available! +- 2020/02/22 [MelGAN G + MelGAN D + STFT-loss samples](#Results) are available! +- 2020/02/12 Support [MelGAN](https://arxiv.org/abs/1910.06711)'s discriminator! +- 2020/02/08 Support [MelGAN](https://arxiv.org/abs/1910.06711)'s generator! + +## Requirements + +This repository is tested on Ubuntu 20.04 with a GPU Titan V. + +- Python 3.7+ +- Cuda 10.0+ +- CuDNN 7+ +- NCCL 2+ (for distributed multi-gpu training) +- libsndfile (you can install via `sudo apt install libsndfile-dev` in ubuntu) +- jq (you can install via `sudo apt install jq` in ubuntu) +- sox (you can install via `sudo apt install sox` in ubuntu) + +Different cuda version should be working but not explicitly tested. +All of the codes are tested on Pytorch 1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, and 1.11.0. + +Pytorch 1.6 works but there are some issues in cpu mode (See #198). + +## Setup + +You can select the installation method from two alternatives. + +### A. Use pip + +```bash +$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git +$ cd ParallelWaveGAN +$ pip install -e . +# If you want to use distributed training, please install +# apex manually by following https://github.com/NVIDIA/apex +$ ... +``` +Note that your cuda version must be exactly matched with the version used for the pytorch binary to install apex. +To install pytorch compiled with different cuda version, see `tools/Makefile`. + +### B. Make virtualenv + +```bash +$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git +$ cd ParallelWaveGAN/tools +$ make +# If you want to use distributed training, please run following +# command to install apex. +$ make apex +``` + +Note that we specify cuda version used to compile pytorch wheel. +If you want to use different cuda version, please check `tools/Makefile` to change the pytorch wheel to be installed. + +## Recipe + +This repository provides [Kaldi](https://github.com/kaldi-asr/kaldi)-style recipes, as the same as [ESPnet](https://github.com/espnet/espnet). +Currently, the following recipes are supported. + +- [LJSpeech](https://keithito.com/LJ-Speech-Dataset/): English female speaker +- [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut): Japanese female speaker +- [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus): Japanese female speaker +- [CSMSC](https://www.data-baker.com/open_source.html): Mandarin female speaker +- [CMU Arctic](http://www.festvox.org/cmu_arctic/): English speakers +- [JNAS](http://research.nii.ac.jp/src/en/JNAS.html): Japanese multi-speaker +- [VCTK](https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html): English multi-speaker +- [LibriTTS](https://arxiv.org/abs/1904.02882): English multi-speaker +- [YesNo](https://arxiv.org/abs/1904.02882): English speaker (For debugging) +- [KSS](https://www.kaggle.com/bryanpark/korean-single-speaker-speech-dataset): Single Korean female speaker +- [Oniku\_kurumi\_utagoe\_db/](http://onikuru.info/db-download/): Single Japanese female singer (singing voice) +- [Kiritan](https://zunko.jp/kiridev/login.php): Single Japanese male singer (singing voice) +- [Ofuton\_p\_utagoe\_db](https://sites.google.com/view/oftn-utagoedb/%E3%83%9B%E3%83%BC%E3%83%A0): Single Japanese female singer (singing voice) +- [Opencpop](https://wenet.org.cn/opencpop/download/): Single Mandarin female singer (singing voice) +- [CSD](https://zenodo.org/record/4785016/): Single Korean/English female singer (singing voice) +- [KiSing](http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/): Single Mandarin female singer (singing voice) + +To run the recipe, please follow the below instruction. + +```bash +# Let us move on the recipe directory +$ cd egs/ljspeech/voc1 + +# Run the recipe from scratch +$ ./run.sh + +# You can change config via command line +$ ./run.sh --conf <your_customized_yaml_config> + +# You can select the stage to start and stop +$ ./run.sh --stage 2 --stop_stage 2 + +# If you want to specify the gpu +$ CUDA_VISIBLE_DEVICES=1 ./run.sh --stage 2 + +# If you want to resume training from 10000 steps checkpoint +$ ./run.sh --stage 2 --resume <path>/<to>/checkpoint-10000steps.pkl +``` + +See more info about the recipes in [this README](./egs/README.md). + +## Speed + +The decoding speed is RTF = 0.016 with TITAN V, much faster than the real-time. + +```bash +[decode]: 100%|██████████| 250/250 [00:30<00:00, 8.31it/s, RTF=0.0156] +2019-11-03 09:07:40,480 (decode:127) INFO: finished generation of 250 utterances (RTF = 0.016). +``` + +Even on the CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads), it can generate less than the real-time. + +```bash +[decode]: 100%|██████████| 250/250 [22:16<00:00, 5.35s/it, RTF=0.841] +2019-11-06 09:04:56,697 (decode:129) INFO: finished generation of 250 utterances (RTF = 0.734). +``` + +If you use MelGAN's generator, the decoding speed will be further faster. + +```bash +# On CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads) +[decode]: 100%|██████████| 250/250 [04:00<00:00, 1.04it/s, RTF=0.0882] +2020-02-08 10:45:14,111 (decode:142) INFO: Finished generation of 250 utterances (RTF = 0.137). + +# On GPU (TITAN V) +[decode]: 100%|██████████| 250/250 [00:06<00:00, 36.38it/s, RTF=0.00189] +2020-02-08 05:44:42,231 (decode:142) INFO: Finished generation of 250 utterances (RTF = 0.002). +``` + +If you use Multi-band MelGAN's generator, the decoding speed will be much further faster. + +```bash +# On CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads) +[decode]: 100%|██████████| 250/250 [01:47<00:00, 2.95it/s, RTF=0.048] +2020-05-22 15:37:19,771 (decode:151) INFO: Finished generation of 250 utterances (RTF = 0.059). + +# On GPU (TITAN V) +[decode]: 100%|██████████| 250/250 [00:05<00:00, 43.67it/s, RTF=0.000928] +2020-05-22 15:35:13,302 (decode:151) INFO: Finished generation of 250 utterances (RTF = 0.001). +``` + +If you want to accelerate the inference more, it is worthwhile to try the conversion from pytorch to tensorflow. +The example of the conversion is available in [the notebook](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/notebooks/convert_melgan_from_pytorch_to_tensorflow.ipynb) (Provided by [@dathudeptrai](https://github.com/dathudeptrai)). + +## Results + +Here the results are summarized in the table. +You can listen to the samples and download pretrained models from the link to our google drive. + +| Model | Conf | Lang | Fs [Hz] | Mel range [Hz] | FFT / Hop / Win [pt] | # iters | +| :----------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------: | :---: | :-----: | :------------: | :------------------: | :-----: | +| [ljspeech_parallel_wavegan.v1](https://drive.google.com/open?id=1wdHr1a51TLeo4iKrGErVKHVFyq6D17TU) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_parallel_wavegan.v1.long](https://drive.google.com/open?id=1XRn3s_wzPF2fdfGshLwuvNHrbgD0hqVS) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_parallel_wavegan.v1.no_limit](https://drive.google.com/open?id=1NoD3TCmKIDHHtf74YsScX8s59aZFOFJA) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.no_limit.yaml) | EN | 22.05k | None | 1024 / 256 / None | 400k | +| [ljspeech_parallel_wavegan.v3](https://drive.google.com/open?id=1a5Q2KiJfUQkVFo5Bd1IoYPVicJGnm7EL) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v3.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 3M | +| [ljspeech_melgan.v1](https://drive.google.com/open?id=1z0vO1UMFHyeCdCLAmd7Moewi4QgCb07S) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_melgan.v1.long](https://drive.google.com/open?id=1RqNGcFO7Geb6-4pJtMbC9-ph_WiWA14e) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_melgan_large.v1](https://drive.google.com/open?id=1KQt-gyxbG6iTZ4aVn9YjQuaGYjAleYs8) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan_large.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_melgan_large.v1.long](https://drive.google.com/open?id=1ogEx-wiQS7HVtdU0_TmlENURIe4v2erC) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan_large.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_melgan.v3](https://drive.google.com/open?id=1eXkm_Wf1YVlk5waP4Vgqd0GzMaJtW3y5) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v3.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 2M | +| [ljspeech_melgan.v3.long](https://drive.google.com/open?id=1u1w4RPefjByX8nfsL59OzU2KgEksBhL1) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v3.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 4M | +| [ljspeech_full_band_melgan.v1](https://drive.google.com/open?id=1RQqkbnoow0srTDYJNYA7RJ5cDRC5xB-t) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/full_band_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_full_band_melgan.v2](https://drive.google.com/open?id=1d9DWOzwOyxT1K5lPnyMqr2nED62vlHaX) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/full_band_melgan.v2.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_multi_band_melgan.v1](https://drive.google.com/open?id=1ls_YxCccQD-v6ADbG6qXlZ8f30KrrhLT) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/multi_band_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_multi_band_melgan.v2](https://drive.google.com/open?id=1wevYP2HQ7ec2fSixTpZIX0sNBtYZJz_I) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/multi_band_melgan.v2.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_hifigan.v1](https://drive.google.com/open?id=18_R5-pGHDIbIR1QvrtBZwVRHHpBy5xiZ) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/hifigan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 2.5M | +| [ljspeech_style_melgan.v1](https://drive.google.com/open?id=1WFlVknhyeZhTT5R6HznVJCJ4fwXKtb3B) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/style_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1.5M | +| [jsut_parallel_wavegan.v1](https://drive.google.com/open?id=1UDRL0JAovZ8XZhoH0wi9jj_zeCKb-AIA) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/parallel_wavegan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [jsut_multi_band_melgan.v2](https://drive.google.com/open?id=1E4fe0c5gMLtmSS0Hrzj-9nUbMwzke4PS) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/multi_band_melgan.v2.