# espnet

**Repository Path**: 397667796/espnet

## Basic Information

- **Project Name**: espnet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-10
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="left"><img src="doc/image/espnet_logo1.png" width="550"/></div>

# ESPnet: end-to-end speech processing toolkit

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[**Docs**](https://espnet.github.io/espnet/)
| [**Example**](https://github.com/espnet/espnet/tree/master/egs)
| [**Docker**](https://github.com/espnet/espnet/tree/master/docker)
| [**Notebook**](https://github.com/espnet/notebook)
| [**Tutorial (2019)**](https://github.com/espnet/interspeech2019-tutorial)

ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition and end-to-end text-to-speech.
ESPnet uses [chainer](https://chainer.org/) and [pytorch](http://pytorch.org/) as a main deep learning engine,
and also follows [Kaldi](http://kaldi-asr.org/) style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments.

## Key Features

### Kaldi style complete recipe
- Support numbers of `ASR` recipes (WSJ, Switchboard, CHiME-4/5, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, REVERB, etc.)
- Support numbers of `TTS` recipes with a similar manner to the ASR recipe (LJSpeech, LibriTTS, M-AILABS, etc.)
- Support numbers of `ST` recipes (Fisher-CallHome Spanish, Libri-trans, IWSLT'18, How2, Must-C, Mboshi-French, etc.)
- Support numbers of `MT` recipes (IWSLT'16, the above ST recipes etc.)
- Support speech separation and recognition recipe (WSJ-2mix)


### ASR: Automatic Speech Recognition

- **State-of-the-art performance** in several ASR benchmarks (comparable/superior to hybrid DNN/HMM and CTC)
- **Hybrid CTC/attention** based end-to-end ASR
  - Fast/accurate training with CTC/attention multitask training
  - CTC/attention joint decoding to boost monotonic alignment decoding
  - Encoder: VGG-like CNN + BiRNN (LSTM/GRU), sub-sampling BiRNN (LSTM/GRU) or Transformer
- Attention: Dot product, location-aware attention, variants of multihead
- Incorporate RNNLM/LSTMLM/TransformerLM trained only with text data
- Batch GPU decoding
- **Transducer** based end-to-end ASR
  - Available: RNN-Transducer, Transformer-Transducer, Transformer/RNN-Transducer
  - Support attention extension and VGG-Transformer (encoder)

### TTS: Text-to-speech
- Tacotron2 based end-to-end TTS
- Transformer based end-to-end TTS
- Feed-forward Transformer (a.k.a. FastSpeech) based end-to-end TTS (new!)

### ST: Speech Translation & MT: Machine Translation
- **State-of-the-art performance** in several ST benchmarks (comparable/superior to cascaded ASR and MT)
- Transformer based end-to-end ST (new!)
- Transformer based end-to-end MT (new!)

### DNN Framework
- Flexible network architecture thanks to chainer and pytorch
- Flexible front-end processing thanks to [kaldiio](https://github.com/nttcslab-sp/kaldiio) and HDF5 support
- Tensorboard based monitoring

## Installation
See https://espnet.github.io/espnet/installation.html

## Usage
See https://espnet.github.io/espnet/tutorial.html

## Docker Container

go to [docker/](docker/) and follow [instructions](https://espnet.github.io/espnet/docker.html).

## Contribution
Any contributions to ESPNet are welcome and feel free to ask any questions or requests to [issues](https://github.com/espnet/espnet/issues).
If you are the first commiter, please follow the [contribution guide](CONTRIBUTING.md).

