Upload ParallelWaveGAN/README.md

ParallelWaveGAN/README.md

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+# Parallel WaveGAN implementation with Pytorch
+
+![](https://github.com/kan-bayashi/ParallelWaveGAN/workflows/CI/badge.svg) [![](https://img.shields.io/pypi/v/parallel-wavegan)](https://pypi.org/project/parallel-wavegan/) ![](https://img.shields.io/pypi/pyversions/parallel-wavegan) ![](https://img.shields.io/pypi/l/parallel-wavegan) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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).
+
+![](https://user-images.githubusercontent.com/22779813/68081503-4b8fcf00-fe52-11e9-8791-e02851220355.png)
+
+> 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 demonstration in Google Colab!
+- Real-time demonstration with ESPnet2  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb)
+- Real-time demonstration with ESPnet1  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb)
+
+## What's new
+
+- 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 3.6+
+- 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, and 1.9.
+
+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)
+
+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    |
+
+Please access at [our google drive](https://drive.google.com/open?id=1sd_QzcUNnbiaWq7L0ykMP7Xmk-zOuxTi) to check more results.
+
+## 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  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb)
+- Real-time demonstration with ESPnet1  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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).
+```
+
+## 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`