# ESPnet: end-to-end speech processing toolkit

|system/pytorch ver.|1.12.1|1.13.1|2.0.1|2.1.0|
| :---- | :---: | :---: | :---: | :---: |
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[**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)

______________________________________________________________________

ESPnet is an end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, and so on.
ESPnet uses [pytorch](http://pytorch.org/) as a 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 various speech processing experiments.

## Tutorial Series
- 2019 Tutorial at Interspeech
  - [Material](https://github.com/espnet/interspeech2019-tutorial)
- 2021 Tutorial at CMU
  - [Online video](https://youtu.be/2mRz3wH1vd0)
  - [Material](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tutorial_2021_CMU_11751_18781.ipynb)
- 2022 Tutorial at CMU
  - Usage of ESPnet (ASR as an example)
    - [Online video](https://youtu.be/YDN8cVjxSik)
    - [Material](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_recipe_tutorial_CMU_11751_18781_Fall2022.ipynb)
  - Add new models/tasks to ESPnet
    - [Online video](https://youtu.be/Css3XAes7SU)
    - [Material](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_new_task_tutorial_CMU_11751_18781_Fall2022.ipynb)

## Key Features

### Kaldi-style complete recipe
- Support numbers of `ASR` recipes (WSJ, Switchboard, CHiME-4/5, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, REVERB, Gigaspeech, etc.)
- Support numbers of `TTS` recipes in 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'14, IWSLT'16, the above ST recipes etc.)
- Support numbers of `SLU` recipes (CATSLU-MAPS, FSC, Grabo, IEMOCAP, JDCINAL, SNIPS, SLURP, SWBD-DA, etc.)
- Support numbers of `SE/SS` recipes (DNS-IS2020, LibriMix, SMS-WSJ, VCTK-noisyreverb, WHAM!, WHAMR!, WSJ-2mix, etc.)
- Support voice conversion recipe (VCC2020 baseline)
- Support speaker diarization recipe (mini_librispeech, librimix)
- Support singing voice synthesis recipe (ofuton_p_utagoe_db, opencpop, m4singer, etc.)

### 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), Transformer, Conformer, [Branchformer](https://proceedings.mlr.press/v162/peng22a.html), or [E-Branchformer](https://arxiv.org/abs/2210.00077)
  - Decoder: RNN (LSTM/GRU), Transformer, or S4
- Attention: Dot product, location-aware attention, variants of multi-head
- Incorporate RNNLM/LSTMLM/TransformerLM/N-gram trained only with text data
- Batch GPU decoding
- Data augmentation
- **Transducer** based end-to-end ASR
  - Architecture:
    - Custom encoder supporting RNNs, Conformer, Branchformer (w/ variants), 1D Conv / TDNN.
    - Decoder w/ parameters shared across blocks supporting RNN, stateless w/ 1D Conv, [MEGA](https://arxiv.org/abs/2209.10655), and [RWKV](https://arxiv.org/abs/2305.13048).
    - Pre-encoder: VGG2L or Conv2D available.
  - Search algorithms:
    - Greedy search constrained to one emission by timestep.
    - Default beam search algorithm [[Graves, 2012]](https://arxiv.org/abs/1211.3711) without prefix search.
    - Alignment-Length Synchronous decoding [[Saon et al., 2020]](https://ieeexplore.ieee.org/abstract/document/9053040).
    - Time Synchronous Decoding [[Saon et al., 2020]](https://ieeexplore.ieee.org/abstract/document/9053040).
    - N-step Constrained beam search modified from [[Kim et al., 2020]](https://arxiv.org/abs/2002.03577).
    - modified Adaptive Expansion Search based on [[Kim et al., 2021]](https://ieeexplore.ieee.org/abstract/document/9250505) and NSC.
  - Features:
    - Unified interface for offline and streaming speech recognition.
    - Multi-task learning with various auxiliary losses:
      - Encoder: CTC, auxiliary Transducer and symmetric KL divergence.
      - Decoder: cross-entropy w/ label smoothing.
    - Transfer learning with an acoustic model and/or language model.
    - Training with FastEmit regularization method [[Yu et al., 2021]](https://arxiv.org/abs/2010.11148).
  > Please refer to the [tutorial page](https://espnet.github.io/espnet/tutorial.html#transducer) 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 pre-trained model as Encoder, imported from [FairSeq](https://github.com/pytorch/fairseq/tree/master/fairseq).
- Self-supervised learning representations as features, using upstream models in [S3PRL](https://github.com/s3prl/s3prl) in frontend.
  - Set `frontend` to `s3prl`
  - Select any upstream model by setting the `frontend_conf` to the corresponding name.
- Transfer Learning :
  - easy usage and transfers from models previously trained by your group or models from [ESPnet Hugging Face repository](https://huggingface.co/espnet).
  - [Documentation](https://github.com/espnet/espnet/tree/master/egs2/mini_an4/asr1/transfer_learning.md) and [toy example runnable on colab](https://github.com/espnet/notebook/blob/master/espnet2_asr_transfer_learning_demo.ipynb).
- Streaming Transformer/Conformer ASR with blockwise synchronous beam search.
- Restricted Self-Attention based on [Longformer](https://arxiv.org/abs/2004.05150) as an encoder for long sequences
- OpenAI [Whisper](https://openai.com/blog/whisper/) model, robust ASR based on large-scale, weakly-supervised multitask learning

Demonstration
- Real-time ASR 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_asr_realtime_demo.ipynb)
- [Gradio](https://github.com/gradio-app/gradio) Web Demo on [Hugging Face Spaces](https://huggingface.co/docs/hub/spaces). Check out the [Web Demo](https://huggingface.co/spaces/akhaliq/espnet2_asr)
- Streaming Transformer ASR [Local Demo](https://github.com/espnet/notebook/blob/master/espnet2_streaming_asr_demo.ipynb) with ESPnet2.

