# espnet **Repository Path**: quminzi/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**: 2020-09-11 - **Last Updated**: 2021-03-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
# ESPnet: end-to-end speech processing toolkit |system/pytorch ver.|1.0.1|1.1.0|1.2.0|1.3.1|1.4.0|1.5.1|1.6.0|1.7.1|1.8.1| | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |ubuntu18/python3.8/pip|||||||||[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)| |ubuntu18/python3.7/pip|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)|[![Github Actions](https://github.com/espnet/espnet/workflows/CI/badge.svg)](https://github.com/espnet/espnet/actions)| |debian9/python3.6/conda|||||||||[![debian9](https://github.com/espnet/espnet/workflows/debian9/badge.svg)](https://github.com/espnet/espnet/actions?query=workflow%3Adebian9)| |centos7/python3.6/conda|||||||||[![centos7](https://github.com/espnet/espnet/workflows/centos7/badge.svg)](https://github.com/espnet/espnet/actions?query=workflow%3Acentos7)| |[docs/coverage] python3.8|||||||||[![Build Status](https://travis-ci.org/espnet/espnet.svg?branch=master)](https://travis-ci.org/espnet/espnet)| [![PyPI version](https://badge.fury.io/py/espnet.svg)](https://badge.fury.io/py/espnet) [![Python Versions](https://img.shields.io/pypi/pyversions/espnet.svg)](https://pypi.org/project/espnet/) [![Downloads](https://pepy.tech/badge/espnet)](https://pepy.tech/project/espnet) [![GitHub license](https://img.shields.io/github/license/espnet/espnet.svg)](https://github.com/espnet/espnet) [![codecov](https://codecov.io/gh/espnet/espnet/branch/master/graph/badge.svg)](https://codecov.io/gh/espnet/espnet) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Mergify Status](https://img.shields.io/endpoint.svg?url=https://gh.mergify.io/badges/espnet/espnet&style=flat)](https://mergify.io) [![Gitter](https://badges.gitter.im/espnet-en/community.svg)](https://gitter.im/espnet-en/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [**Docs**](https://espnet.github.io/espnet/) | [**Example**](https://github.com/espnet/espnet/tree/master/egs) | [**Example (ESPnet2)**](https://github.com/espnet/espnet/tree/master/egs2) | [**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) - Support voice conversion recipe (VCC2020 baseline) (new!) ### 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/N-gram trained only with text data - Batch GPU decoding - **Transducer** based end-to-end ASR - Available: RNN-based encoder/decoder or custom encoder/decoder w/ supports for Transformer, Conformer, TDNN (encoder) and causal conv1d (decoder) blocks. - Also support: mixed RNN/Custom encoder-decoder, VGG2L (RNN/Cutom encoder) and various decoding algorithms. > Please refer to the [tutorial page](https://espnet.github.io/espnet/tutorial.html#transducer) for complete documentation. - CTC segmentation - Non-autoregressive based on Mask CTC - ASR examples for supporting endangered language documentation (Please refer to egs/puebla_nahuatl and egs/yoloxochitl_mixtec for details) - Wav2Vec2.0 pretrained model as Encoder, imported from [FairSeq](https://github.com/pytorch/fairseq/tree/master/fairseq). ### TTS: Text-to-speech - Tacotron2 - Transformer-TTS - FastSpeech - FastSpeech2 (in ESPnet2) - Conformer-based FastSpeech & FastSpeech2 (in ESPnet2) - Multi-speaker model with pretrained speaker embedding - Multi-speaker model with GST (in ESPnet2) - Phoneme-based training (En, Jp, and Zn) - Integration with neural vocoders (WaveNet, ParallelWaveGAN, and MelGAN) You can try demo online now! - Real-time TTS demo 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 TTS demo 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) To train the neural vocoder, please check the following repositories: - [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) - [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder) > **NOTE**: > - We are moving on ESPnet2-based development for TTS. > - If you are beginner, we recommend using [ESPnet2-TTS](https://github.com/espnet/espnet/tree/master/egs2/TEMPLATE/tts1). ### 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!) ### VC: Voice conversion - Transformer and Tacotron2 based parallel VC using melspectrogram (new!) - End-to-end VC based on cascaded ASR+TTS (Baseline system for Voice Conversion Challenge 2020!) ### 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 ### ESPnet2 See [ESPnet2](https://espnet.github.io/espnet/espnet2_tutorial.html). - Indepedent