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [just_hifigan.v1](https://drive.google.com/open?id=1TY88141UWzQTAQXIPa8_g40QshuqVj6Y) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [just_style_melgan.v1](https://drive.google.com/open?id=1-qKAC0zLya6iKMngDERbSzBYD4JHmGdh) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/style_melgan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [csmsc_parallel_wavegan.v1](https://drive.google.com/open?id=1C2nu9nOFdKcEd-D9xGquQ0bCia0B2v_4) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/parallel_wavegan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [csmsc_multi_band_melgan.v2](https://drive.google.com/open?id=1F7FwxGbvSo1Rnb5kp0dhGwimRJstzCrz) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/multi_band_melgan.v2.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [csmsc_hifigan.v1](https://drive.google.com/open?id=1gTkVloMqteBfSRhTrZGdOBBBRsGd3qt8) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [csmsc_style_melgan.v1](https://drive.google.com/open?id=1gl4P5W_ST_nnv0vjurs7naVm5UJqkZIn) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/style_melgan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [arctic_slt_parallel_wavegan.v1](https://drive.google.com/open?id=1xG9CmSED2TzFdklD6fVxzf7kFV2kPQAJ) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/arctic/voc1/conf/parallel_wavegan.v1.yaml) | EN | 16k | 80-7600 | 1024 / 256 / None | 400k | +| [jnas_parallel_wavegan.v1](https://drive.google.com/open?id=1n_hkxPxryVXbp6oHM1NFm08q0TcoDXz1) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jnas/voc1/conf/parallel_wavegan.v1.yaml) | JP | 16k | 80-7600 | 1024 / 256 / None | 400k | +| [vctk_parallel_wavegan.v1](https://drive.google.com/open?id=1dGTu-B7an2P5sEOepLPjpOaasgaSnLpi) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/parallel_wavegan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [vctk_parallel_wavegan.v1.long](https://drive.google.com/open?id=1qoocM-VQZpjbv5B-zVJpdraazGcPL0So) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [vctk_multi_band_melgan.v2](https://drive.google.com/open?id=17EkB4hSKUEDTYEne-dNHtJT724hdivn4) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/multi_band_melgan.v2.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [vctk_hifigan.v1](https://drive.google.com/open?id=17fu7ukS97m-8StXPc6ltW8a3hr0fsQBP) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [vctk_style_melgan.v1](https://drive.google.com/open?id=1kfJgzDgrOFYxTfVTNbTHcnyq--cc6plo) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/style_melgan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [libritts_parallel_wavegan.v1](https://drive.google.com/open?id=1pb18Nd2FCYWnXfStszBAEEIMe_EZUJV0) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [libritts_parallel_wavegan.v1.long](https://drive.google.com/open?id=15ibzv-uTeprVpwT946Hl1XUYDmg5Afwz) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [libritts_multi_band_melgan.v2](https://drive.google.com/open?id=1jfB15igea6tOQ0hZJGIvnpf3QyNhTLnq) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/multi_band_melgan.v2.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [libritts_hifigan.v1](https://drive.google.com/open?id=10jBLsjQT3LvR-3GgPZpRvWIWvpGjzDnM) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [libritts_style_melgan.v1](https://drive.google.com/open?id=1OPpYbrqYOJ_hHNGSQHzUxz_QZWWBwV9r) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/style_melgan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [kss_parallel_wavegan.v1](https://drive.google.com/open?id=1n5kitXZqPHUr-veoUKCyfJvb3p1g0VlY) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | KO | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [hui_acg_hokuspokus_parallel_wavegan.v1](https://drive.google.com/open?id=1rwzpIwb65xbW5fFPsqPWdforsk4U-vDg) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | DE | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [ruslan_parallel_wavegan.v1](https://drive.google.com/open?id=1QGuesaRKGful0bUTTaFZdbjqHNhy2LpE) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | RU | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [oniku_hifigan.v1](https://drive.google.com/open?id=1K1WNqmZVJaZqTwWNVcucZNeGKHu8-LVm) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/oniku_kurumi_utagoe_db/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [kiritan_hifigan.v1](https://drive.google.com/open?id=1FHUUF5uUnlJ9-D7HmXw3_Sn_GRS48I36) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/kiritan/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [ofuton_hifigan.v1](https://drive.google.com/open?id=1fq8ITA2KpdtrzzD2hOlroParMg-qKjr7) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ofuton_p_utagoe_db/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [opencpop_hifigan.v1](https://drive.google.com/open?id=1hMf5yew_MrbPW0qy5qzXn0mxqbfHTadC) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/opencpop/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [csd_english_hifigan.v1](https://drive.google.com/open?id=1NACjfBqmaecwh4dZMl714RukEkV8XLAi) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csd/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [csd_korean_hifigan.v1](https://drive.google.com/open?id=1BGxIoRg4VgXcX0G-4Dwea030-qQ_Ynyp) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csd/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [kising_hifigan.v1](https://drive.google.com/open?id=1GGu3pW89qxmJapd0Vm1aqp6lqgZARLO9) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/kising/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | + + + +Please access at [our google drive](https://drive.google.com/open?id=1sd_QzcUNnbiaWq7L0ykMP7Xmk-zOuxTi) to check more results. + +Please check the license of database (e.g., whether it is proper for commercial usage) before using the pre-trained model. +The authors will not be responsible for any loss due to the use of the model and legal disputes regarding the use of the dataset. + +## How-to-use pretrained models + +### Analysis-synthesis + +Here the minimal code is shown to perform analysis-synthesis using the pretrained model. + +```bash +# Please make sure you installed `parallel_wavegan` +# If not, please install via pip +$ pip install parallel_wavegan + +# You can download the pretrained model from terminal +$ python << EOF +from parallel_wavegan.utils import download_pretrained_model +download_pretrained_model("<pretrained_model_tag>", "pretrained_model") +EOF + +# You can get all of available pretrained models as follows: +$ python << EOF +from parallel_wavegan.utils import PRETRAINED_MODEL_LIST +print(PRETRAINED_MODEL_LIST.keys()) +EOF + +# Now you can find downloaded pretrained model in `pretrained_model/<pretrain_model_tag>/` +$ ls pretrain_model/<pretrain_model_tag> + checkpoint-400000steps.pkl config.yml stats.h5 + +# These files can also be downloaded manually from the above results + +# Please put an audio file in `sample` directory to perform analysis-synthesis +$ ls sample/ + sample.wav + +# Then perform feature extraction -> feature normalization -> synthesis +$ parallel-wavegan-preprocess \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir sample \ + --dumpdir dump/sample/raw +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s] +$ parallel-wavegan-normalize \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir dump/sample/raw \ + --dumpdir dump/sample/norm \ + --stats pretrain_model/<pretrain_model_tag>/stats.h5 +2019-11-13 13:44:29,574 (normalize:87) INFO: the number of files = 1. +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 513.13it/s] +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --dumpdir dump/sample/norm \ + --outdir sample +2019-11-13 13:44:31,229 (decode:91) INFO: the number of features to be decoded = 1. +[decode]: 100%|███████████████████| 1/1 [00:00<00:00, 18.33it/s, RTF=0.0146] +2019-11-13 13:44:37,132 (decode:129) INFO: finished generation of 1 utterances (RTF = 0.015). + +# You can skip normalization step (on-the-fly normalization, feature extraction -> synthesis) +$ parallel-wavegan-preprocess \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir sample \ + --dumpdir dump/sample/raw +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s] +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --dumpdir dump/sample/raw \ + --normalize-before \ + --outdir sample +2019-11-13 13:44:31,229 (decode:91) INFO: the number of features to be decoded = 1. +[decode]: 100%|███████████████████| 1/1 [00:00<00:00, 18.33it/s, RTF=0.0146] +2019-11-13 13:44:37,132 (decode:129) INFO: finished generation of 1 utterances (RTF = 0.015). + +# you can find the generated speech in `sample` directory +$ ls sample + sample.wav sample_gen.wav +``` + +### Decoding with ESPnet-TTS model's features + +Here, I show the procedure to generate waveforms with features generated by [ESPnet-TTS](https://github.com/espnet/espnet) models. + +```bash +# Make sure you already finished running the recipe of ESPnet-TTS. +# You must use the same feature settings for both Text2Mel and Mel2Wav models. +# Let us move on "ESPnet" recipe directory +$ cd /path/to/espnet/egs/<recipe_name>/tts1 +$ pwd +/path/to/espnet/egs/<recipe_name>/tts1 + +# If you use ESPnet2, move on `egs2/` +$ cd /path/to/espnet/egs2/<recipe_name>/tts1 +$ pwd +/path/to/espnet/egs2/<recipe_name>/tts1 + +# Please install this repository in ESPnet conda (or virtualenv) environment +$ . ./path.sh && pip install -U parallel_wavegan + +# You can download the pretrained model from terminal +$ python << EOF +from parallel_wavegan.utils import download_pretrained_model +download_pretrained_model("<pretrained_model_tag>", "pretrained_model") +EOF + +# You can get all of available pretrained models as follows: +$ python << EOF +from parallel_wavegan.utils import PRETRAINED_MODEL_LIST +print(PRETRAINED_MODEL_LIST.keys()) +EOF + +# You can find downloaded pretrained model in `pretrained_model/<pretrain_model_tag>/` +$ ls pretrain_model/<pretrain_model_tag> + checkpoint-400000steps.pkl config.yml stats.h5 + +# These files can also be downloaded manually from the above results +``` + +**Case 1**: If you use the same dataset for both Text2Mel and Mel2Wav + +```bash +# In this case, you can directly use generated features for decoding. +# Please specify `feats.scp` path for `--feats-scp`, which is located in +# exp/<your_model_dir>/outputs_*_decode/<set_name>/feats.scp. +# Note that do not use outputs_*decode_denorm/<set_name>/feats.scp since +# it is de-normalized features (the input for PWG is normalized features). +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/outputs_*_decode/<set_name>/feats.scp \ + --outdir <path_to_outdir> + +# In the case of ESPnet2, the generated feature can be found in +# exp/<your_model_dir>/decode_*/<set_name>/norm/feats.scp. +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/decode_*/<set_name>/norm/feats.scp \ + --outdir <path_to_outdir> + +# You can find the generated waveforms in <path_to_outdir>/. +$ ls <path_to_outdir> + utt_id_1_gen.wav utt_id_2_gen.wav ... utt_id_N_gen.wav +``` + +**Case 2**: If you use different datasets for Text2Mel and Mel2Wav models + +```bash +# In this case, you must provide `--normalize-before` option additionally. +# And use `feats.scp` of de-normalized generated features. + +# ESPnet1 case +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/outputs_*_decode_denorm/<set_name>/feats.scp \ + --outdir <path_to_outdir> \ + --normalize-before + +# ESPnet2 case +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/decode_*/<set_name>/denorm/feats.scp \ + --outdir <path_to_outdir> \ + --normalize-before + +# You can find the generated waveforms in <path_to_outdir>/. +$ ls <path_to_outdir> + utt_id_1_gen.wav utt_id_2_gen.wav ... utt_id_N_gen.wav +``` + +If you want to combine these models in python, you can try the real-time demonstration in Google Colab! +- Real-time demonstration with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) +- Real-time demonstration with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb) + +### Decoding with dumped npy files + +Sometimes we want to decode with dumped npy files, which are mel-spectrogram generated by TTS models. +Please make sure you used the same feature extraction settings of the pretrained vocoder (`fs`, `fft_size`, `hop_size`, `win_length`, `fmin`, and `fmax`). +Only the difference of `log_base` can be changed with some post-processings (we use log 10 instead of natural log as a default). +See detail in [the comment](https://github.com/kan-bayashi/ParallelWaveGAN/issues/169#issuecomment-649320778). + +```bash +# Generate dummy npy file of mel-spectrogram +$ ipython +[ins] In [1]: import numpy as np +[ins] In [2]: x = np.random.randn(512, 80) # (#frames, #mels) +[ins] In [3]: np.save("dummy_1.npy", x) +[ins] In [4]: y = np.random.randn(256, 80) # (#frames, #mels) +[ins] In [5]: np.save("dummy_2.npy", y) +[ins] In [6]: exit + +# Make scp file (key-path format) +$ find -name "*.npy" | awk '{print "dummy_" NR " " $1}' > feats.scp + +# Check (<utt_id> <path>) +$ cat feats.scp +dummy_1 ./dummy_1.npy +dummy_2 ./dummy_2.npy + +# Decode without feature normalization +# This case assumes that the input mel-spectrogram is normalized with the same statistics of the pretrained model. +$ parallel-wavegan-decode \ + --checkpoint /path/to/checkpoint-400000steps.pkl \ + --feats-scp ./feats.scp \ + --outdir wav +2021-08-10 09:13:07,624 (decode:140) INFO: The number of features to be decoded = 2. +[decode]: 100%|████████████████████████████████████████| 2/2 [00:00<00:00, 13.84it/s, RTF=0.00264] +2021-08-10 09:13:29,660 (decode:174) INFO: Finished generation of 2 utterances (RTF = 0.005). + +# Decode with feature normalization +# This case assumes that the input mel-spectrogram is not normalized. +$ parallel-wavegan-decode \ + --checkpoint /path/to/checkpoint-400000steps.pkl \ + --feats-scp ./feats.scp \ + --normalize-before \ + --outdir wav +2021-08-10 09:13:07,624 (decode:140) INFO: The number of features to be decoded = 2. +[decode]: 100%|████████████████████████████████████████| 2/2 [00:00<00:00, 13.84it/s, RTF=0.00264] +2021-08-10 09:13:29,660 (decode:174) INFO: Finished generation of 2 utterances (RTF = 0.005). +``` + +## Notes + +The terms of use of the pretrained model follow that of each corpus used for the training. Please carefully check by yourself. + +## References + +- [Parallel WaveGAN](https://arxiv.org/abs/1910.11480) +- [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder) +- [LiyuanLucasLiu/RAdam](https://github.com/LiyuanLucasLiu/RAdam) +- [MelGAN](https://arxiv.org/abs/1910.06711) +- [descriptinc/melgan-neurips](https://github.com/descriptinc/melgan-neurips) +- [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) +- [HiFi-GAN](https://arxiv.org/abs/2010.05646) +- [jik876/hifi-gan](https://github.com/jik876/hifi-gan) +- [StyleMelGAN](https://arxiv.org/abs/2011.01557) + +## Acknowledgement + +The author would like to thank Ryuichi Yamamoto ([@r9y9](https://github.com/r9y9)) for his great repository, paper, and valuable discussions. + +## Author + +Tomoki Hayashi ([@kan-bayashi](https://github.com/kan-bayashi)) +E-mail: `hayashi.tomoki<at>g.sp.m.is.nagoya-u.ac.jp` + +%package -n python3-parallel-wavegan +Summary: Parallel WaveGAN implementation +Provides: python-parallel-wavegan +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-parallel-wavegan +# Parallel WaveGAN implementation with Pytorch + + [](https://pypi.org/project/parallel-wavegan/)   [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) + +This repository provides **UNOFFICIAL** pytorch implementations of the following models: +- [Parallel WaveGAN](https://arxiv.org/abs/1910.11480) +- [MelGAN](https://arxiv.org/abs/1910.06711) +- [Multiband-MelGAN](https://arxiv.org/abs/2005.05106) +- [HiFi-GAN](https://arxiv.org/abs/2010.05646) +- [StyleMelGAN](https://arxiv.org/abs/2011.01557) + +You can combine these state-of-the-art non-autoregressive models to build your own great vocoder! + +Please check our samples in [our demo HP](https://kan-bayashi.github.io/ParallelWaveGAN). + + + +> Source of the figure: https://arxiv.org/pdf/1910.11480.pdf + +The goal of this repository is to provide real-time neural vocoder, which is compatible with [ESPnet-TTS](https://github.com/espnet/espnet). +Also, this repository can be combined with [NVIDIA/tacotron2](https://github.com/NVIDIA/tacotron2)-based implementation (See [this comment](https://github.com/kan-bayashi/ParallelWaveGAN/issues/169#issuecomment-649320778)). + +You can try the real-time end-to-end text-to-speech and singing voice synthesis demonstration in Google Colab! +- Real-time demonstration with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) +- Real-time demonstration with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb) +- Real-time demonstration with Muskits [](https://colab.research.google.com/github/SJTMusicTeam/svs_demo/blob/master/muskit_svs_realtime.ipynb) + +## What's new + +- 2022/02/27 Support singing voice vocoder [egs/{kiritan, opencpop, oniku\_kurumi\_utagoe\_db, ofuton\_p\_utagoe\_db, csd, kising}/voc1] +- 2021/10/21 Single-speaker Korean recipe [egs/kss/voc1] is available. +- 2021/08/24 Add more pretrained models of StyleMelGAN and HiFi-GAN. +- 2021/08/07 Add initial pretrained models of StyleMelGAN and HiFi-GAN. +- 2021/08/03 Support [StyleMelGAN](https://arxiv.org/abs/2011.01557) generator and discriminator! +- 2021/08/02 Support [HiFi-GAN](https://arxiv.org/abs/2010.05646) generator and discriminator! +- 2020/10/07 [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus) recipe is available! +- 2020/08/19 [Real-time demo with ESPnet2](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) is available! +- 2020/05/29 [VCTK, JSUT, and CSMSC multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/27 [New LJSpeech multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/24 [LJSpeech full-band MelGAN pretrained model](#Results) is available! +- 2020/05/22 [LJSpeech multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/16 [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) is available! +- 2020/03/25 [LibriTTS pretrained models](#Results) are available! +- 2020/03/17 [Tensorflow conversion example notebook](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/notebooks/convert_melgan_from_pytorch_to_tensorflow.ipynb) is available (Thanks, [@dathudeptrai](https://github.com/dathudeptrai))! +- 2020/03/16 [LibriTTS recipe](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1) is available! +- 2020/03/12 [PWG G + MelGAN D + STFT-loss samples](#Results) are available! +- 2020/03/12 Multi-speaker English recipe [egs/vctk/voc1](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1) is available! +- 2020/02/22 [MelGAN G + MelGAN D + STFT-loss samples](#Results) are available! +- 2020/02/12 Support [MelGAN](https://arxiv.org/abs/1910.06711)'s discriminator! +- 2020/02/08 Support [MelGAN](https://arxiv.org/abs/1910.06711)'s generator! + +## Requirements + +This repository is tested on Ubuntu 20.04 with a GPU Titan V. + +- Python 3.7+ +- Cuda 10.0+ +- CuDNN 7+ +- NCCL 2+ (for distributed multi-gpu training) +- libsndfile (you can install via `sudo apt install libsndfile-dev` in ubuntu) +- jq (you can install via `sudo apt install jq` in ubuntu) +- sox (you can install via `sudo apt install sox` in ubuntu) + +Different cuda version should be working but not explicitly tested. +All of the codes are tested on Pytorch 1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, and 1.11.0. + +Pytorch 1.6 works but there are some issues in cpu mode (See #198). + +## Setup + +You can select the installation method from two alternatives. + +### A. Use pip + +```bash +$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git +$ cd ParallelWaveGAN +$ pip install -e . +# If you want to use distributed training, please install +# apex manually by following https://github.com/NVIDIA/apex +$ ... +``` +Note that your cuda version must be exactly matched with the version used for the pytorch binary to install apex. +To install pytorch compiled with different cuda version, see `tools/Makefile`. + +### B. Make virtualenv + +```bash +$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git +$ cd ParallelWaveGAN/tools +$ make +# If you want to use distributed training, please run following +# command to install apex. +$ make apex +``` + +Note that we specify cuda version used to compile pytorch wheel. +If you want to use different cuda version, please check `tools/Makefile` to change the pytorch wheel to be installed. + +## Recipe + +This repository provides [Kaldi](https://github.com/kaldi-asr/kaldi)-style recipes, as the same as [ESPnet](https://github.com/espnet/espnet). +Currently, the following recipes are supported. + +- [LJSpeech](https://keithito.com/LJ-Speech-Dataset/): English female speaker +- [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut): Japanese female speaker +- [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus): Japanese female speaker +- [CSMSC](https://www.data-baker.com/open_source.html): Mandarin female speaker +- [CMU Arctic](http://www.festvox.org/cmu_arctic/): English speakers +- [JNAS](http://research.nii.ac.jp/src/en/JNAS.html): Japanese multi-speaker +- [VCTK](https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html): English multi-speaker +- [LibriTTS](https://arxiv.org/abs/1904.02882): English multi-speaker +- [YesNo](https://arxiv.org/abs/1904.02882): English speaker (For debugging) +- [KSS](https://www.kaggle.com/bryanpark/korean-single-speaker-speech-dataset): Single Korean female speaker +- [Oniku\_kurumi\_utagoe\_db/](http://onikuru.info/db-download/): Single Japanese female singer (singing voice) +- [Kiritan](https://zunko.jp/kiridev/login.php): Single Japanese male singer (singing voice) +- [Ofuton\_p\_utagoe\_db](https://sites.google.com/view/oftn-utagoedb/%E3%83%9B%E3%83%BC%E3%83%A0): Single Japanese female singer (singing voice) +- [Opencpop](https://wenet.org.cn/opencpop/download/): Single Mandarin female singer (singing voice) +- [CSD](https://zenodo.org/record/4785016/): Single Korean/English female singer (singing voice) +- [KiSing](http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/): Single Mandarin female singer (singing voice) + +To run the recipe, please follow the below instruction. + +```bash +# Let us move on the recipe directory +$ cd egs/ljspeech/voc1 + +# Run the recipe from scratch +$ ./run.sh + +# You can change config via command line +$ ./run.sh --conf <your_customized_yaml_config> + +# You can select the stage to start and stop +$ ./run.sh --stage 2 --stop_stage 2 + +# If you want to specify the gpu +$ CUDA_VISIBLE_DEVICES=1 ./run.sh --stage 2 + +# If you want to resume training from 10000 steps checkpoint +$ ./run.sh --stage 2 --resume <path>/<to>/checkpoint-10000steps.pkl +``` + +See more info about the recipes in [this README](./egs/README.md). + +## Speed + +The decoding speed is RTF = 0.016 with TITAN V, much faster than the real-time. + +```bash +[decode]: 100%|██████████| 250/250 [00:30<00:00, 8.31it/s, RTF=0.0156] +2019-11-03 09:07:40,480 (decode:127) INFO: finished generation of 250 utterances (RTF = 0.016). +``` + +Even on the CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads), it can generate less than the real-time. + +```bash +[decode]: 100%|██████████| 250/250 [22:16<00:00, 5.35s/it, RTF=0.841] +2019-11-06 09:04:56,697 (decode:129) INFO: finished generation of 250 utterances (RTF = 0.734). +``` + +If you use MelGAN's generator, the decoding speed will be further faster. + +```bash +# On CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads) +[decode]: 100%|██████████| 250/250 [04:00<00:00, 1.04it/s, RTF=0.0882] +2020-02-08 10:45:14,111 (decode:142) INFO: Finished generation of 250 utterances (RTF = 0.137). + +# On GPU (TITAN V) +[decode]: 100%|██████████| 250/250 [00:06<00:00, 36.38it/s, RTF=0.00189] +2020-02-08 05:44:42,231 (decode:142) INFO: Finished generation of 250 utterances (RTF = 0.002). +``` + +If you use Multi-band MelGAN's generator, the decoding speed will be much further faster. + +```bash +# On CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads) +[decode]: 100%|██████████| 250/250 [01:47<00:00, 2.95it/s, RTF=0.048] +2020-05-22 15:37:19,771 (decode:151) INFO: Finished generation of 250 utterances (RTF = 0.059). + +# On GPU (TITAN V) +[decode]: 100%|██████████| 250/250 [00:05<00:00, 43.67it/s, RTF=0.000928] +2020-05-22 15:35:13,302 (decode:151) INFO: Finished generation of 250 utterances (RTF = 0.001). +``` + +If you want to accelerate the inference more, it is worthwhile to try the conversion from pytorch to tensorflow. +The example of the conversion is available in [the notebook](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/notebooks/convert_melgan_from_pytorch_to_tensorflow.ipynb) (Provided by [@dathudeptrai](https://github.com/dathudeptrai)). + +## Results + +Here the results are summarized in the table. +You can listen to the samples and download pretrained models from the link to our google drive. + +| Model | Conf | Lang | Fs [Hz] | Mel range [Hz] | FFT / Hop / Win [pt] | # iters | +| :----------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------: | :---: | :-----: | :------------: | :------------------: | :-----: | +| [ljspeech_parallel_wavegan.v1](https://drive.google.com/open?id=1wdHr1a51TLeo4iKrGErVKHVFyq6D17TU) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_parallel_wavegan.v1.long](https://drive.google.com/open?id=1XRn3s_wzPF2fdfGshLwuvNHrbgD0hqVS) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_parallel_wavegan.v1.no_limit](https://drive.google.com/open?id=1NoD3TCmKIDHHtf74YsScX8s59aZFOFJA) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.no_limit.yaml) | EN | 22.05k | None | 1024 / 256 / None | 400k | +| [ljspeech_parallel_wavegan.v3](https://drive.google.com/open?id=1a5Q2KiJfUQkVFo5Bd1IoYPVicJGnm7EL) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v3.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 3M | +| [ljspeech_melgan.v1](https://drive.google.com/open?id=1z0vO1UMFHyeCdCLAmd7Moewi4QgCb07S) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_melgan.v1.long](https://drive.google.com/open?id=1RqNGcFO7Geb6-4pJtMbC9-ph_WiWA14e) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_melgan_large.v1](https://drive.google.com/open?id=1KQt-gyxbG6iTZ4aVn9YjQuaGYjAleYs8) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan_large.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_melgan_large.v1.long](https://drive.google.com/open?id=1ogEx-wiQS7HVtdU0_TmlENURIe4v2erC) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan_large.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_melgan.v3](https://drive.google.com/open?id=1eXkm_Wf1YVlk5waP4Vgqd0GzMaJtW3y5) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v3.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 2M | +| [ljspeech_melgan.v3.long](https://drive.google.com/open?id=1u1w4RPefjByX8nfsL59OzU2KgEksBhL1) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v3.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 4M | +| [ljspeech_full_band_melgan.v1](https://drive.google.com/open?id=1RQqkbnoow0srTDYJNYA7RJ5cDRC5xB-t) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/full_band_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_full_band_melgan.v2](https://drive.google.com/open?id=1d9DWOzwOyxT1K5lPnyMqr2nED62vlHaX) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/full_band_melgan.v2.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_multi_band_melgan.v1](https://drive.google.com/open?id=1ls_YxCccQD-v6ADbG6qXlZ8f30KrrhLT) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/multi_band_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_multi_band_melgan.v2](https://drive.google.com/open?id=1wevYP2HQ7ec2fSixTpZIX0sNBtYZJz_I) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/multi_band_melgan.v2.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_hifigan.v1](https://drive.google.com/open?id=18_R5-pGHDIbIR1QvrtBZwVRHHpBy5xiZ) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/hifigan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 2.5M | +| [ljspeech_style_melgan.v1](https://drive.google.com/open?id=1WFlVknhyeZhTT5R6HznVJCJ4fwXKtb3B) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/style_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1.5M | +| [jsut_parallel_wavegan.v1](https://drive.google.com/open?id=1UDRL0JAovZ8XZhoH0wi9jj_zeCKb-AIA) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/parallel_wavegan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [jsut_multi_band_melgan.v2](https://drive.google.com/open?id=1E4fe0c5gMLtmSS0Hrzj-9nUbMwzke4PS) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/multi_band_melgan.v2.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [just_hifigan.v1](https://drive.google.com/open?id=1TY88141UWzQTAQXIPa8_g40QshuqVj6Y) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [just_style_melgan.v1](https://drive.google.com/open?id=1-qKAC0zLya6iKMngDERbSzBYD4JHmGdh) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/style_melgan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [csmsc_parallel_wavegan.v1](https://drive.google.com/open?id=1C2nu9nOFdKcEd-D9xGquQ0bCia0B2v_4) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/parallel_wavegan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [csmsc_multi_band_melgan.v2](https://drive.google.com/open?id=1F7FwxGbvSo1Rnb5kp0dhGwimRJstzCrz) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/multi_band_melgan.v2.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [csmsc_hifigan.v1](https://drive.google.com/open?id=1gTkVloMqteBfSRhTrZGdOBBBRsGd3qt8) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [csmsc_style_melgan.v1](https://drive.google.com/open?id=1gl4P5W_ST_nnv0vjurs7naVm5UJqkZIn) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/style_melgan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [arctic_slt_parallel_wavegan.v1](https://drive.google.com/open?id=1xG9CmSED2TzFdklD6fVxzf7kFV2kPQAJ) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/arctic/voc1/conf/parallel_wavegan.v1.yaml) | EN | 16k | 80-7600 | 1024 / 256 / None | 400k | +| [jnas_parallel_wavegan.v1](https://drive.google.com/open?id=1n_hkxPxryVXbp6oHM1NFm08q0TcoDXz1) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jnas/voc1/conf/parallel_wavegan.v1.yaml) | JP | 16k | 80-7600 | 1024 / 256 / None | 400k | +| [vctk_parallel_wavegan.v1](https://drive.google.com/open?id=1dGTu-B7an2P5sEOepLPjpOaasgaSnLpi) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/parallel_wavegan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [vctk_parallel_wavegan.v1.long](https://drive.google.com/open?id=1qoocM-VQZpjbv5B-zVJpdraazGcPL0So) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [vctk_multi_band_melgan.v2](https://drive.google.com/open?id=17EkB4hSKUEDTYEne-dNHtJT724hdivn4) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/multi_band_melgan.v2.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [vctk_hifigan.v1](https://drive.google.com/open?id=17fu7ukS97m-8StXPc6ltW8a3hr0fsQBP) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [vctk_style_melgan.v1](https://drive.google.com/open?id=1kfJgzDgrOFYxTfVTNbTHcnyq--cc6plo) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/style_melgan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [libritts_parallel_wavegan.v1](https://drive.google.com/open?id=1pb18Nd2FCYWnXfStszBAEEIMe_EZUJV0) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [libritts_parallel_wavegan.v1.long](https://drive.google.com/open?id=15ibzv-uTeprVpwT946Hl1XUYDmg5Afwz) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [libritts_multi_band_melgan.v2](https://drive.google.com/open?id=1jfB15igea6tOQ0hZJGIvnpf3QyNhTLnq) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/multi_band_melgan.v2.