### TTS: Text-to-speech
- Architecture
    - Tacotron2
    - Transformer-TTS
    - FastSpeech
    - FastSpeech2
    - Conformer FastSpeech & FastSpeech2
    - VITS
    - JETS
- Multi-speaker & multi-language extention
    - Pre-trained speaker embedding (e.g., X-vector)
    - Speaker ID embedding
    - Language ID embedding
    - Global style token (GST) embedding
    - Mix of the above embeddings
- End-to-end training
    - End-to-end text-to-wav model (e.g., VITS, JETS, etc.)
    - Joint training of text2mel and vocoder
- Various language support
    - En / Jp / Zn / De / Ru / And more...
- Integration with neural vocoders
    - Parallel WaveGAN
    - MelGAN
    - Multi-band MelGAN
    - HiFiGAN
    - StyleMelGAN
    - Mix of the above models

Demonstration
- 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)
- Integrated to [Hugging Face Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/ESPnet2-TTS)

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)

### SE: Speech enhancement (and separation)

- Single-speaker speech enhancement
- Multi-speaker speech separation
- Unified encoder-separator-decoder structure for time-domain and frequency-domain models
  - Encoder/Decoder: STFT/iSTFT, Convolution/Transposed-Convolution
  - Separators: BLSTM, Transformer, Conformer, [TasNet](https://arxiv.org/abs/1809.07454), [DPRNN](https://arxiv.org/abs/1910.06379), [SkiM](https://arxiv.org/abs/2201.10800), [SVoice](https://arxiv.org/abs/2011.02329), [DC-CRN](https://web.cse.ohio-state.edu/~wang.77/papers/TZW.taslp21.pdf), [DCCRN](https://arxiv.org/abs/2008.00264), [Deep Clustering](https://ieeexplore.ieee.org/document/7471631), [Deep Attractor Network](https://pubmed.ncbi.nlm.nih.gov/29430212/), [FaSNet](https://arxiv.org/abs/1909.13387), [iFaSNet](https://arxiv.org/abs/1910.14104), Neural Beamformers, etc.
- Flexible ASR integration: working as an individual task or as the ASR frontend
- Easy to import pre-trained models from [Asteroid](https://github.com/asteroid-team/asteroid)
  - Both the pre-trained models from Asteroid and the specific configuration are supported.

Demonstration
- Interactive SE demo with ESPnet2 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1fjRJCh96SoYLZPRxsjF9VDv4Q2VoIckI?usp=sharing)
- Streaming SE demo with ESPnet2 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17vd1V78eJpp3PHBnbFE5aVY5uMxQFL6o?usp=sharing)

### 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 Mel spectrogram
- End-to-end VC based on cascaded ASR+TTS (Baseline system for Voice Conversion Challenge 2020!)

### SLU: Spoken Language Understanding
- Architecture
    - Transformer-based Encoder
    - Conformer-based Encoder
    - [Branchformer](https://proceedings.mlr.press/v162/peng22a.html) based Encoder
    - [E-Branchformer](https://arxiv.org/abs/2210.00077) based Encoder
    - RNN based Decoder
    - Transformer-based Decoder
- Support Multitasking with ASR
    - Predict both intent and ASR transcript
- Support Multitasking with NLU
    - Deliberation encoder based 2 pass model
- Support using pre-trained ASR models
    - Hubert
    - Wav2vec2
    - VQ-APC
    - TERA and more ...
- Support using pre-trained NLP models
    - BERT
    - MPNet And more...
- Various language support
    - En / Jp / Zn / Nl / And more...
- Supports using context from previous utterances
- Supports using other tasks like SE in a pipeline manner
- Supports Two Pass SLU that combines audio and ASR transcript
Demonstration
- Performing noisy spoken language understanding using a speech enhancement model followed by a spoken language understanding model.  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14nCrJ05vJcQX0cJuXjbMVFWUHJ3Wfb6N?usp=sharing)
- Performing two-pass spoken language understanding where the second pass model attends to both acoustic and semantic information.  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1p2cbGIPpIIcynuDl4ZVHDpmNPl8Nh_ci?usp=sharing)
- Integrated to [Hugging Face Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See SLU demo on multiple languages: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Siddhant/ESPnet2-SLU)

### SUM: Speech Summarization
- End to End Speech Summarization Recipe for Instructional Videos using Restricted Self-Attention [[Sharma et al., 2022]](https://arxiv.org/abs/2110.06263)

### SVS: Singing Voice Synthesis
- Framework merge from [Muskits](https://github.com/SJTMusicTeam/Muskits)
- Architecture
  - RNN-based non-autoregressive model
  - Xiaoice
  - Tacotron-singing
  - DiffSinger (in progress)
  - VISinger
  - VISinger 2 (its variations with different vocoders-architecture)
- Support multi-speaker & multilingual singing synthesis
  - Speaker ID embedding
  - Language ID embedding
- Various language support
  - Jp / En / Kr / Zh
- Tight integration with neural vocoders (the same as TTS)

### SSL: Self-supervised Learning
- Support HuBERT Pre-training:
  * Example recipe: [egs2/LibriSpeech/ssl1](egs2/LibriSpeech/ssl1)