## Results and demo

You can find useful tutorials and demos in [Interspeech 2019 Tutorial](https://github.com/espnet/interspeech2019-tutorial)

### ASR results

We list the character error rate (CER) and word error rate (WER) of major ASR tasks.

| Task                   | CER (%) | WER (%) | Pretrained model                                                                                                                                                      |
| -----------            | :----:  | :----:  | :----:                                                                                                                                                                |
| Aishell dev            | 6.0     | N/A     | [link](https://github.com/espnet/espnet/blob/master/egs/aishell/asr1/RESULTS.md#transformer-result-default-transformer-with-initial-learning-rate--10-and-epochs--50) |
| Aishell test           | 6.7     | N/A     | same as above                                                                                                                                                         |
| Common Voice dev       | 1.7     | 2.2     | [link](https://github.com/espnet/espnet/blob/master/egs/commonvoice/asr1/RESULTS.md#first-results-default-pytorch-transformer-setting-with-bpe-100-epochs-single-gpu) |
| Common Voice test      | 1.8     | 2.3     | same as above                                                                                                                                                         |
| CSJ eval1              | 5.7     | N/A     | [link](https://github.com/espnet/espnet/blob/master/egs/csj/asr1/RESULTS.md#pytorch-backend-transformer-without-any-hyperparameter-tuning)                            |
| CSJ eval2              | 3.8     | N/A     | same as above                                                                                                                                                         |
| CSJ eval3              | 4.2     | N/A     | same as above                                                                                                                                                         |
| HKUST dev              | 23.5    | N/A     | [link](https://github.com/espnet/espnet/blob/master/egs/hkust/asr1/RESULTS.md#transformer-only-20-epochs)                                                             |
| Librispeech dev_clean  | N/A     | 2.1     | [link](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-transformer-with-specaug-4-gpus--transformer-lm-4-gpus)             |
| Librispeech dev_other  | N/A     | 5.3     | same as above                                                                                                                                                         |
| Librispeech test_clean | N/A     | 2.5     | same as above                                                                                                                                                         |
| Librispeech test_other | N/A     | 5.5     | same as above                                                                                                                                                         |
| TEDLIUM2 dev           | N/A     | 9.3     | [link](https://github.com/espnet/espnet/blob/master/egs/tedlium2/asr1/RESULTS.md#transformer-large-model--specaug--large-lm)                                          |
| TEDLIUM2 test          | N/A     | 8.1     | same as above                                                                                                                                                         |
| TEDLIUM3 dev           | N/A     | 9.7     | [link](https://github.com/espnet/espnet/blob/master/egs/tedlium3/asr1/RESULTS.md#transformer-elayers12-dlayers6-units2048-8-gpus-specaug--large-lm)                   |
| TEDLIUM3 test          | N/A     | 8.0     | same as above                                                                                                                                                         |
| WSJ dev93              | 3.2     | 7.0     | N/A                                                                                                                                                                   |
| WSJ eval92             | 2.1     | 4.7     | N/A                                                                                                                                                                   |

Note that the performance of the CSJ, HKUST, and Librispeech tasks was significantly improved by using the wide network (#units = 1024) and large subword units if necessary reported by [RWTH](https://arxiv.org/pdf/1805.03294.pdf).

If you want to check the results of the other recipes, please check `egs/<name_of_recipe>/asr1/RESULTS.md`.

### ASR demo

You can recognize speech in a WAV file using pretrained models.
Go to a recipe directory and run `utils/recog_wav.sh` as follows:
```sh
cd egs/tedlium2/asr1
../../../utils/recog_wav.sh --models tedlium2.transformer.v1 example.wav
```
where `example.wav` is a WAV file to be recognized.
The sampling rate must be consistent with that of data used in training.

Available pretrained models in the demo script are listed as below.

| Model                                                                                            | Notes                                                      |
| :------                                                                                          | :------                                                    |
| [tedlium2.rnn.v1](https://drive.google.com/open?id=1UqIY6WJMZ4sxNxSugUqp3mrGb3j6h7xe)            | Streaming decoding based on CTC-based VAD                  |
| [tedlium2.rnn.v2](https://drive.google.com/open?id=1cac5Uc09lJrCYfWkLQsF8eapQcxZnYdf)            | Streaming decoding based on CTC-based VAD (batch decoding) |
| [tedlium2.transformer.v1](https://drive.google.com/open?id=1mgbiWabOSkh_oHJIDA-h7hekQ3W95Z_U)    | Joint-CTC attention Transformer trained on Tedlium 2       |
| [tedlium3.transformer.v1](https://drive.google.com/open?id=1wYYTwgvbB7uy6agHywhQfnuVWWW_obmO)    | Joint-CTC attention Transformer trained on Tedlium 3       |
| [librispeech.transformer.v1](https://drive.google.com/open?id=1BtQvAnsFvVi-dp_qsaFP7n4A_5cwnlR6) | Joint-CTC attention Transformer trained on Librispeech     |
| [commonvoice.transformer.v1](https://drive.google.com/open?id=1tWccl6aYU67kbtkm8jv5H6xayqg1rzjh) | Joint-CTC attention Transformer trained on CommonVoice     |
| [csj.transformer.v1](https://drive.google.com/open?id=120nUQcSsKeY5dpyMWw_kI33ooMRGT2uF)         | Joint-CTC attention Transformer trained on CSJ             |


