# ParallelWaveGAN **Repository Path**: cestbon-z/ParallelWaveGAN ## Basic Information - **Project Name**: ParallelWaveGAN - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-09-07 - **Last Updated**: 2024-09-07 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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 and singing voice synthesis 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) - Real-time demonstration with Muskits [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 [![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 ( ) $ 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`