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
ubuntu20/python3.8/pip
ubuntu18/python3.7/pip
debian9/python3.6/conda
centos7/python3.6/conda
[docs/coverage] python3.8

Docs | Example | Example (ESPnet2) | Docker | Notebook | Tutorial (2019)

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 and pytorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments.

• 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!)

• 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 for complete documentation.

• CTC segmentation
• Non-autoregressive model 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.

Demonstration

• Real-time ASR demo with ESPnet2

• 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)

Demonstration

• Real-time TTS demo with ESPnet2
• Real-time TTS demo with ESPnet1

To train the neural vocoder, please check the following repositories:

• kan-bayashi/ParallelWaveGAN
• r9y9/wavenet_vocoder

NOTE:

• We are moving on ESPnet2-based development for TTS.
• If you are beginner, we recommend using ESPnet2-TTS.

• 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!)

• 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!)

• Flexible network architecture thanks to chainer and pytorch
• Flexible front-end processing thanks to kaldiio and HDF5 support
• Tensorboard based monitoring

See ESPnet2.

• 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 or MPI
• Supporting Sharded Training provided by 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
• Integrated with wandb

• If you intend to do full experiments including DNN training, then see Installation.

• If you just need the Python module only:

  pip install espnet
  # To install latest
  # pip install git https://github.com/espnet/espnet

  You need to install some packages.

  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.

See Usage.

go to docker/ and follow instructions.

Thank you for taking times for ESPnet! Any contributions to ESPNet are welcome and feel free to ask any questions or requests to issues. If it's the first contribution to ESPnet for you, please follow the contribution guide.

You can find useful tutorials and demos in Interspeech 2019 Tutorial

# 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

# 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

# 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

synth_wav.sh --models ljspeech.fastspeech.v1 example.txt

synth_wav.sh --vocoder_models ljspeech.wavenet.mol.v1 example.txt

synth_wav.sh --help

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

# 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

min_confidence_score=-5
awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' ${align_dir}/aligned_segments

[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

@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},
}