### ST results

We list 4-gram BLEU of major ST tasks.

#### end-to-end system
| Task | BLEU | Pretrained model |
| ---- | :----: | :----: |
| Fisher-CallHome Spanish fisher_test (Es->En)      | 48.39 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/st1/RESULTS.md#train_spen_lcrm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans) |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 18.67 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/st1/RESULTS.md#train_spen_lcrm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans) |
| Libri-trans test (En->Fr)                         | 16.70 | [link](https://github.com/espnet/espnet/blob/master/egs/libri_trans/st1/RESULTS.md#train_spfr_lc_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans-1) |
| How2 dev5 (En->Pt)                                | 45.68 | [link](https://github.com/espnet/espnet/blob/master/egs/how2/st1/RESULTS.md#trainpt_tc_pytorch_train_pytorch_transformer_short_long_bpe8000_specaug_asrtrans_mttrans-1) |
| Must-C tst-COMMON (En->De)                        | 22.91 | [link](https://github.com/espnet/espnet/blob/master/egs/must_c/st1/RESULTS.md#train_spen-dede_tc_pytorch_train_pytorch_transformer_short_long_bpe8000_specaug_asrtrans_mttrans) |
| Mboshi-French dev (Fr->Mboshi)                    | 6.18  | N/A  |

#### cascaded system
| Task | BLEU | Pretrained model |
| ---- | :----: | :----: |
| Fisher-CallHome Spanish fisher_test (Es->En)      | 42.16 | N/A  |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 19.82 | N/A  |
| Libri-trans test (En->Fr)                         | 16.96 | N/A  |
| How2 dev5 (En->Pt)                                | 44.90 | N/A  |
| Must-C tst-COMMON (En->De)                        | 23.65 | N/A  |

If you want to check the results of the other recipes, please check `egs/<name_of_recipe>/st1/RESULTS.md`.

### ST demo

(**New!**) We made a new real-time E2E-ST + TTS demonstration in Google Colab.
Please access the notebook from the following button and enjoy the real-time speech-to-speech translation!

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/espnet/notebook/blob/master/st_demo.ipynb)

---

You can translate speech in a WAV file using pretrained models.
Go to a recipe directory and run `utils/translate_wav.sh` as follows:
```sh
cd egs/fisher_callhome_spanish/st1/
wget -O - https://github.com/espnet/espnet/files/4100928/test.wav.tar.gz | tar zxvf - ../../../utils/translate_wav.sh --models fisher_callhome_spanish.transformer.v1.es-en test.wav
```
where `test.wav` is a WAV file to be translated.
The sampling rate must be consistent with that of data used in training.

Available pretrained models in the demo script are listed as below.

| Model                                                                                            | Notes                                                      |
| :------                                                                                          | :------                                                    |
| [fisher_callhome_spanish.transformer.v1](https://drive.google.com/open?id=1hawp5ZLw4_SIHIT3edglxbKIIkPVe8n3)            | Transformer-ST trained on Fisher-CallHome Spanish Es->En                  |