### UASR: Unsupervised ASR (EURO: ESPnet Unsupervised Recognition - Open-source)
- Architecture
  - wav2vec-U (with different self-supervised models)
  - wav2vec-U 2.0 (in progress)
- Support PrefixBeamSearch and K2-based WFST decoding

### S2T: Speech-to-text with Whisper-style multilingual multitask models
- Reproduces Whisper-style training from scratch using public data: [OWSM](https://arxiv.org/abs/2309.13876)
- Supports multiple tasks in a single model
  - Multilingual speech recognition
  - Any-to-any speech translation
  - Language identification
  - Utterance-level timestamp prediction (segmentation)

### 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).

- Independent 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 that can be applied to 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
    # We recommend you install PyTorch before installing espnet following https://pytorch.org/get-started/locally/
    pip install espnet
    # To install the latest
    # pip install git+https://github.com/espnet/espnet
    # To install additional packages
    # pip install "espnet[all]"
    ```

    If you use ESPnet1, please install chainer and cupy.

    ```sh
    pip install chainer==6.0.0 cupy==6.0.0    # [Option]
    ```

    You might need to install some packages depending on each task. We prepared various installation scripts at [tools/installers](tools/installers).

- (ESPnet2) Once installed, run `wandb login` and set `--use_wandb true` to enable tracking runs using W&B.

## Docker Container

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

## Contribution
Thank you for taking the time 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 your first ESPnet contribution,  please follow the [contribution guide](CONTRIBUTING.md).

### ASR results

expand

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

| Task                                                              |     CER (%)     |     WER (%)     |                                                                              Pre-trained 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                                      |     4.1/4.4     |       N/A       |                [link](https://github.com/espnet/espnet/tree/master/egs2/aishell/asr1#branchformer-initial)                                                                  |
| 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)                                         |
| **ESPnet2** GigaSpeech dev/test                                   |       N/A       |    10.6/10.5    |                                          [link](https://github.com/espnet/espnet/tree/master/egs2/gigaspeech/asr1#e-branchformer)                                           |
| 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 | 0.6/1.5/0.6/1.4 | 1.7/3.4/1.8/3.6 |    [link](https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1#self-supervised-learning-features-hubert_large_ll60k-conformer-utt_mvn-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)           |
| **ESPnet2** Switchboard (eval2000) callhm/swbd                    |       N/A       |    13.4/7.3     |                                             [link](https://github.com/espnet/espnet/tree/master/egs2/swbd/asr1#e-branchformer)                                              |
| 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)                 |
| **ESPnet2** TEDLIUM2 dev/test                                     |       N/A       |     7.3/7.1     |                 [link](https://github.com/espnet/espnet/blob/master/egs2/tedlium2/asr1/README.md#e-branchformer-12-encoder-layers)                                          |
| 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                                      |     1.1/0.8     |     2.8/1.8     |       [link](https://github.com/espnet/espnet/tree/master/egs2/wsj/asr1#self-supervised-learning-features-wav2vec2_large_ll60k-conformer-utt_mvn-with-transformer-lm)       |

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

expand

You can recognize speech in a WAV file using pre-trained models.
Go to a recipe directory and run `utils/recog_wav.sh` as follows:
```sh
# go to the 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 pre-trained models in the demo script are listed 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                |

### SE results
expand

We list results from three different models on WSJ0-2mix, which is one the most widely used benchmark dataset for speech separation.

| Model                                             | STOI | SAR   | SDR   | SIR   |
| ------------------------------------------------- | ---- | ----- | ----- | ----- |
| [TF Masking](https://zenodo.org/record/4498554)   | 0.89 | 11.40 | 10.24 | 18.04 |
| [Conv-Tasnet](https://zenodo.org/record/4498562)  | 0.95 | 16.62 | 15.94 | 25.90 |
| [DPRNN-Tasnet](https://zenodo.org/record/4688000) | 0.96 | 18.82 | 18.29 | 28.92 |

### SE demos
expand

You can try the interactive demo with Google Colab. Please click the following button to get access to the demos.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1fjRJCh96SoYLZPRxsjF9VDv4Q2VoIckI?usp=sharing)

It is based on ESPnet2. Pre-trained models are available for both speech enhancement and speech separation tasks.

Speech separation streaming demos:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17vd1V78eJpp3PHBnbFE5aVY5uMxQFL6o?usp=sharing)

### ST results

expand

We list 4-gram BLEU of major ST tasks.

#### end-to-end system
| Task                                              | BLEU  |                                                                                Pre-trained 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  | Pre-trained 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

expand

(**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 pre-trained 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 pre-trained 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

expand

| Task                                              | BLEU  |                                                                        Pre-trained 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'14 test2014 (De->En)                        | 32.2  | [link](https://github.com/espnet/espnet/blob/master/egs2/iwslt14/mt1/README.md)  |
| 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 pre-trained models via `espnet_model_zoo`.
- [ESPnet model zoo](https://github.com/espnet/espnet_model_zoo)
- [Pre-trained model list](https://github.com/espnet/espnet_model_zoo/blob/master/espnet_model_zoo/table.csv)

You can download pre-trained vocoders via `kan-bayashi/ParallelWaveGAN`.
- [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)
- [Pre-trained 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 pre-trained models and generated samples:
- [All of the pre-trained 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 the 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 pre-trained 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 pre-trained 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 a shell script to perform synthesis.
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 an 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 TECHNIQUE TO CONVERT TEXT INTO SPEECH." >> example_multi.txt
synth_wav.sh example_multi.txt
```

You can change the pre-trained model as follows:

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

Waveform synthesis is performed with the Griffin-Lim algorithm and neural vocoders (WaveNet and ParallelWaveGAN).
You can change the pre-trained 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

expand

- 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).