from Kaldi/Chainer, unlike ESPnet1 - On the fly feature extraction and text processing when training - Supporting DistributedDataParallel and DaraParallel both - Supporting multiple nodes training and integrated with [Slurm](https://slurm.schedmd.com/) or MPI - Supporting Sharded Training provided by [fairscale](https://github.com/facebookresearch/fairscale) - A template recipe which can be applied for all corpora - Possible to train any size of corpus without cpu memory error - [ESPnet Model Zoo](https://github.com/espnet/espnet_model_zoo) - Integrated with [wandb](https://espnet.github.io/espnet/espnet2_training_option.html#weights-biases-integration) ## Installation - If you intend to do full experiments including DNN training, then see [Installation](https://espnet.github.io/espnet/installation.html). - If you just need the Python module only: ```sh pip install espnet # To install latest # pip install git+https://github.com/espnet/espnet ``` You need to install some packages. ```sh pip install torch pip install chainer==6.0.0 cupy==6.0.0 # [Option] If you'll use ESPnet1 pip install torchaudio # [Option] If you'll use enhancement task pip install torch_optimizer # [Option] If you'll use additional optimizers in ESPnet2 ``` There are some required packages depending on each task other than above. If you meet ImportError, please intall them at that time. ## Usage See [Usage](https://espnet.github.io/espnet/tutorial.html). ## Docker Container go to [docker/](docker/) and follow [instructions](https://espnet.github.io/espnet/docker.html). ## Contribution Thank you for taking times for ESPnet! Any contributions to ESPNet are welcome and feel free to ask any questions or requests to [issues](https://github.com/espnet/espnet/issues). If it's the first contribution to ESPnet for you, 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
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We list the character error rate (CER) and word error rate (WER) of major ASR tasks. | Task | CER (%) | WER (%) | Pretrained model| | ----------- | :----: | :----: | :----: | | Aishell dev/test | 4.6/5.1 | N/A | [link](https://github.com/espnet/espnet/blob/master/egs/aishell/asr1/RESULTS.md#conformer-kernel-size--15--specaugment--lm-weight--00-result) | | **ESPnet2** Aishell dev/test | 4.4/4.7 | N/A | [link](https://github.com/espnet/espnet/tree/master/egs2/aishell/asr1#conformer--specaug--speed-perturbation-featsraw-n_fft512-hop_length128) | | Common Voice dev/test | 1.7/1.8 | 2.2/2.3 | [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) | | CSJ eval1/eval2/eval3 | 5.7/3.8/4.2 | N/A | [link](https://github.com/espnet/espnet/blob/master/egs/csj/asr1/RESULTS.md#pytorch-backend-transformer-without-any-hyperparameter-tuning) | | **ESPnet2** CSJ eval1/eval2/eval3 | 4.5/3.3/3.6 | N/A | [link](https://github.com/espnet/espnet/tree/master/egs2/csj/asr1#initial-conformer-results) | | HKUST dev | 23.5 | N/A | [link](https://github.com/espnet/espnet/blob/master/egs/hkust/asr1/RESULTS.md#transformer-only-20-epochs) | | **ESPnet2** HKUST dev | 21.2 | N/A | [link](https://github.com/espnet/espnet/tree/master/egs2/hkust/asr1#transformer-asr--transformer-lm) | | Librispeech dev_clean/dev_other/test_clean/test_other | N/A | 1.9/4.9/2.1/4.9 | [link](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-conformer-with-specaug--speed-perturbation-8-gpus--transformer-lm-4-gpus) | | **ESPnet2** Librispeech dev_clean/dev_other/test_clean/test_other | 0.7/2.2/0.7/2.1 | 1.9/4.6/2.1/4.7 | [link](https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1#with-transformer-lm) | | Switchboard (eval2000) callhm/swbd | N/A | 14.0/6.8 | [link](https://github.com/espnet/espnet/blob/master/egs/swbd/asr1/RESULTS.md#conformer-with-bpe-2000-specaug-speed-perturbation-transformer-lm-decoding) | | TEDLIUM2 dev/test | N/A | 8.6/7.2 | [link](https://github.com/espnet/espnet/blob/master/egs/tedlium2/asr1/RESULTS.md#conformer-large-model--specaug--speed-perturbation--rnnlm) | | TEDLIUM3 dev/test | N/A | 9.6/7.6 | [link](https://github.com/espnet/espnet/blob/master/egs/tedlium3/asr1/RESULTS.md) | | WSJ dev93/eval92 | 3.2/2.1 | 7.0/4.7 | N/A | | **ESPnet2** WSJ dev93/eval92 | 2.7/1.8 | 6.6/4.6 | [link](https://github.com/espnet/espnet/tree/master/egs2/wsj/asr1#using-transformer-lm-asr-model-is-same-as-the-above-lm_weight12-ctc_weight03-beam_size20) | 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//asr1/RESULTS.md`.