### MT results

| Task | BLEU | Pretrained model |
| ---- | :----: | :----: |
| Fisher-CallHome Spanish fisher_test (Es->En)      | 61.45 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/mt1/RESULTS.md#trainen_lcrm_lcrm_pytorch_train_pytorch_transformer_bpe_bpe1000) |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 29.86 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/mt1/RESULTS.md#trainen_lcrm_lcrm_pytorch_train_pytorch_transformer_bpe_bpe1000) |
| Libri-trans test (En->Fr)                         | 18.09 | [link](https://github.com/espnet/espnet/blob/master/egs/libri_trans/mt1/RESULTS.md#trainfr_lcrm_tc_pytorch_train_pytorch_transformer_bpe1000) |
| How2 dev5 (En->Pt)                                | 58.61 | [link](https://github.com/espnet/espnet/blob/master/egs/how2/mt1/RESULTS.md#trainpt_tc_tc_pytorch_train_pytorch_transformer_bpe8000) |
| Must-C tst-COMMON (En->De)                        | 27.63 | [link](https://github.com/espnet/espnet/blob/master/egs/must_c/mt1/RESULTS.md#summary-4-gram-bleu) |
| IWSLT'14 test2014 (En->De)                        | 24.70 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
| IWSLT'14 test2014 (De->En)                        | 29.22 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
| IWSLT'16 test2014 (En->De)                        | 24.05 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
| IWSLT'16 test2014 (De->En)                        | 29.13 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |


### TTS results

You can listen to our samples in demo HP [espnet-tts-sample](https://espnet.github.io/espnet-tts-sample/).
Here we list some notable ones:

- [Single English speaker Tacotron2](https://drive.google.com/open?id=18JgsOCWiP_JkhONasTplnHS7yaF_konr)
- [Single Japanese speaker Tacotron2](https://drive.google.com/open?id=1fEgS4-K4dtgVxwI4Pr7uOA1h4PE-zN7f)
- [Single other language speaker Tacotron2](https://drive.google.com/open?id=1q_66kyxVZGU99g8Xb5a0Q8yZ1YVm2tN0)
- [Multi English speaker Tacotron2](https://drive.google.com/open?id=18S_B8Ogogij34rIfJOeNF8D--uG7amz2)
- [Single English speaker Transformer](https://drive.google.com/open?id=14EboYVsMVcAq__dFP1p6lyoZtdobIL1X)
- [Single English speaker FastSpeech](https://drive.google.com/open?id=1PSxs1VauIndwi8d5hJmZlppGRVu2zuy5)
- [Multi English speaker Transformer](https://drive.google.com/open?id=1_vrdqjM43DdN1Qz7HJkvMQ6lCMmWLeGp)
- [Single Italian speaker FastSpeech](https://drive.google.com/open?id=13I5V2w7deYFX4DlVk1-0JfaXmUR2rNOv)
- [Single Mandarin speaker Transformer](https://drive.google.com/open?id=1mEnZfBKqA4eT6Bn0eRZuP6lNzL-IL3VD)
- [Single Mandarin speaker FastSpeech](https://drive.google.com/open?id=1Ol_048Tuy6BgvYm1RpjhOX4HfhUeBqdK)
- [Multi Japanese speaker Transformer](https://drive.google.com/open?id=1fFMQDF6NV5Ysz48QLFYE8fEvbAxCsMBw)
- [Single English speaker models with Parallel WaveGAN](https://drive.google.com/open?id=1HvB0_LDf1PVinJdehiuCt5gWmXGguqtx)
- [Single English speaker knowledge distillation-based FastSpeech (New!)](https://drive.google.com/open?id=1wG-Y0itVYalxuLAHdkAHO7w1CWFfRPF4)

You can download all of the pretrained models and generated samples:
- [All of the pretrained E2E-TTS models](https://drive.google.com/open?id=1k9RRyc06Zl0mM2A7mi-hxNiNMFb_YzTF)
- [All of the generated samples](https://drive.google.com/open?id=1bQGuqH92xuxOX__reWLP4-cif0cbpMLX)

Note that in the generated samples we use three vocoders: Griffin-Lim (**GL**), WaveNet vocoder (**WaveNet**), Parallel WaveGAN (**ParallelWaveGAN**), and MelGAN (**MelGAN**).
The neural vocoders are based on following repositories.
- [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN): Parallel WaveGAN / MelGAN
- [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder): 16 bit mixture of Logistics WaveNet vocoder
- [kan-bayashi/PytorchWaveNetVocoder](https://github.com/kan-bayashi/PytorchWaveNetVocoder): 8 bit Softmax WaveNet Vocoder with the noise shaping

If you want to build your own neural vocoder, please check the above repositories.