### SLU results

expand

We list the performance on various SLU tasks and datasets using the metric reported in the original dataset paper

| Task                                                              | Dataset                                                              |    Metric     |     Result     |                                                                              Pre-trained Model                                         |
| ----------------------------------------------------------------- | :-------------: | :-------------: | :-------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Intent Classification                                                 |     SLURP     |       Acc       |       86.3       |                [link](https://github.com/espnet/espnet/tree/master/egs2/slurp/asr1/README.md)                |
| Intent Classification                                                   |     FSC     |       Acc       |       99.6       |                [link](https://github.com/espnet/espnet/tree/master/egs2/fsc/asr1/README.md)                |
| Intent Classification                                                  |     FSC Unseen Speaker Set     |       Acc       |       98.6       |                [link](https://github.com/espnet/espnet/tree/master/egs2/fsc_unseen/asr1/README.md)                |
| Intent Classification                                                   |     FSC Unseen Utterance Set     |       Acc       |       86.4       |                [link](https://github.com/espnet/espnet/tree/master/egs2/fsc_unseen/asr1/README.md)                |
| Intent Classification                                                   |     FSC Challenge Speaker Set     |       Acc       |       97.5       |                [link](https://github.com/espnet/espnet/tree/master/egs2/fsc_challenge/asr1/README.md)                |
| Intent Classification                                                   |     FSC Challenge Utterance Set     |       Acc       |       78.5       |                [link](https://github.com/espnet/espnet/tree/master/egs2/fsc_challenge/asr1/README.md)                |
| Intent Classification                                                   |     SNIPS     |       F1       |       91.7       |                [link](https://github.com/espnet/espnet/tree/master/egs2/snips/asr1/README.md)                |
| Intent Classification                                                   |     Grabo (Nl)   |       Acc       |       97.2       |                [link](https://github.com/espnet/espnet/tree/master/egs2/grabo/asr1/README.md)                |
| Intent Classification                                                   |     CAT SLU MAP (Zn)     |       Acc       |       78.9       |                [link](https://github.com/espnet/espnet/tree/master/egs2/catslu/asr1/README.md)                |
| Intent Classification                                                  |     Google Speech Commands    |       Acc       |       98.4       |                [link](https://github.com/espnet/espnet/tree/master/egs2/speechcommands/asr1/README.md)                |
| Slot Filling                                                  |     SLURP     |       SLU-F1       |       71.9       |                [link](https://github.com/espnet/espnet/tree/master/egs2/slurp_entity/asr1/README.md)                |
| Dialogue  Act Classification                                                 |     Switchboard     |       Acc       |       67.5       |                [link](https://github.com/espnet/espnet/tree/master/egs2/swbd_da/asr1/README.md)                |
| Dialogue  Act Classification                                                 |     Jdcinal (Jp)    |       Acc       |       67.4       |                [link](https://github.com/espnet/espnet/tree/master/egs2/jdcinal/asr1/README.md)                |
| Emotion Recognition                                                  |     IEMOCAP     |       Acc       |       69.4       |                [link](https://github.com/espnet/espnet/tree/master/egs2/iemocap/asr1/README.md)                |
| Emotion Recognition                                                  |     swbd_sentiment     |       Macro F1       |       61.4       |                [link](https://github.com/espnet/espnet/tree/master/egs2/swbd_sentiment/asr1/README.md)                |
| Emotion Recognition                                                  |     slue_voxceleb     |       Macro F1       |       44.0       |                [link](https://github.com/espnet/espnet/tree/master/egs2/slue-voxceleb/asr1/README.md)                |

If you want to check the results of the other recipes, please check `egs2/<name_of_recipe>/asr1/RESULTS.md`.

### CTC Segmentation demo

ESPnet1

[CTC segmentation](https://arxiv.org/abs/2007.09127) determines utterance segments within audio files.
Aligned utterance segments constitute the labels of speech datasets.

As a demo, we align the start and end of utterances within the audio file `ctc_align_test.wav`, using the example script `utils/asr_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 names, utterance start and end times in seconds, and a confidence score.
The confidence score is a probability in log space that indicates how well 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 pre-trained ASR model (see the 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 with 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/`.

ESPnet2

[CTC segmentation](https://arxiv.org/abs/2007.09127) determines utterance segments within audio files.
Aligned utterance segments constitute the labels of speech datasets.

As a demo, we align the start and end of utterances within the audio file `ctc_align_test.wav`.
This can be done either directly from the Python command line or using the script `espnet2/bin/asr_align.py`.