### ASR demo
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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 # go to recipe directory and source path of espnet tools cd egs/tedlium2/asr1 && . ./path.sh # let's recognize speech! 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=1cVeSOYY1twOfL9Gns7Z3ZDnkrJqNwPow) | Joint-CTC attention Transformer trained on Tedlium 2 | | [tedlium3.transformer.v1](https://drive.google.com/open?id=1zcPglHAKILwVgfACoMWWERiyIquzSYuU) | 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 | | [csj.rnn.v1](https://drive.google.com/open?id=1ALvD4nHan9VDJlYJwNurVr7H7OV0j2X9) | Joint-CTC attention VGGBLSTM trained on CSJ |
### ST results
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We list 4-gram BLEU of major ST tasks. #### end-to-end system | Task | BLEU | Pretrained model | | ---- | :----: | :----: | | Fisher-CallHome Spanish fisher_test (Es->En) | 51.03 | [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) | 20.44 | [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//st1/RESULTS.md`.
### ST demo
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(**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 # go to recipe directory and source path of espnet tools cd egs/fisher_callhome_spanish/st1 && . ./path.sh # download example wav file wget -O - https://github.com/espnet/espnet/files/4100928/test.wav.tar.gz | tar zxvf - # let's translate speech! 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
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| 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
ESPnet2
You can listen to the generated samples in the following url. - [ESPnet2 TTS generated samples](https://drive.google.com/drive/folders/1H3fnlBbWMEkQUfrHqosKN_ZX_WjO29ma?usp=sharing) > Note that in the generation we use Griffin-Lim (`wav/`) and [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) (`wav_pwg/`). You can download pretrained models via `espnet_model_zoo`. - [ESPnet model zoo](https://github.com/espnet/espnet_model_zoo) - [Pretrained model list](https://github.com/espnet/espnet_model_zoo/blob/master/espnet_model_zoo/table.csv) You can download pretrained vocoders via `kan-bayashi/ParallelWaveGAN`. - [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) - [Pretrained vocoder list](https://github.com/kan-bayashi/ParallelWaveGAN#results)
ESPnet1
> NOTE: We are moving on ESPnet2-based development for TTS. Please check the latest results in the above ESPnet2 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](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 the following 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 / Multi-band 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. [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) provides [the manual](https://github.com/kan-bayashi/ParallelWaveGAN#decoding-with-espnet-tts-models-features) about how to decode ESPnet-TTS model's features with neural vocoders. Please check it. 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](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) | | [ljspeech.melgan.v3](https://drive.google.com/open?id=1_a8faVA5OGCzIcJNw4blQYjfG4oA9VEt) | 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
ESPnet2
You can try the real-time demo in Google Colab. Please access the notebook from the following button and enjoy the real-time synthesis! - Real-time TTS demo 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) English, Japanese, and Mandarin models are available in the demo.