Here we list all of the pretrained neural vocoders. Please download and enjoy the generation of high quality speech!

| Model link                                                                                              | Lang  | Fs [Hz] | Mel range [Hz] | FFT / Shift / Win [pt] | Model type                                                              |
| :------                                                                                                 | :---: | :----:  | :--------:     | :---------------:      | :------                                                                 |
| [ljspeech.wavenet.softmax.ns.v1](https://drive.google.com/open?id=1eA1VcRS9jzFa-DovyTgJLQ_jmwOLIi8L)    | EN    | 22.05k  | None           | 1024 / 256 / None      | [Softmax WaveNet](https://github.com/kan-bayashi/PytorchWaveNetVocoder) |
| [ljspeech.wavenet.mol.v1](https://drive.google.com/open?id=1sY7gEUg39QaO1szuN62-Llst9TrFno2t)           | EN    | 22.05k  | None           | 1024 / 256 / None      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [ljspeech.parallel_wavegan.v1](https://drive.google.com/open?id=1tv9GKyRT4CDsvUWKwH3s_OfXkiTi0gw7)      | EN    | 22.05k  | None           | 1024 / 256 / None      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |
| [ljspeech.wavenet.mol.v2](https://drive.google.com/open?id=1es2HuKUeKVtEdq6YDtAsLNpqCy4fhIXr)           | EN    | 22.05k  | 80-7600        | 1024 / 256 / None      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [ljspeech.parallel_wavegan.v2](https://drive.google.com/open?id=1Grn7X9wD35UcDJ5F7chwdTqTa4U7DeVB)      | EN    | 22.05k  | 80-7600        | 1024 / 256 / None      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |
| [ljspeech.melgan.v1 (EXPERIMENTAL)](https://drive.google.com/open?id=1ipPWYl8FBNRlBFaKj1-i23eQpW_W_YcR) | EN    | 22.05k  | 80-7600        | 1024 / 256 / None      | [MelGAN](https://github.com/kan-bayashi/ParallelWaveGAN)                |
| [libritts.wavenet.mol.v1](https://drive.google.com/open?id=1jHUUmQFjWiQGyDd7ZeiCThSjjpbF_B4h)           | EN    | 24k     | None           | 1024 / 256 / None      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [jsut.wavenet.mol.v1](https://drive.google.com/open?id=187xvyNbmJVZ0EZ1XHCdyjZHTXK9EcfkK)               | JP    | 24k     | 80-7600        | 2048 / 300 / 1200      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [jsut.parallel_wavegan.v1](https://drive.google.com/open?id=1OwrUQzAmvjj1x9cDhnZPp6dqtsEqGEJM)          | JP    | 24k     | 80-7600        | 2048 / 300 / 1200      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |
| [csmsc.wavenet.mol.v1](https://drive.google.com/open?id=1PsjFRV5eUP0HHwBaRYya9smKy5ghXKzj)              | ZH    | 24k     | 80-7600        | 2048 / 300 / 1200      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [csmsc.parallel_wavegan.v1](https://drive.google.com/open?id=10M6H88jEUGbRWBmU1Ff2VaTmOAeL8CEy)         | ZH    | 24k     | 80-7600        | 2048 / 300 / 1200      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |

If you want to use the above pretrained vocoders, please exactly match the feature setting with them.


### TTS demo

(**New!**) We made a new real-time E2E-TTS demonstration in Google Colab.
Please access the notebook from the following button and enjoy the real-time synthesis!