From the Python command line interface:

```python
# load a model with character tokens
from espnet_model_zoo.downloader import ModelDownloader
d = ModelDownloader(cachedir="./modelcache")
wsjmodel = d.download_and_unpack("kamo-naoyuki/wsj")
# load the example file included in the ESPnet repository
import soundfile
speech, rate = soundfile.read("./test_utils/ctc_align_test.wav")
# CTC segmentation
from espnet2.bin.asr_align import CTCSegmentation
aligner = CTCSegmentation( **wsjmodel , fs=rate )
text = """
utt1 THE SALE OF THE HOTELS
utt2 IS PART OF HOLIDAY'S STRATEGY
utt3 TO SELL OFF ASSETS
utt4 AND CONCENTRATE ON PROPERTY MANAGEMENT
"""
segments = aligner(speech, text)
print(segments)
# utt1 utt 0.26 1.73 -0.0154 THE SALE OF THE HOTELS
# utt2 utt 1.73 3.19 -0.7674 IS PART OF HOLIDAY'S STRATEGY
# utt3 utt 3.19 4.20 -0.7433 TO SELL OFF ASSETS
# utt4 utt 4.20 6.10 -0.4899 AND CONCENTRATE ON PROPERTY MANAGEMENT
```

Aligning also works with fragments of the text.
For this, set the `gratis_blank` option that allows skipping unrelated audio sections without penalty.
It's also possible to omit the utterance names at the beginning of each line by setting `kaldi_style_text` to False.

```python
aligner.set_config( gratis_blank=True, kaldi_style_text=False )
text = ["SALE OF THE HOTELS", "PROPERTY MANAGEMENT"]
segments = aligner(speech, text)
print(segments)
# utt_0000 utt 0.37 1.72 -2.0651 SALE OF THE HOTELS
# utt_0001 utt 4.70 6.10 -5.0566 PROPERTY MANAGEMENT
```

The script `espnet2/bin/asr_align.py` uses a similar interface. To align utterances:

```sh
# ASR model and config files from pre-trained model (e.g., from cachedir):
asr_config=<path-to-model>/config.yaml
asr_model=<path-to-model>/valid.*best.pth
# prepare the text file
wav="test_utils/ctc_align_test.wav"
text="test_utils/ctc_align_text.txt"
cat << EOF > ${text}
utt1 THE SALE OF THE HOTELS
utt2 IS PART OF HOLIDAY'S STRATEGY
utt3 TO SELL OFF ASSETS
utt4 AND CONCENTRATE
utt5 ON PROPERTY MANAGEMENT
EOF
# obtain alignments:
python espnet2/bin/asr_align.py --asr_train_config ${asr_config} --asr_model_file ${asr_model} --audio ${wav} --text ${text}
# utt1 ctc_align_test 0.26 1.73 -0.0154 THE SALE OF THE HOTELS
# utt2 ctc_align_test 1.73 3.19 -0.7674 IS PART OF HOLIDAY'S STRATEGY
# utt3 ctc_align_test 3.19 4.20 -0.7433 TO SELL OFF ASSETS
# utt4 ctc_align_test 4.20 4.97 -0.6017 AND CONCENTRATE
# utt5 ctc_align_test 4.97 6.10 -0.3477 ON PROPERTY MANAGEMENT
```

The output of the script can be redirected to a `segments` file by adding the argument `--output segments`.
Each line contains the file/utterance name, utterance start and end times in seconds, and a confidence score; optionally also the utterance text.
The confidence score is a probability in log space that indicates how well the utterance was aligned. If needed, remove bad utterances:

```sh
min_confidence_score=-7
# here, we assume that the output was written to the file `segments`
awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' segments
```

See the module documentation for more information.
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.

Also, we can use this tool to provide token-level segmentation information if we prepare a list of tokens instead of that of utterances in the `text` file. See the discussion in https://github.com/espnet/espnet/issues/4278#issuecomment-1100756463.