ESPnet1
> NOTE: We are moving on ESPnet2-based development for TTS. Please check the latest demo in the above ESPnet2 demo. You can try the real-time demo in Google Colab. Please access the notebook from the following button and enjoy the real-time synthesis. - Real-time TTS demo 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) We also provide shell script to perform synthesize. Go to a recipe directory and run `utils/synth_wav.sh` as follows: ```sh # go to recipe directory and source path of espnet tools cd egs/ljspeech/tts1 && . ./path.sh # we use upper-case char sequence for the default model. echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example.txt # let's synthesize speech! synth_wav.sh example.txt # also you can use multiple sentences echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example_multi.txt echo "TEXT TO SPEECH IS A TECHQNIQUE TO CONVERT TEXT INTO SPEECH." >> example_multi.txt synth_wav.sh example_multi.txt ``` You can change the pretrained model as follows: ```sh 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: ```sh synth_wav.sh --vocoder_models ljspeech.wavenet.mol.v1 example.txt ``` WaveNet vocoder provides very high quality speech but it takes time to generate. See more details or available models via `--help`. ```sh synth_wav.sh --help ```
### VC results
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- Transformer and Tacotron2 based VC You can listen to some samples on the [demo webpage](https://unilight.github.io/Publication-Demos/publications/transformer-vc/). - Cascade ASR+TTS as one of the baseline systems of VCC2020 The [Voice Conversion Challenge 2020](http://www.vc-challenge.org/) (VCC2020) adopts ESPnet to build an end-to-end based baseline system. In VCC2020, the objective is intra/cross lingual nonparallel VC. You can download converted samples of the cascade ASR+TTS baseline system [here](https://drive.google.com/drive/folders/1oeZo83GrOgtqxGwF7KagzIrfjr8X59Ue?usp=sharing).
### CTC Segmentation demo
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[CTC segmentation](https://arxiv.org/abs/2007.09127) determines utterance segments within audio files. Aligned utterance segments constitute the labels of speech datasets. As demo, we align start and end of utterances within the audio file `ctc_align_test.wav`, using the example script `utils/ctc_align_wav.sh`. For preparation, set up a data directory: ```sh cd egs/tedlium2/align1/ # data directory align_dir=data/demo mkdir -p ${align_dir} # wav file base=ctc_align_test wav=../../../test_utils/${base}.wav # recipe files echo "batchsize: 0" > ${align_dir}/align.yaml cat << EOF > ${align_dir}/utt_text ${base} THE SALE OF THE HOTELS ${base} IS PART OF HOLIDAY'S STRATEGY ${base} TO SELL OFF ASSETS ${base} AND CONCENTRATE ${base} ON PROPERTY MANAGEMENT EOF ``` Here, `utt_text` is the file containing the list of utterances. Choose a pre-trained ASR model that includes a CTC layer to find utterance segments: ```sh # pre-trained ASR model model=wsj.transformer_small.v1 mkdir ./conf && cp ../../wsj/asr1/conf/no_preprocess.yaml ./conf ../../../utils/asr_align_wav.sh \ --models ${model} \ --align_dir ${align_dir} \ --align_config ${align_dir}/align.yaml \ ${wav} ${align_dir}/utt_text ``` Segments are written to `aligned_segments` as a list of file/utterance name, utterance start and end times in seconds and a confidence score. The confidence score is a probability in log space that indicates how good the utterance was aligned. If needed, remove bad utterances: ```sh min_confidence_score=-5 awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' ${align_dir}/aligned_segments ``` The demo script `utils/ctc_align_wav.sh` uses an already pretrained ASR model (see list above for more models). It is recommended to use models with RNN-based encoders (such as BLSTMP) for aligning large audio files; rather than using Transformer models that have a high memory consumption on longer audio data. The sample rate of the audio must be consistent with that of the data used in training; adjust with `sox` if needed. A full example recipe is in `egs/tedlium2/align1/`.
## 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={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } @inproceedings{inaguma-etal-2020-espnet, title = "{ESP}net-{ST}: All-in-One Speech Translation Toolkit", author = "Inaguma, Hirofumi and Kiyono, Shun and Duh, Kevin and Karita, Shigeki and Yalta, Nelson and Hayashi, Tomoki and Watanabe, Shinji", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-demos.34", pages = "302--311", } @inproceedings{li2020espnet, title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, author={Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph Boeddeker and Zhuo Chen and Shinji Watanabe}, booktitle={Proceedings of IEEE Spoken Language Technology Workshop (SLT)}, pages={785--792}, year={2021}, organization={IEEE}, } ```