[![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)

---

You can synthesize speech in a TXT file using pretrained models.
Go to a recipe directory and run `utils/synth_wav.sh` as follows:

```sh
cd egs/ljspeech/tts1
echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example.txt
../../../utils/synth_wav.sh example.txt
```

You can change the pretrained model as follows:

```sh
../../../utils/synth_wav.sh --models ljspeech.fastspeech.v1 example.txt
```

Waveform synthesis is performed with Griffin-Lim algorithm and neural vocoders (WaveNet and ParallelWaveGAN).
You can change the pretrained vocoder model as follows:

```
../../../utils/synth_wav.sh --vocoder_models ljspeech.wavenet.mol.v1 example.txt
```

Note that WaveNet vocoder provides very high quality speech but it takes time to generate.

Available pretrained models in the demo script are listed as follows:

| Model link                                                                                    | Lang  | Fs [Hz] | Mel range [Hz] | FFT / Shift / Win [pt] | Input  | R   | Model type                                  |
| :------                                                                                       | :---: | :----:  | :--------:     | :---------------:      | :---:  | :-: | :------                                     |
| [ljspeech.tacotron2.v1](https://drive.google.com/open?id=1dKzdaDpOkpx7kWZnvrvx2De7eZEdPHZs)   | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 2   | Tacotron 2                                  |
| [ljspeech.tacotron2.v2](https://drive.google.com/open?id=11T9qw8rJlYzUdXvFjkjQjYrp3iGfQ15h)   | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 1   | Tacotron 2 + forward attention              |
| [ljspeech.tacotron2.v3](https://drive.google.com/open?id=1hiZn14ITUDM1nkn-GkaN_M3oaTOUcn1n)   | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 1   | Tacotron 2 + guided attention loss          |
| [ljspeech.transformer.v1](https://drive.google.com/open?id=13DR-RB5wrbMqBGx_MC655VZlsEq52DyS) | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 1   | Deep Transformer                            |
| [ljspeech.transformer.v2](https://drive.google.com/open?id=1xxAwPuUph23RnlC5gym7qDM02ZCW9Unp) | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 3   | Shallow Transformer                         |
| [ljspeech.transformer.v3](https://drive.google.com/open?id=1M_w7nxI6AfbtSHpMO-exILnAc_aUYvXP) | EN    | 22.05k  | None           | 1024 / 256 / None      | phn    | 1   | Deep Transformer                            |
| [ljspeech.fastspeech.v1](https://drive.google.com/open?id=17RUNFLP4SSTbGA01xWRJo7RkR876xM0i)  | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 1   | FF-Transformer                              |
| [ljspeech.fastspeech.v2](https://drive.google.com/open?id=1zD-2GMrWM3thaDpS3h3rkTU4jIC0wc5B)  | EN    | 22.05k  | None           | 1024 / 256 / None      | char   | 1   | FF-Transformer + CNN in FFT block           |
| [ljspeech.fastspeech.v3](https://drive.google.com/open?id=1W86YEQ6KbuUTIvVURLqKtSNqe_eI2GDN)  | EN    | 22.05k  | None           | 1024 / 256 / None      | phn    | 1   | FF-Transformer + CNN in FFT block + postnet |
| [libritts.tacotron2.v1](https://drive.google.com/open?id=1iAXwC0AuWusa9AcFeUVkcNLG0I-hnSr3)   | EN    | 24k     | 80-7600        | 1024 / 256 / None      | char   | 2   | Multi-speaker Tacotron 2                    |
| [libritts.transformer.v1](https://drive.google.com/open?id=1Xj73mDPuuPH8GsyNO8GnOC3mn0_OK4g3) | EN    | 24k     | 80-7600        | 1024 / 256 / None      | char   | 2   | Multi-speaker Transformer                   |
| [jsut.tacotron2](https://drive.google.com/open?id=1kp5M4VvmagDmYckFJa78WGqh1drb_P9t)          | JP    | 24k     | 80-7600        | 2048 / 300 / 1200      | phn    | 2   | Tacotron 2                                  |
| [jsut.transformer](https://drive.google.com/open?id=1mEnZfBKqA4eT6Bn0eRZuP6lNzL-IL3VD)        | JP    | 24k     | 80-7600        | 2048 / 300 / 1200      | phn    | 3   | Shallow Transformer                         |
| [csmsc.transformer.v1](https://drive.google.com/open?id=1bTSygvonv5TS6-iuYsOIUWpN2atGnyhZ)    | ZH    | 24k     | 80-7600        | 2048 / 300 / 1200      | pinyin | 1   | Deep Transformer                            |
| [csmsc.fastspeech.v3](https://drive.google.com/open?id=1T8thxkAxjGFPXPWPTcKLvHnd6lG0-82R)     | ZH    | 24k     | 80-7600        | 2048 / 300 / 1200      | pinyin | 1   | FF-Transformer + CNN in FFT block + postnet |