## 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",
}
@article{hayashi2021espnet2,
  title={Espnet2-tts: Extending the edge of tts research},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Yoshimura, Takenori and Wu, Peter and Shi, Jiatong and Saeki, Takaaki and Ju, Yooncheol and Yasuda, Yusuke and Takamichi, Shinnosuke and Watanabe, Shinji},
  journal={arXiv preprint arXiv:2110.07840},
  year={2021}
}
@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},
}
@inproceedings{arora2021espnet,
  title={{ESPnet-SLU}: Advancing Spoken Language Understanding through ESPnet},
  author={Arora, Siddhant and Dalmia, Siddharth and Denisov, Pavel and Chang, Xuankai and Ueda, Yushi and Peng, Yifan and Zhang, Yuekai and Kumar, Sujay and Ganesan, Karthik and Yan, Brian and others},
  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7167--7171},
  year={2022},
  organization={IEEE}
}
@inproceedings{shi2022muskits,
  author={Shi, Jiatong and Guo, Shuai and Qian, Tao and Huo, Nan and Hayashi, Tomoki and Wu, Yuning and Xu, Frank and Chang, Xuankai and Li, Huazhe and Wu, Peter and Watanabe, Shinji and Jin, Qin},
  title={{Muskits}: an End-to-End Music Processing Toolkit for Singing Voice Synthesis},
  year={2022},
  booktitle={Proceedings of Interspeech},
  pages={4277-4281},
  url={https://www.isca-speech.org/archive/pdfs/interspeech_2022/shi22d_interspeech.pdf}
}
@inproceedings{lu22c_interspeech,
  author={Yen-Ju Lu and Xuankai Chang and Chenda Li and Wangyou Zhang and Samuele Cornell and Zhaoheng Ni and Yoshiki Masuyama and Brian Yan and Robin Scheibler and Zhong-Qiu Wang and Yu Tsao and Yanmin Qian and Shinji Watanabe},
  title={{ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={5458--5462},
}
@article{gao2022euro,
  title={{EURO}: {ESPnet} Unsupervised ASR Open-source Toolkit},
  author={Gao, Dongji and Shi, Jiatong and Chuang, Shun-Po and Garcia, Leibny Paola and Lee, Hung-yi and Watanabe, Shinji and Khudanpur, Sanjeev},
  journal={arXiv preprint arXiv:2211.17196},
  year={2022}
}
@article{peng2023reproducing,
  title={Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data},
  author={Peng, Yifan and Tian, Jinchuan and Yan, Brian and Berrebbi, Dan and Chang, Xuankai and Li, Xinjian and Shi, Jiatong and Arora, Siddhant and Chen, William and Sharma, Roshan and others},
  journal={arXiv preprint arXiv:2309.13876},
  year={2023}
}
```


SQLAlchemy
==========

|PyPI| |Python| |Downloads|

.. |PyPI| image:: https://img.shields.io/pypi/v/sqlalchemy
    :target: https://pypi.org/project/sqlalchemy
    :alt: PyPI

.. |Python| image:: https://img.shields.io/pypi/pyversions/sqlalchemy
    :target: https://pypi.org/project/sqlalchemy
    :alt: PyPI - Python Version

.. |Downloads| image:: https://static.pepy.tech/badge/sqlalchemy/month
    :target: https://pepy.tech/project/sqlalchemy
    :alt: PyPI - Downloads

The Python SQL Toolkit and Object Relational Mapper

Introduction
-------------

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper
that gives application developers the full power and
flexibility of SQL. SQLAlchemy provides a full suite
of well known enterprise-level persistence patterns,
designed for efficient and high-performing database
access, adapted into a simple and Pythonic domain
language.

Major SQLAlchemy features include:

* An industrial strength ORM, built
  from the core on the identity map, unit of work,
  and data mapper patterns.   These patterns
  allow transparent persistence of objects
  using a declarative configuration system.
  Domain models
  can be constructed and manipulated naturally,
  and changes are synchronized with the
  current transaction automatically.
* A relationally-oriented query system, exposing
  the full range of SQL's capabilities
  explicitly, including joins, subqueries,
  correlation, and most everything else,
  in terms of the object model.
  Writing queries with the ORM uses the same
  techniques of relational composition you use
  when writing SQL.  While you can drop into
  literal SQL at any time, it's virtually never
  needed.
* A comprehensive and flexible system
  of eager loading for related collections and objects.
  Collections are cached within a session,
  and can be loaded on individual access, all
  at once using joins, or by query per collection
  across the full result set.
* A Core SQL construction system and DBAPI
  interaction layer.  The SQLAlchemy Core is
  separate from the ORM and is a full database
  abstraction layer in its own right, and includes
  an extensible Python-based SQL expression
  language, schema metadata, connection pooling,
  type coercion, and custom types.
* All primary and foreign key constraints are
  assumed to be composite and natural.  Surrogate
  integer primary keys are of course still the
  norm, but SQLAlchemy never assumes or hardcodes
  to this model.
* Database introspection and generation.  Database
  schemas can be "reflected" in one step into
  Python structures representing database metadata;
  those same structures can then generate
  CREATE statements right back out - all within
  the Core, independent of the ORM.

SQLAlchemy's philosophy:

* SQL databases behave less and less like object
  collections the more size and performance start to
  matter; object collections behave less and less like
  tables and rows the more abstraction starts to matter.
  SQLAlchemy aims to accommodate both of these
  principles.
* An ORM doesn't need to hide the "R".   A relational
  database provides rich, set-based functionality
  that should be fully exposed.   SQLAlchemy's
  ORM provides an open-ended set of patterns
  that allow a developer to construct a custom
  mediation layer between a domain model and
  a relational schema, turning the so-called
  "object relational impedance" issue into
  a distant memory.
* The developer, in all cases, makes all decisions
  regarding the design, structure, and naming conventions
  of both the object model as well as the relational
  schema.   SQLAlchemy only provides the means
  to automate the execution of these decisions.
* With SQLAlchemy, there's no such thing as
  "the ORM generated a bad query" - you
  retain full control over the structure of
  queries, including how joins are organized,
  how subqueries and correlation is used, what
  columns are requested.  Everything SQLAlchemy