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [libritts_hifigan.v1](https://drive.google.com/open?id=10jBLsjQT3LvR-3GgPZpRvWIWvpGjzDnM) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [libritts_style_melgan.v1](https://drive.google.com/open?id=1OPpYbrqYOJ_hHNGSQHzUxz_QZWWBwV9r) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/style_melgan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [kss_parallel_wavegan.v1](https://drive.google.com/open?id=1n5kitXZqPHUr-veoUKCyfJvb3p1g0VlY) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | KO | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [hui_acg_hokuspokus_parallel_wavegan.v1](https://drive.google.com/open?id=1rwzpIwb65xbW5fFPsqPWdforsk4U-vDg) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | DE | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [ruslan_parallel_wavegan.v1](https://drive.google.com/open?id=1QGuesaRKGful0bUTTaFZdbjqHNhy2LpE) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | RU | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [oniku_hifigan.v1](https://drive.google.com/open?id=1K1WNqmZVJaZqTwWNVcucZNeGKHu8-LVm) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/oniku_kurumi_utagoe_db/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [kiritan_hifigan.v1](https://drive.google.com/open?id=1FHUUF5uUnlJ9-D7HmXw3_Sn_GRS48I36) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/kiritan/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [ofuton_hifigan.v1](https://drive.google.com/open?id=1fq8ITA2KpdtrzzD2hOlroParMg-qKjr7) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ofuton_p_utagoe_db/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [opencpop_hifigan.v1](https://drive.google.com/open?id=1hMf5yew_MrbPW0qy5qzXn0mxqbfHTadC) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/opencpop/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [csd_english_hifigan.v1](https://drive.google.com/open?id=1NACjfBqmaecwh4dZMl714RukEkV8XLAi) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csd/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [csd_korean_hifigan.v1](https://drive.google.com/open?id=1BGxIoRg4VgXcX0G-4Dwea030-qQ_Ynyp) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csd/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [kising_hifigan.v1](https://drive.google.com/open?id=1GGu3pW89qxmJapd0Vm1aqp6lqgZARLO9) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/kising/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | + + + +Please access at [our google drive](https://drive.google.com/open?id=1sd_QzcUNnbiaWq7L0ykMP7Xmk-zOuxTi) to check more results. + +Please check the license of database (e.g., whether it is proper for commercial usage) before using the pre-trained model. +The authors will not be responsible for any loss due to the use of the model and legal disputes regarding the use of the dataset. + +## How-to-use pretrained models + +### Analysis-synthesis + +Here the minimal code is shown to perform analysis-synthesis using the pretrained model. + +```bash +# Please make sure you installed `parallel_wavegan` +# If not, please install via pip +$ pip install parallel_wavegan + +# You can download the pretrained model from terminal +$ python << EOF +from parallel_wavegan.utils import download_pretrained_model +download_pretrained_model("<pretrained_model_tag>", "pretrained_model") +EOF + +# You can get all of available pretrained models as follows: +$ python << EOF +from parallel_wavegan.utils import PRETRAINED_MODEL_LIST +print(PRETRAINED_MODEL_LIST.keys()) +EOF + +# Now you can find downloaded pretrained model in `pretrained_model/<pretrain_model_tag>/` +$ ls pretrain_model/<pretrain_model_tag> + checkpoint-400000steps.pkl config.yml stats.h5 + +# These files can also be downloaded manually from the above results + +# Please put an audio file in `sample` directory to perform analysis-synthesis +$ ls sample/ + sample.wav + +# Then perform feature extraction -> feature normalization -> synthesis +$ parallel-wavegan-preprocess \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir sample \ + --dumpdir dump/sample/raw +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s] +$ parallel-wavegan-normalize \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir dump/sample/raw \ + --dumpdir dump/sample/norm \ + --stats pretrain_model/<pretrain_model_tag>/stats.h5 +2019-11-13 13:44:29,574 (normalize:87) INFO: the number of files = 1. +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 513.13it/s] +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --dumpdir dump/sample/norm \ + --outdir sample +2019-11-13 13:44:31,229 (decode:91) INFO: the number of features to be decoded = 1. +[decode]: 100%|███████████████████| 1/1 [00:00<00:00, 18.33it/s, RTF=0.0146] +2019-11-13 13:44:37,132 (decode:129) INFO: finished generation of 1 utterances (RTF = 0.015). + +# You can skip normalization step (on-the-fly normalization, feature extraction -> synthesis) +$ parallel-wavegan-preprocess \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir sample \ + --dumpdir dump/sample/raw +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s] +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --dumpdir dump/sample/raw \ + --normalize-before \ + --outdir sample +2019-11-13 13:44:31,229 (decode:91) INFO: the number of features to be decoded = 1. +[decode]: 100%|███████████████████| 1/1 [00:00<00:00, 18.33it/s, RTF=0.0146] +2019-11-13 13:44:37,132 (decode:129) INFO: finished generation of 1 utterances (RTF = 0.015). + +# you can find the generated speech in `sample` directory +$ ls sample + sample.wav sample_gen.wav +``` + +### Decoding with ESPnet-TTS model's features + +Here, I show the procedure to generate waveforms with features generated by [ESPnet-TTS](https://github.com/espnet/espnet) models. + +```bash +# Make sure you already finished running the recipe of ESPnet-TTS. +# You must use the same feature settings for both Text2Mel and Mel2Wav models. +# Let us move on "ESPnet" recipe directory +$ cd /path/to/espnet/egs/<recipe_name>/tts1 +$ pwd +/path/to/espnet/egs/<recipe_name>/tts1 + +# If you use ESPnet2, move on `egs2/` +$ cd /path/to/espnet/egs2/<recipe_name>/tts1 +$ pwd +/path/to/espnet/egs2/<recipe_name>/tts1 + +# Please install this repository in ESPnet conda (or virtualenv) environment +$ . ./path.sh && pip install -U parallel_wavegan + +# You can download the pretrained model from terminal +$ python << EOF +from parallel_wavegan.utils import download_pretrained_model +download_pretrained_model("<pretrained_model_tag>", "pretrained_model") +EOF + +# You can get all of available pretrained models as follows: +$ python << EOF +from parallel_wavegan.utils import PRETRAINED_MODEL_LIST +print(PRETRAINED_MODEL_LIST.keys()) +EOF + +# You can find downloaded pretrained model in `pretrained_model/<pretrain_model_tag>/` +$ ls pretrain_model/<pretrain_model_tag> + checkpoint-400000steps.pkl config.yml stats.h5 + +# These files can also be downloaded manually from the above results +``` + +**Case 1**: If you use the same dataset for both Text2Mel and Mel2Wav + +```bash +# In this case, you can directly use generated features for decoding. +# Please specify `feats.scp` path for `--feats-scp`, which is located in +# exp/<your_model_dir>/outputs_*_decode/<set_name>/feats.scp. +# Note that do not use outputs_*decode_denorm/<set_name>/feats.scp since +# it is de-normalized features (the input for PWG is normalized features). +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/outputs_*_decode/<set_name>/feats.scp \ + --outdir <path_to_outdir> + +# In the case of ESPnet2, the generated feature can be found in +# exp/<your_model_dir>/decode_*/<set_name>/norm/feats.scp. +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/decode_*/<set_name>/norm/feats.scp \ + --outdir <path_to_outdir> + +# You can find the generated waveforms in <path_to_outdir>/. +$ ls <path_to_outdir> + utt_id_1_gen.wav utt_id_2_gen.wav ... utt_id_N_gen.wav +``` + +**Case 2**: If you use different datasets for Text2Mel and Mel2Wav models + +```bash +# In this case, you must provide `--normalize-before` option additionally. +# And use `feats.scp` of de-normalized generated features. + +# ESPnet1 case +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/outputs_*_decode_denorm/<set_name>/feats.scp \ + --outdir <path_to_outdir> \ + --normalize-before + +# ESPnet2 case +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/decode_*/<set_name>/denorm/feats.scp \ + --outdir <path_to_outdir> \ + --normalize-before + +# You can find the generated waveforms in <path_to_outdir>/. +$ ls <path_to_outdir> + utt_id_1_gen.wav utt_id_2_gen.wav ... utt_id_N_gen.wav +``` + +If you want to combine these models in python, you can try the real-time demonstration in Google Colab! +- Real-time demonstration with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) +- Real-time demonstration with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb) + +### Decoding with dumped npy files + +Sometimes we want to decode with dumped npy files, which are mel-spectrogram generated by TTS models. +Please make sure you used the same feature extraction settings of the pretrained vocoder (`fs`, `fft_size`, `hop_size`, `win_length`, `fmin`, and `fmax`). +Only the difference of `log_base` can be changed with some post-processings (we use log 10 instead of natural log as a default). +See detail in [the comment](https://github.com/kan-bayashi/ParallelWaveGAN/issues/169#issuecomment-649320778). + +```bash +# Generate dummy npy file of mel-spectrogram +$ ipython +[ins] In [1]: import numpy as np +[ins] In [2]: x = np.random.randn(512, 80) # (#frames, #mels) +[ins] In [3]: np.save("dummy_1.npy", x) +[ins] In [4]: y = np.random.randn(256, 80) # (#frames, #mels) +[ins] In [5]: np.save("dummy_2.npy", y) +[ins] In [6]: exit + +# Make scp file (key-path format) +$ find -name "*.npy" | awk '{print "dummy_" NR " " $1}' > feats.scp + +# Check (<utt_id> <path>) +$ cat feats.scp +dummy_1 ./dummy_1.npy +dummy_2 ./dummy_2.npy + +# Decode without feature normalization +# This case assumes that the input mel-spectrogram is normalized with the same statistics of the pretrained model. +$ parallel-wavegan-decode \ + --checkpoint /path/to/checkpoint-400000steps.pkl \ + --feats-scp ./feats.scp \ + --outdir wav +2021-08-10 09:13:07,624 (decode:140) INFO: The number of features to be decoded = 2. +[decode]: 100%|████████████████████████████████████████| 2/2 [00:00<00:00, 13.84it/s, RTF=0.00264] +2021-08-10 09:13:29,660 (decode:174) INFO: Finished generation of 2 utterances (RTF = 0.005). + +# Decode with feature normalization +# This case assumes that the input mel-spectrogram is not normalized. +$ parallel-wavegan-decode \ + --checkpoint /path/to/checkpoint-400000steps.pkl \ + --feats-scp ./feats.scp \ + --normalize-before \ + --outdir wav +2021-08-10 09:13:07,624 (decode:140) INFO: The number of features to be decoded = 2. +[decode]: 100%|████████████████████████████████████████| 2/2 [00:00<00:00, 13.84it/s, RTF=0.00264] +2021-08-10 09:13:29,660 (decode:174) INFO: Finished generation of 2 utterances (RTF = 0.005). +``` + +## Notes + +The terms of use of the pretrained model follow that of each corpus used for the training. Please carefully check by yourself. + +## References + +- [Parallel WaveGAN](https://arxiv.org/abs/1910.11480) +- [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder) +- [LiyuanLucasLiu/RAdam](https://github.com/LiyuanLucasLiu/RAdam) +- [MelGAN](https://arxiv.org/abs/1910.06711) +- [descriptinc/melgan-neurips](https://github.com/descriptinc/melgan-neurips) +- [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) +- [HiFi-GAN](https://arxiv.org/abs/2010.05646) +- [jik876/hifi-gan](https://github.com/jik876/hifi-gan) +- [StyleMelGAN](https://arxiv.org/abs/2011.01557) + +## Acknowledgement + +The author would like to thank Ryuichi Yamamoto ([@r9y9](https://github.com/r9y9)) for his great repository, paper, and valuable discussions. + +## Author + +Tomoki Hayashi ([@kan-bayashi](https://github.com/kan-bayashi)) +E-mail: `hayashi.tomoki<at>g.sp.m.is.nagoya-u.ac.jp` + +%package help +Summary: Development documents and examples for parallel-wavegan +Provides: python3-parallel-wavegan-doc +%description help +# Parallel WaveGAN implementation with Pytorch + + [](https://pypi.org/project/parallel-wavegan/)   [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) + +This repository provides **UNOFFICIAL** pytorch implementations of the following models: +- [Parallel WaveGAN](https://arxiv.org/abs/1910.11480) +- [MelGAN](https://arxiv.org/abs/1910.06711) +- [Multiband-MelGAN](https://arxiv.org/abs/2005.05106) +- [HiFi-GAN](https://arxiv.org/abs/2010.05646) +- [StyleMelGAN](https://arxiv.org/abs/2011.01557) + +You can combine these state-of-the-art non-autoregressive models to build your own great vocoder! + +Please check our samples in [our demo HP](https://kan-bayashi.github.io/ParallelWaveGAN). + + + +> Source of the figure: https://arxiv.org/pdf/1910.11480.pdf + +The goal of this repository is to provide real-time neural vocoder, which is compatible with [ESPnet-TTS](https://github.com/espnet/espnet). +Also, this repository can be combined with [NVIDIA/tacotron2](https://github.com/NVIDIA/tacotron2)-based implementation (See [this comment](https://github.com/kan-bayashi/ParallelWaveGAN/issues/169#issuecomment-649320778)). + +You can try the real-time end-to-end text-to-speech and singing voice synthesis demonstration in Google Colab! +- Real-time demonstration with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) +- Real-time demonstration with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb) +- Real-time demonstration with Muskits [](https://colab.research.google.com/github/SJTMusicTeam/svs_demo/blob/master/muskit_svs_realtime.ipynb) + +## What's new + +- 2022/02/27 Support singing voice vocoder [egs/{kiritan, opencpop, oniku\_kurumi\_utagoe\_db, ofuton\_p\_utagoe\_db, csd, kising}/voc1] +- 2021/10/21 Single-speaker Korean recipe [egs/kss/voc1] is available. +- 2021/08/24 Add more pretrained models of StyleMelGAN and HiFi-GAN. +- 2021/08/07 Add initial pretrained models of StyleMelGAN and HiFi-GAN. +- 2021/08/03 Support [StyleMelGAN](https://arxiv.org/abs/2011.01557) generator and discriminator! +- 2021/08/02 Support [HiFi-GAN](https://arxiv.org/abs/2010.05646) generator and discriminator! +- 2020/10/07 [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus) recipe is available! +- 2020/08/19 [Real-time demo with ESPnet2](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) is available! +- 2020/05/29 [VCTK, JSUT, and CSMSC multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/27 [New LJSpeech multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/24 [LJSpeech full-band MelGAN pretrained model](#Results) is available! +- 2020/05/22 [LJSpeech multi-band MelGAN pretrained model](#Results) is available! +- 2020/05/16 [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) is available! +- 2020/03/25 [LibriTTS pretrained models](#Results) are available! +- 2020/03/17 [Tensorflow conversion example notebook](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/notebooks/convert_melgan_from_pytorch_to_tensorflow.ipynb) is available (Thanks, [@dathudeptrai](https://github.com/dathudeptrai))! +- 2020/03/16 [LibriTTS recipe](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1) is available! +- 2020/03/12 [PWG G + MelGAN D + STFT-loss samples](#Results) are available! +- 2020/03/12 Multi-speaker English recipe [egs/vctk/voc1](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1) is available! +- 2020/02/22 [MelGAN G + MelGAN D + STFT-loss samples](#Results) are available! +- 2020/02/12 Support [MelGAN](https://arxiv.org/abs/1910.06711)'s discriminator! +- 2020/02/08 Support [MelGAN](https://arxiv.org/abs/1910.06711)'s generator! + +## Requirements + +This repository is tested on Ubuntu 20.04 with a GPU Titan V. + +- Python 3.7+ +- Cuda 10.0+ +- CuDNN 7+ +- NCCL 2+ (for distributed multi-gpu training) +- libsndfile (you can install via `sudo apt install libsndfile-dev` in ubuntu) +- jq (you can install via `sudo apt install jq` in ubuntu) +- sox (you can install via `sudo apt install sox` in ubuntu) + +Different cuda version should be working but not explicitly tested. +All of the codes are tested on Pytorch 1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, and 1.11.0. + +Pytorch 1.6 works but there are some issues in cpu mode (See #198). + +## Setup + +You can select the installation method from two alternatives. + +### A. Use pip + +```bash +$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git +$ cd ParallelWaveGAN +$ pip install -e . +# If you want to use distributed training, please install +# apex manually by following https://github.com/NVIDIA/apex +$ ... +``` +Note that your cuda version must be exactly matched with the version used for the pytorch binary to install apex. +To install pytorch compiled with different cuda version, see `tools/Makefile`. + +### B. Make virtualenv + +```bash +$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git +$ cd ParallelWaveGAN/tools +$ make +# If you want to use distributed training, please run following +# command to install apex. +$ make apex +``` + +Note that we specify cuda version used to compile pytorch wheel. +If you want to use different cuda version, please check `tools/Makefile` to change the pytorch wheel to be installed. + +## Recipe + +This repository provides [Kaldi](https://github.com/kaldi-asr/kaldi)-style recipes, as the same as [ESPnet](https://github.com/espnet/espnet). +Currently, the following recipes are supported. + +- [LJSpeech](https://keithito.com/LJ-Speech-Dataset/): English female speaker +- [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut): Japanese female speaker +- [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus): Japanese female speaker +- [CSMSC](https://www.data-baker.com/open_source.html): Mandarin female speaker +- [CMU Arctic](http://www.festvox.org/cmu_arctic/): English speakers +- [JNAS](http://research.nii.ac.jp/src/en/JNAS.html): Japanese multi-speaker +- [VCTK](https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html): English multi-speaker +- [LibriTTS](https://arxiv.org/abs/1904.02882): English multi-speaker +- [YesNo](https://arxiv.org/abs/1904.02882): English speaker (For debugging) +- [KSS](https://www.kaggle.com/bryanpark/korean-single-speaker-speech-dataset): Single Korean female speaker +- [Oniku\_kurumi\_utagoe\_db/](http://onikuru.info/db-download/): Single Japanese female singer (singing voice) +- [Kiritan](https://zunko.jp/kiridev/login.php): Single Japanese male singer (singing voice) +- [Ofuton\_p\_utagoe\_db](https://sites.google.com/view/oftn-utagoedb/%E3%83%9B%E3%83%BC%E3%83%A0): Single Japanese female singer (singing voice) +- [Opencpop](https://wenet.org.cn/opencpop/download/): Single Mandarin female singer (singing voice) +- [CSD](https://zenodo.org/record/4785016/): Single Korean/English female singer (singing voice) +- [KiSing](http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/): Single Mandarin female singer (singing voice) + +To run the recipe, please follow the below instruction. + +```bash +# Let us move on the recipe directory +$ cd egs/ljspeech/voc1 + +# Run the recipe from scratch +$ ./run.sh + +# You can change config via command line +$ ./run.sh --conf <your_customized_yaml_config> + +# You can select the stage to start and stop +$ ./run.sh --stage 2 --stop_stage 2 + +# If you want to specify the gpu +$ CUDA_VISIBLE_DEVICES=1 ./run.sh --stage 2 + +# If you want to resume training from 10000 steps checkpoint +$ ./run.sh --stage 2 --resume <path>/<to>/checkpoint-10000steps.pkl +``` + +See more info about the recipes in [this README](./egs/README.md). + +## Speed + +The decoding speed is RTF = 0.016 with TITAN V, much faster than the real-time. + +```bash +[decode]: 100%|██████████| 250/250 [00:30<00:00, 8.31it/s, RTF=0.0156] +2019-11-03 09:07:40,480 (decode:127) INFO: finished generation of 250 utterances (RTF = 0.016). +``` + +Even on the CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads), it can generate less than the real-time. + +```bash +[decode]: 100%|██████████| 250/250 [22:16<00:00, 5.35s/it, RTF=0.841] +2019-11-06 09:04:56,697 (decode:129) INFO: finished generation of 250 utterances (RTF = 0.734). +``` + +If you use MelGAN's generator, the decoding speed will be further faster. + +```bash +# On CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads) +[decode]: 100%|██████████| 250/250 [04:00<00:00, 1.04it/s, RTF=0.0882] +2020-02-08 10:45:14,111 (decode:142) INFO: Finished generation of 250 utterances (RTF = 0.137). + +# On GPU (TITAN