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https://github.com/kan-bayashi/ParallelWaveGAN
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch
https://github.com/kan-bayashi/ParallelWaveGAN
hifigan melgan neural-vocoder parallel-wavenet pytorch realtime speech-synthesis style-melgan text-to-speech tts vocoder wavenet
Last synced: about 1 month ago
JSON representation
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch
- Host: GitHub
- URL: https://github.com/kan-bayashi/ParallelWaveGAN
- Owner: kan-bayashi
- License: mit
- Created: 2019-10-29T02:32:35.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-04-22T02:36:29.000Z (5 months ago)
- Last Synced: 2024-07-18T21:13:21.297Z (about 2 months ago)
- Topics: hifigan, melgan, neural-vocoder, parallel-wavenet, pytorch, realtime, speech-synthesis, style-melgan, text-to-speech, tts, vocoder, wavenet
- Language: Jupyter Notebook
- Homepage: https://kan-bayashi.github.io/ParallelWaveGAN/
- Size: 34.8 MB
- Stars: 1,526
- Watchers: 45
- Forks: 336
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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- StarryDivineSky - kan-bayashi/ParallelWaveGAN - band MelGAN) implementation with Pytorch (语音合成 / 网络服务_其他)
README
# 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
- 2023/08/17 [LibriTTS-R recipe](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/libritts_r/voc1) is available!
- 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 3.8+
- Cuda 11.0+
- CuDNN 8+
- 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 1.8.1, 1.9, 1.10.2, 1.11.0, 1.12.1, 1.13.1, 2.0.1 and 2.1.0.
## 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
- [LibriTTS-R](https://arxiv.org/abs/2305.18802): English multi-speaker enhanced by speech restoration.
- [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
# 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 //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 |
| [m4singer_hifigan.v1](https://drive.google.com/open?id=1dvD6imY6p2L80tN8tr_kzqUa3M7QJtLY) | [link](https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/egs/m4singer/voc1/conf/hifigan.v1.yaml) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | 1M |
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")
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//`
$ ls pretrain_model/
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//config.yml \
--rootdir sample \
--dumpdir dump/sample/raw
100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s]
$ parallel-wavegan-normalize \
--config pretrain_model//config.yml \
--rootdir dump/sample/raw \
--dumpdir dump/sample/norm \
--stats pretrain_model//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//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//config.yml \
--rootdir sample \
--dumpdir dump/sample/raw
100%|████████████████████████████████████████| 1/1 [00:00<00:00, 914.19it/s]
$ parallel-wavegan-decode \
--checkpoint pretrain_model//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//tts1
$ pwd
/path/to/espnet/egs//tts1
# If you use ESPnet2, move on `egs2/`
$ cd /path/to/espnet/egs2//tts1
$ pwd
/path/to/espnet/egs2//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")
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//`
$ ls pretrain_model/
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//outputs_*_decode//feats.scp.
# Note that do not use outputs_*decode_denorm//feats.scp since
# it is de-normalized features (the input for PWG is normalized features).
$ parallel-wavegan-decode \
--checkpoint pretrain_model//checkpoint-400000steps.pkl \
--feats-scp exp//outputs_*_decode//feats.scp \
--outdir
# In the case of ESPnet2, the generated feature can be found in
# exp//decode_*//norm/feats.scp.
$ parallel-wavegan-decode \
--checkpoint pretrain_model//checkpoint-400000steps.pkl \
--feats-scp exp//decode_*//norm/feats.scp \
--outdir
# You can find the generated waveforms in /.
$ ls
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//checkpoint-400000steps.pkl \
--feats-scp exp//outputs_*_decode_denorm//feats.scp \
--outdir \
--normalize-before
# ESPnet2 case
$ parallel-wavegan-decode \
--checkpoint pretrain_model//checkpoint-400000steps.pkl \
--feats-scp exp//decode_*//denorm/feats.scp \
--outdir \
--normalize-before
# You can find the generated waveforms in /.
$ ls
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 ( )
$ 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.
- Some codes are derived from ESPnet or Kaldi, which are based on Apache-2.0 licenese.
## 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.tomokig.sp.m.is.nagoya-u.ac.jp`