  does is ultimately the result of a developer-initiated
  decision.
* Don't use an ORM if the problem doesn't need one.
  SQLAlchemy consists of a Core and separate ORM
  component.   The Core offers a full SQL expression
  language that allows Pythonic construction
  of SQL constructs that render directly to SQL
  strings for a target database, returning
  result sets that are essentially enhanced DBAPI
  cursors.
* Transactions should be the norm.  With SQLAlchemy's
  ORM, nothing goes to permanent storage until
  commit() is called.  SQLAlchemy encourages applications
  to create a consistent means of delineating
  the start and end of a series of operations.
* Never render a literal value in a SQL statement.
  Bound parameters are used to the greatest degree
  possible, allowing query optimizers to cache
  query plans effectively and making SQL injection
  attacks a non-issue.

Documentation
-------------

Latest documentation is at:

https://www.sqlalchemy.org/docs/

Installation / Requirements
---------------------------

Full documentation for installation is at
`Installation <https://www.sqlalchemy.org/docs/intro.html#installation>`_.

Getting Help / Development / Bug reporting
------------------------------------------

Please refer to the `SQLAlchemy Community Guide <https://www.sqlalchemy.org/support.html>`_.

Code of Conduct
---------------

Above all, SQLAlchemy places great emphasis on polite, thoughtful, and
constructive communication between users and developers.
Please see our current Code of Conduct at
`Code of Conduct <https://www.sqlalchemy.org/codeofconduct.html>`_.

License
-------

SQLAlchemy is distributed under the `MIT license
<https://www.opensource.org/licenses/mit-license.php>`_.


[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![CodeStyle](https://img.shields.io/badge/Check%20Style-Google-brightgreen)](https://checkstyle.sourceforge.io/google_style.html) [![Style](https://img.shields.io/badge/Check%20Style-Black-black)](https://checkstyle.sourceforge.io/google_style.html) [![Build Status](https://travis-ci.org/FederatedAI/FATE.svg?branch=master)](https://travis-ci.org/FederatedAI/FATE)
[![codecov](https://codecov.io/gh/FederatedAI/FATE/branch/master/graph/badge.svg)](https://codecov.io/gh/FederatedAI/FATE)
[![Documentation Status](https://readthedocs.org/projects/fate/badge/?version=latest)](https://fate.readthedocs.io/en/latest/?badge=latest)
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[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/6308/badge)](https://bestpractices.coreinfrastructure.org/projects/6308)

FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy.
It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC).
Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.

FATE is an open source project hosted by Linux Foundation. The [Technical Charter](https://github.com/FederatedAI/FATE-Community/blob/master/FATE_Project_Technical_Charter.pdf) sets forth the responsibilities and procedures for technical contribution to, and oversight of, the FATE (“Federated AI Technology Enabler”) Project.

<https://fate.readthedocs.io/en/latest>

## Getting Started
FATE can be deployed on a single node or on multiple nodes. Choose the deployment approach which matches your environment.
[Release version can be downloaded here.](https://github.com/FederatedAI/FATE/wiki/Download)

### Version >= 2.0
### Standalone deployment

- Deploying FATE on a single node via PyPI, pre-built docker images or installers. It is for simple testing purposes. Refer to this [guide](./deploy/standalone-deploy/).

### Cluster deployment
Deploying FATE to multiple nodes to achieve scalability, reliability and manageability.
- [Cluster deployment by CLI](./deploy/cluster-deploy): Using CLI to deploy a FATE cluster.

### Quick Start
- [Training Demo With Installing FATE Only From Pypi](doc/2.0/fate/ml)
- [Training Demo With Installing FATE AND FATE-Flow From Pypi](doc/2.0/fate/quick_start.md)

### More examples
- [ML examples](examples/launchers)
- [PipeLine examples](examples/pipeline)

## Documentation

### FATE Design
- [Architecture](./doc/architecture/README.md): Building Unified and Standardized API for Heterogeneous Computing Engines Interconnection
- [FATE Algorithm Components](./doc/2.0/fate/components/README.md): Building Standardized Algorithm Components for different Scheduling Engines
- [OSX (Open Site Exchange)](./doc/2.0/osx/osx.md): Building Open Platform for Cross-Site Communication Interconnection
- [FATE-Flow](https://github.com/FederatedAI/FATE-Flow/blob/main/doc/fate_flow.md): Building Open and Standardized Scheduling Platform for Scheduling Interconnection
- [PipeLine Design](https://github.com/FederatedAI/FATE-Client/blob/main/doc/pipeline.md): Building Scalable Federated DSL for Application Layer Interconnection And Providing Tools For Fast Federated Modeling
- [RoadMap](./doc/images/roadmap.png)
- [Paper & Conference](./doc/resources/README.md)

## Related Repositories (Projects)
- [KubeFATE](https://github.com/FederatedAI/KubeFATE): An operational tool for the FATE platform using cloud native technologies such as containers and Kubernetes.
- [FATE-Flow](https://github.com/FederatedAI/FATE-Flow): A multi-party secure task scheduling platform for federated learning pipeline.
- [FATE-Board](https://github.com/FederatedAI/FATE-Board): A suite of visualization tools to explore and understand federated models easily and effectively.
- [FATE-Serving](https://github.com/FederatedAI/FATE-Serving): A high-performance and production-ready serving system for federated learning models.
- [FATE-Cloud](https://github.com/FederatedAI/FATE-Cloud): An infrastructure for building and managing industrial-grade federated learning cloud services.
- [EggRoll](https://github.com/WeBankFinTech/eggroll): A simple high-performance computing framework for (federated) machine learning.
- [AnsibleFATE](https://github.com/FederatedAI/AnsibleFATE): A tool to optimize and automate the configuration and deployment operations via Ansible.
- [FATE-Builder](https://github.com/FederatedAI/FATE-Builder): A tool to build package and docker image for FATE and KubeFATE.
- [FATE-Client](https://github.com/FederatedAI/FATE-Client): A tool to enable fast federated modeling tasks for FATE.
- [FATE-Test](https://github.com/FederatedAI/FATE-Test): An automated testing tool for FATE, including tests and benchmark comparisons.