V) +[decode]: 100%|██████████| 250/250 [00:06<00:00, 36.38it/s, RTF=0.00189] +2020-02-08 05:44:42,231 (decode:142) INFO: Finished generation of 250 utterances (RTF = 0.002). +``` + +If you use Multi-band MelGAN's generator, the decoding speed will be much further faster. + +```bash +# On CPU (Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz 16 threads) +[decode]: 100%|██████████| 250/250 [01:47<00:00, 2.95it/s, RTF=0.048] +2020-05-22 15:37:19,771 (decode:151) INFO: Finished generation of 250 utterances (RTF = 0.059). + +# On GPU (TITAN V) +[decode]: 100%|██████████| 250/250 [00:05<00:00, 43.67it/s, RTF=0.000928] +2020-05-22 15:35:13,302 (decode:151) INFO: Finished generation of 250 utterances (RTF = 0.001). +``` + +If you want to accelerate the inference more, it is worthwhile to try the conversion from pytorch to tensorflow. +The example of the conversion is available in [the notebook](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/notebooks/convert_melgan_from_pytorch_to_tensorflow.ipynb) (Provided by [@dathudeptrai](https://github.com/dathudeptrai)). + +## Results + +Here the results are summarized in the table. +You can listen to the samples and download pretrained models from the link to our google drive. + +| Model | Conf | Lang | Fs [Hz] | Mel range [Hz] | FFT / Hop / Win [pt] | # iters | +| :----------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------: | :---: | :-----: | :------------: | :------------------: | :-----: | +| [ljspeech_parallel_wavegan.v1](https://drive.google.com/open?id=1wdHr1a51TLeo4iKrGErVKHVFyq6D17TU) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_parallel_wavegan.v1.long](https://drive.google.com/open?id=1XRn3s_wzPF2fdfGshLwuvNHrbgD0hqVS) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_parallel_wavegan.v1.no_limit](https://drive.google.com/open?id=1NoD3TCmKIDHHtf74YsScX8s59aZFOFJA) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v1.no_limit.yaml) | EN | 22.05k | None | 1024 / 256 / None | 400k | +| [ljspeech_parallel_wavegan.v3](https://drive.google.com/open?id=1a5Q2KiJfUQkVFo5Bd1IoYPVicJGnm7EL) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/parallel_wavegan.v3.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 3M | +| [ljspeech_melgan.v1](https://drive.google.com/open?id=1z0vO1UMFHyeCdCLAmd7Moewi4QgCb07S) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_melgan.v1.long](https://drive.google.com/open?id=1RqNGcFO7Geb6-4pJtMbC9-ph_WiWA14e) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_melgan_large.v1](https://drive.google.com/open?id=1KQt-gyxbG6iTZ4aVn9YjQuaGYjAleYs8) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan_large.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 400k | +| [ljspeech_melgan_large.v1.long](https://drive.google.com/open?id=1ogEx-wiQS7HVtdU0_TmlENURIe4v2erC) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan_large.v1.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_melgan.v3](https://drive.google.com/open?id=1eXkm_Wf1YVlk5waP4Vgqd0GzMaJtW3y5) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v3.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 2M | +| [ljspeech_melgan.v3.long](https://drive.google.com/open?id=1u1w4RPefjByX8nfsL59OzU2KgEksBhL1) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/melgan.v3.long.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 4M | +| [ljspeech_full_band_melgan.v1](https://drive.google.com/open?id=1RQqkbnoow0srTDYJNYA7RJ5cDRC5xB-t) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/full_band_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_full_band_melgan.v2](https://drive.google.com/open?id=1d9DWOzwOyxT1K5lPnyMqr2nED62vlHaX) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/full_band_melgan.v2.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_multi_band_melgan.v1](https://drive.google.com/open?id=1ls_YxCccQD-v6ADbG6qXlZ8f30KrrhLT) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/multi_band_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_multi_band_melgan.v2](https://drive.google.com/open?id=1wevYP2HQ7ec2fSixTpZIX0sNBtYZJz_I) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/multi_band_melgan.v2.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1M | +| [ljspeech_hifigan.v1](https://drive.google.com/open?id=18_R5-pGHDIbIR1QvrtBZwVRHHpBy5xiZ) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/hifigan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 2.5M | +| [ljspeech_style_melgan.v1](https://drive.google.com/open?id=1WFlVknhyeZhTT5R6HznVJCJ4fwXKtb3B) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ljspeech/voc1/conf/style_melgan.v1.yaml) | EN | 22.05k | 80-7600 | 1024 / 256 / None | 1.5M | +| [jsut_parallel_wavegan.v1](https://drive.google.com/open?id=1UDRL0JAovZ8XZhoH0wi9jj_zeCKb-AIA) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/parallel_wavegan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [jsut_multi_band_melgan.v2](https://drive.google.com/open?id=1E4fe0c5gMLtmSS0Hrzj-9nUbMwzke4PS) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/multi_band_melgan.v2.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [just_hifigan.v1](https://drive.google.com/open?id=1TY88141UWzQTAQXIPa8_g40QshuqVj6Y) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [just_style_melgan.v1](https://drive.google.com/open?id=1-qKAC0zLya6iKMngDERbSzBYD4JHmGdh) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jsut/voc1/conf/style_melgan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [csmsc_parallel_wavegan.v1](https://drive.google.com/open?id=1C2nu9nOFdKcEd-D9xGquQ0bCia0B2v_4) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/parallel_wavegan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [csmsc_multi_band_melgan.v2](https://drive.google.com/open?id=1F7FwxGbvSo1Rnb5kp0dhGwimRJstzCrz) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/multi_band_melgan.v2.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [csmsc_hifigan.v1](https://drive.google.com/open?id=1gTkVloMqteBfSRhTrZGdOBBBRsGd3qt8) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [csmsc_style_melgan.v1](https://drive.google.com/open?id=1gl4P5W_ST_nnv0vjurs7naVm5UJqkZIn) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csmsc/voc1/conf/style_melgan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [arctic_slt_parallel_wavegan.v1](https://drive.google.com/open?id=1xG9CmSED2TzFdklD6fVxzf7kFV2kPQAJ) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/arctic/voc1/conf/parallel_wavegan.v1.yaml) | EN | 16k | 80-7600 | 1024 / 256 / None | 400k | +| [jnas_parallel_wavegan.v1](https://drive.google.com/open?id=1n_hkxPxryVXbp6oHM1NFm08q0TcoDXz1) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/jnas/voc1/conf/parallel_wavegan.v1.yaml) | JP | 16k | 80-7600 | 1024 / 256 / None | 400k | +| [vctk_parallel_wavegan.v1](https://drive.google.com/open?id=1dGTu-B7an2P5sEOepLPjpOaasgaSnLpi) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/parallel_wavegan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [vctk_parallel_wavegan.v1.long](https://drive.google.com/open?id=1qoocM-VQZpjbv5B-zVJpdraazGcPL0So) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [vctk_multi_band_melgan.v2](https://drive.google.com/open?id=17EkB4hSKUEDTYEne-dNHtJT724hdivn4) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/multi_band_melgan.v2.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [vctk_hifigan.v1](https://drive.google.com/open?id=17fu7ukS97m-8StXPc6ltW8a3hr0fsQBP) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [vctk_style_melgan.v1](https://drive.google.com/open?id=1kfJgzDgrOFYxTfVTNbTHcnyq--cc6plo) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/vctk/voc1/conf/style_melgan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [libritts_parallel_wavegan.v1](https://drive.google.com/open?id=1pb18Nd2FCYWnXfStszBAEEIMe_EZUJV0) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [libritts_parallel_wavegan.v1.long](https://drive.google.com/open?id=15ibzv-uTeprVpwT946Hl1XUYDmg5Afwz) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.long.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [libritts_multi_band_melgan.v2](https://drive.google.com/open?id=1jfB15igea6tOQ0hZJGIvnpf3QyNhTLnq) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/multi_band_melgan.v2.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1M | +| [libritts_hifigan.v1](https://drive.google.com/open?id=10jBLsjQT3LvR-3GgPZpRvWIWvpGjzDnM) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 2.5M | +| [libritts_style_melgan.v1](https://drive.google.com/open?id=1OPpYbrqYOJ_hHNGSQHzUxz_QZWWBwV9r) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/style_melgan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 1.5M | +| [kss_parallel_wavegan.v1](https://drive.google.com/open?id=1n5kitXZqPHUr-veoUKCyfJvb3p1g0VlY) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | KO | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [hui_acg_hokuspokus_parallel_wavegan.v1](https://drive.google.com/open?id=1rwzpIwb65xbW5fFPsqPWdforsk4U-vDg) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | DE | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [ruslan_parallel_wavegan.v1](https://drive.google.com/open?id=1QGuesaRKGful0bUTTaFZdbjqHNhy2LpE) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts/voc1/conf/parallel_wavegan.v1.yaml) | RU | 24k | 80-7600 | 2048 / 300 / 1200 | 400k | +| [oniku_hifigan.v1](https://drive.google.com/open?id=1K1WNqmZVJaZqTwWNVcucZNeGKHu8-LVm) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/oniku_kurumi_utagoe_db/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [kiritan_hifigan.v1](https://drive.google.com/open?id=1FHUUF5uUnlJ9-D7HmXw3_Sn_GRS48I36) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/kiritan/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [ofuton_hifigan.v1](https://drive.google.com/open?id=1fq8ITA2KpdtrzzD2hOlroParMg-qKjr7) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/ofuton_p_utagoe_db/voc1/conf/hifigan.v1.yaml) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [opencpop_hifigan.v1](https://drive.google.com/open?id=1hMf5yew_MrbPW0qy5qzXn0mxqbfHTadC) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/opencpop/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [csd_english_hifigan.v1](https://drive.google.com/open?id=1NACjfBqmaecwh4dZMl714RukEkV8XLAi) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csd/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | +| [csd_korean_hifigan.v1](https://drive.google.com/open?id=1BGxIoRg4VgXcX0G-4Dwea030-qQ_Ynyp) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/csd/voc1/conf/hifigan.v1.yaml) | EN | 24k | 80-7600 | 2048 / 300 / 1200 | 250k | +| [kising_hifigan.v1](https://drive.google.com/open?id=1GGu3pW89qxmJapd0Vm1aqp6lqgZARLO9) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/kising/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 300k | + + + +Please access at [our google drive](https://drive.google.com/open?id=1sd_QzcUNnbiaWq7L0ykMP7Xmk-zOuxTi) to check more results. + +Please check the license of database (e.g., whether it is proper for commercial usage) before using the pre-trained model. +The authors will not be responsible for any loss due to the use of the model and legal disputes regarding the use of the dataset. + +## How-to-use pretrained models + +### Analysis-synthesis + +Here the minimal code is shown to perform analysis-synthesis using the pretrained model. + +```bash +# Please make sure you installed `parallel_wavegan` +# If not, please install via pip +$ pip install parallel_wavegan + +# You can download the pretrained model from terminal +$ python << EOF +from parallel_wavegan.utils import download_pretrained_model +download_pretrained_model("<pretrained_model_tag>", "pretrained_model") +EOF + +# You can get all of available pretrained models as follows: +$ python << EOF +from parallel_wavegan.utils import PRETRAINED_MODEL_LIST +print(PRETRAINED_MODEL_LIST.keys()) +EOF + +# Now you can find downloaded pretrained model in `pretrained_model/<pretrain_model_tag>/` +$ ls pretrain_model/<pretrain_model_tag> + checkpoint-400000steps.pkl config.yml stats.h5 + +# These files can also be downloaded manually from the above results + +# Please put an audio file in `sample` directory to perform analysis-synthesis +$ ls sample/ + sample.wav + +# Then perform feature extraction -> feature normalization -> synthesis +$ parallel-wavegan-preprocess \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir sample \ + --dumpdir dump/sample/raw +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s] +$ parallel-wavegan-normalize \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir dump/sample/raw \ + --dumpdir dump/sample/norm \ + --stats pretrain_model/<pretrain_model_tag>/stats.h5 +2019-11-13 13:44:29,574 (normalize:87) INFO: the number of files = 1. +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 513.13it/s] +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --dumpdir dump/sample/norm \ + --outdir sample +2019-11-13 13:44:31,229 (decode:91) INFO: the number of features to be decoded = 1. +[decode]: 100%|███████████████████| 1/1 [00:00<00:00, 18.33it/s, RTF=0.0146] +2019-11-13 13:44:37,132 (decode:129) INFO: finished generation of 1 utterances (RTF = 0.015). + +# You can skip normalization step (on-the-fly normalization, feature extraction -> synthesis) +$ parallel-wavegan-preprocess \ + --config pretrain_model/<pretrain_model_tag>/config.yml \ + --rootdir sample \ + --dumpdir dump/sample/raw +100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s] +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --dumpdir dump/sample/raw \ + --normalize-before \ + --outdir sample +2019-11-13 13:44:31,229 (decode:91) INFO: the number of features to be decoded = 1. +[decode]: 100%|███████████████████| 1/1 [00:00<00:00, 18.33it/s, RTF=0.0146] +2019-11-13 13:44:37,132 (decode:129) INFO: finished generation of 1 utterances (RTF = 0.015). + +# you can find the generated speech in `sample` directory +$ ls sample + sample.wav sample_gen.wav +``` + +### Decoding with ESPnet-TTS model's features + +Here, I show the procedure to generate waveforms with features generated by [ESPnet-TTS](https://github.com/espnet/espnet) models. + +```bash +# Make sure you already finished running the recipe of ESPnet-TTS. +# You must use the same feature settings for both Text2Mel and Mel2Wav models. +# Let us move on "ESPnet" recipe directory +$ cd /path/to/espnet/egs/<recipe_name>/tts1 +$ pwd +/path/to/espnet/egs/<recipe_name>/tts1 + +# If you use ESPnet2, move on `egs2/` +$ cd /path/to/espnet/egs2/<recipe_name>/tts1 +$ pwd +/path/to/espnet/egs2/<recipe_name>/tts1 + +# Please install this repository in ESPnet conda (or virtualenv) environment +$ . ./path.sh && pip install -U parallel_wavegan + +# You can download the pretrained model from terminal +$ python << EOF +from parallel_wavegan.utils import download_pretrained_model +download_pretrained_model("<pretrained_model_tag>", "pretrained_model") +EOF + +# You can get all of available pretrained models as follows: +$ python << EOF +from parallel_wavegan.utils import PRETRAINED_MODEL_LIST +print(PRETRAINED_MODEL_LIST.keys()) +EOF + +# You can find downloaded pretrained model in `pretrained_model/<pretrain_model_tag>/` +$ ls pretrain_model/<pretrain_model_tag> + checkpoint-400000steps.pkl config.yml stats.h5 + +# These files can also be downloaded manually from the above results +``` + +**Case 1**: If you use the same dataset for both Text2Mel and Mel2Wav + +```bash +# In this case, you can directly use generated features for decoding. +# Please specify `feats.scp` path for `--feats-scp`, which is located in +# exp/<your_model_dir>/outputs_*_decode/<set_name>/feats.scp. +# Note that do not use outputs_*decode_denorm/<set_name>/feats.scp since +# it is de-normalized features (the input for PWG is normalized features). +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/outputs_*_decode/<set_name>/feats.scp \ + --outdir <path_to_outdir> + +# In the case of ESPnet2, the generated feature can be found in +# exp/<your_model_dir>/decode_*/<set_name>/norm/feats.scp. +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/decode_*/<set_name>/norm/feats.scp \ + --outdir <path_to_outdir> + +# You can find the generated waveforms in <path_to_outdir>/. +$ ls <path_to_outdir> + utt_id_1_gen.wav utt_id_2_gen.wav ... utt_id_N_gen.wav +``` + +**Case 2**: If you use different datasets for Text2Mel and Mel2Wav models + +```bash +# In this case, you must provide `--normalize-before` option additionally. +# And use `feats.scp` of de-normalized generated features. + +# ESPnet1 case +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/outputs_*_decode_denorm/<set_name>/feats.scp \ + --outdir <path_to_outdir> \ + --normalize-before + +# ESPnet2 case +$ parallel-wavegan-decode \ + --checkpoint pretrain_model/<pretrain_model_tag>/checkpoint-400000steps.pkl \ + --feats-scp exp/<your_model_dir>/decode_*/<set_name>/denorm/feats.scp \ + --outdir <path_to_outdir> \ + --normalize-before + +# You can find the generated waveforms in <path_to_outdir>/. +$ ls <path_to_outdir> + utt_id_1_gen.wav utt_id_2_gen.wav ... utt_id_N_gen.wav +``` + +If you want to combine these models in python, you can try the real-time demonstration in Google Colab! +- Real-time demonstration with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb) +- Real-time demonstration with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb) + +### Decoding with dumped npy files + +Sometimes we want to decode with dumped npy files, which are mel-spectrogram generated by TTS models. +Please make sure you used the same feature extraction settings of the pretrained vocoder (`fs`, `fft_size`, `hop_size`, `win_length`, `fmin`, and `fmax`). +Only the difference of `log_base` can be changed with some post-processings (we use log 10 instead of natural log as a default). +See detail in [the comment](https://github.com/kan-bayashi/ParallelWaveGAN/issues/169#issuecomment-649320778). + +```bash +# Generate dummy npy file of mel-spectrogram +$ ipython +[ins] In [1]: import numpy as np +[ins] In [2]: x = np.random.randn(512, 80) # (#frames, #mels) +[ins] In [3]: np.save("dummy_1.npy", x) +[ins] In [4]: y = np.random.randn(256, 80) # (#frames, #mels) +[ins] In [5]: np.save("dummy_2.npy", y) +[ins] In [6]: exit + +# Make scp file (key-path format) +$ find -name "*.npy" | awk '{print "dummy_" NR " " $1}' > feats.scp + +# Check (<utt_id> <path>) +$ cat feats.scp +dummy_1 ./dummy_1.npy +dummy_2 ./dummy_2.npy + +# Decode without feature normalization +# This case assumes that the input mel-spectrogram is normalized with the same statistics of the pretrained model. +$ parallel-wavegan-decode \ + --checkpoint /path/to/checkpoint-400000steps.pkl \ + --feats-scp ./feats.scp \ + --outdir wav +2021-08-10 09:13:07,624 (decode:140) INFO: The number of features to be decoded = 2. +[decode]: 100%|████████████████████████████████████████| 2/2 [00:00<00:00, 13.84it/s, RTF=0.00264] +2021-08-10 09:13:29,660 (decode:174) INFO: Finished generation of 2 utterances (RTF = 0.005). + +# Decode with feature normalization +# This case assumes that the input mel-spectrogram is not normalized. +$ parallel-wavegan-decode \ + --checkpoint /path/to/checkpoint-400000steps.pkl \ + --feats-scp ./feats.scp \ + --normalize-before \ + --outdir wav +2021-08-10 09:13:07,624 (decode:140) INFO: The number of features to be decoded = 2. +[decode]: 100%|████████████████████████████████████████| 2/2 [00:00<00:00, 13.84it/s, RTF=0.00264] +2021-08-10 09:13:29,660 (decode:174) INFO: Finished generation of 2 utterances (RTF = 0.005). +``` + +## Notes + +The terms of use of the pretrained model follow that of each corpus used for the training. Please carefully check by yourself. + +## References + +- [Parallel WaveGAN](https://arxiv.org/abs/1910.11480) +- [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder) +- [LiyuanLucasLiu/RAdam](https://github.com/LiyuanLucasLiu/RAdam) +- [MelGAN](https://arxiv.org/abs/1910.06711) +- [descriptinc/melgan-neurips](https://github.com/descriptinc/melgan-neurips) +- [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) +- [HiFi-GAN](https://arxiv.org/abs/2010.05646) +- [jik876/hifi-gan](https://github.com/jik876/hifi-gan) +- [StyleMelGAN](https://arxiv.org/abs/2011.01557) + +## Acknowledgement + +The author would like to thank Ryuichi Yamamoto ([@r9y9](https://github.com/r9y9)) for his great repository, paper, and valuable discussions. + +## Author + +Tomoki Hayashi ([@kan-bayashi](https://github.com/kan-bayashi)) +E-mail: `hayashi.tomoki<at>g.sp.m.is.nagoya-u.ac.jp` + +%prep +%autosetup -n parallel-wavegan-0.5.5 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-parallel-wavegan -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.5-1 +- Package Spec generated @@ -0,0 +1 @@ +83712e3a256c7a0740ed9f0d24a9a173 parallel_wavegan-0.5.5.tar.gz |