Available pretrained vocoder models in the demo script are listed as follows:

| Model link                                                                                           | Lang  | Fs [Hz] | Mel range [Hz] | FFT / Shift / Win [pt] | Model type                                                              |
| :------                                                                                              | :---: | :----:  | :--------:     | :---------------:      | :------                                                                 |
| [ljspeech.wavenet.softmax.ns.v1](https://drive.google.com/open?id=1eA1VcRS9jzFa-DovyTgJLQ_jmwOLIi8L) | EN    | 22.05k  | None           | 1024 / 256 / None      | [Softmax WaveNet](https://github.com/kan-bayashi/PytorchWaveNetVocoder) |
| [ljspeech.wavenet.mol.v1](https://drive.google.com/open?id=1sY7gEUg39QaO1szuN62-Llst9TrFno2t)        | EN    | 22.05k  | None           | 1024 / 256 / None      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [ljspeech.parallel_wavegan.v1](https://drive.google.com/open?id=1tv9GKyRT4CDsvUWKwH3s_OfXkiTi0gw7)   | EN    | 22.05k  | None           | 1024 / 256 / None      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |
| [libritts.wavenet.mol.v1](https://drive.google.com/open?id=1jHUUmQFjWiQGyDd7ZeiCThSjjpbF_B4h)        | EN    | 24k     | None           | 1024 / 256 / None      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [jsut.wavenet.mol.v1](https://drive.google.com/open?id=187xvyNbmJVZ0EZ1XHCdyjZHTXK9EcfkK)            | JP    | 24k     | 80-7600        | 2048 / 300 / 1200      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [jsut.parallel_wavegan.v1](https://drive.google.com/open?id=1OwrUQzAmvjj1x9cDhnZPp6dqtsEqGEJM)       | JP    | 24k     | 80-7600        | 2048 / 300 / 1200      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |
| [csmsc.wavenet.mol.v1](https://drive.google.com/open?id=1PsjFRV5eUP0HHwBaRYya9smKy5ghXKzj)           | ZH    | 24k     | 80-7600        | 2048 / 300 / 1200      | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder)                  |
| [csmsc.parallel_wavegan.v1](https://drive.google.com/open?id=10M6H88jEUGbRWBmU1Ff2VaTmOAeL8CEy)      | ZH    | 24k     | 80-7600        | 2048 / 300 / 1200      | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)      |


## References

[1] Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, and Tsubasa Ochiai, "ESPnet: End-to-End Speech Processing Toolkit," *Proc. Interspeech'18*, pp. 2207-2211 (2018)

[2] Suyoun Kim, Takaaki Hori, and Shinji Watanabe, "Joint CTC-attention based end-to-end speech recognition using multi-task learning," *Proc. ICASSP'17*, pp. 4835--4839 (2017)

[3] Shinji Watanabe, Takaaki Hori, Suyoun Kim, John R. Hershey and Tomoki Hayashi, "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition," *IEEE Journal of Selected Topics in Signal Processing*, vol. 11, no. 8, pp. 1240-1253, Dec. 2017

## Citations

```
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={ESPnet: End-to-End Speech Processing Toolkit},
  year=2018,
  booktitle={Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@misc{hayashi2019espnettts,
    title={ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit},
    author={Tomoki Hayashi and Ryuichi Yamamoto and Katsuki Inoue and Takenori Yoshimura and Shinji Watanabe and Tomoki Toda and Kazuya Takeda and Yu Zhang and Xu Tan},
    year={2019},
    eprint={1910.10909},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```