- [FATE-LLM](https://github.com/FederatedAI/FATE-LLM/blob/main/README.md) : A framework to support federated learning for large language models(LLMs).
## Governance

[FATE-Community](https://github.com/FederatedAI/FATE-Community) contains all the documents about how the community members coopearte with each other.

- [GOVERNANCE.md](https://github.com/FederatedAI/FATE-Community/blob/master/GOVERNANCE.md) documents the governance model of the project.
- [Minutes](https://github.com/FederatedAI/FATE-Community/blob/master/meeting-minutes) of working meetings
- [Development Process Guidelines](https://github.com/FederatedAI/FATE-Community/blob/master/FederatedAI_PROJECT_PROCESS_GUIDELINE.md)
- [Security Release Process](https://github.com/FederatedAI/FATE-Community/blob/master/SECURITY.md)

## Getting Involved

### Contributing
FATE is an inclusive and open community. We welcome developers who are interested in making FATE better! Contributions of all kinds are welcome. Please refer to the general [contributing guideline](https://github.com/FederatedAI/FATE-Community/blob/master/CONTRIBUTING.md) of all FATE projects and the contributing guideline of each repository.

### Mailing list

Join the FATE user [mailing list](https://groups.io/g/Fate-FedAI), and stay connected with the community and learn about the latest news and information of the FATE project. Discussion and feedback of FATE project are welcome.

### Bugs or feature requests

File bugs and features requests via the [GitHub issues](https://github.com/FederatedAI/FATE/issues). If you need help, ask your questions via the mailing list.

### Contact emails

Maintainers: FedAI-maintainers @ groups.io

Security Response Committee: FATE-security @ groups.io

### Twitter

Follow us on twitter [@FATEFedAI](https://twitter.com/FateFedAI)

### FAQ
https://github.com/FederatedAI/FATE/wiki

## License
[Apache License 2.0](LICENSE)


.. image:: https://raw.githubusercontent.com/quantumlib/Cirq/main/docs/images/Cirq_logo_color.png
  :target: https://github.com/quantumlib/cirq
  :alt: Cirq
  :width: 500px

Cirq is a Python library for writing, manipulating, and optimizing quantum
circuits and running them against quantum computers and simulators.

.. image:: https://github.com/quantumlib/Cirq/actions/workflows/ci.yml/badge.svg?event=schedule
  :target: https://github.com/quantumlib/Cirq
  :alt: Build Status

.. image:: https://codecov.io/gh/quantumlib/Cirq/branch/main/graph/badge.svg
  :target: https://codecov.io/gh/quantumlib/Cirq

.. image:: https://badge.fury.io/py/cirq.svg
  :target: https://badge.fury.io/py/cirq

Installation and Documentation
------------------------------

Cirq documentation is available at `quantumai.google/cirq <https://quantumai.google/cirq>`_.

Documentation for the latest **pre-release** version of cirq (tracks the repository's main branch; what you get if you ``pip install cirq~=1.0.dev``), is available `here <https://quantumai.google/reference/python/cirq/all_symbols?version=nightly>`__.

Documentation for the latest **stable** version of cirq (what you get if you ``pip install cirq``) is available `here <https://quantumai.google/reference/python/cirq/all_symbols>`__.

- `Installation <https://quantumai.google/cirq/start/install>`_
- `Documentation <https://quantumai.google/cirq>`_
- `Tutorials <https://quantumai.google/cirq/build>`_

For a comprehensive list all of the interactive Jupyter Notebooks in our repo (including the ones not yet published to the site) open our repo in `Colab <https://colab.research.google.com/github/quantumlib/Cirq>`_.

For the latest news regarding Cirq, sign up to the `Cirq-announce email list <https://groups.google.com/forum/#!forum/cirq-announce>`__!

Hello Qubit
-----------

A simple example to get you up and running:

.. code-block:: python

  import cirq

  # Pick a qubit.
  qubit = cirq.GridQubit(0, 0)

  # Create a circuit
  circuit = cirq.Circuit(
      cirq.X(qubit)**0.5,  # Square root of NOT.
      cirq.measure(qubit, key='m')  # Measurement.
  )
  print("Circuit:")
  print(circuit)

  # Simulate the circuit several times.
  simulator = cirq.Simulator()
  result = simulator.run(circuit, repetitions=20)
  print("Results:")
  print(result)

Example output:

.. code-block::

  Circuit:
  (0, 0): ───X^0.5───M('m')───
  Results:
  m=11000111111011001000

Feature requests / Bugs / Questions
-----------------------------------

If you have feature requests or you found a bug, please `file them on GitHub <https://github.com/quantumlib/Cirq/issues/new/choose>`__.

For questions about how to use Cirq post to
`Quantum Computing Stack Exchange <https://quantumcomputing.stackexchange.com/>`__ with the
`cirq <https://quantumcomputing.stackexchange.com/questions/tagged/cirq>`__ tag.

How to cite Cirq
----------------

Cirq is uploaded to Zenodo automatically. Click on the badge below to see all the citation formats for all versions.

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4062499.svg
  :target: https://doi.org/10.5281/zenodo.4062499
  :alt: DOI

Cirq Contributors Community
---------------------------

We welcome contributions! Before opening your first PR, a good place to start is to read our
`guidelines <https://github.com/quantumlib/cirq/blob/main/CONTRIBUTING.md>`__.

We are dedicated to cultivating an open and inclusive community to build software for near term quantum computers.
Please read our `code of conduct <https://github.com/quantumlib/cirq/blob/main/CODE_OF_CONDUCT.md>`__ for the rules of engagement within our community.

**Cirq Cynque** is our weekly meeting for contributors to discuss upcoming features, designs, issues, community and status of different efforts.
To get an invitation please join the `cirq-dev email list <https://groups.google.com/forum/#!forum/cirq-dev>`__ which also serves as yet another platform to discuss contributions and design ideas.

See Also
--------

For those interested in using quantum computers to solve problems in
chemistry and materials science, we encourage exploring
`OpenFermion <https://github.com/quantumlib/openfermion>`__ and
its sister library for compiling quantum simulation algorithms in Cirq,
`OpenFermion-Cirq <https://github.com/quantumlib/openfermion-cirq>`__.

For machine learning enthusiasts, `Tensorflow Quantum <https://github.com/tensorflow/quantum>`__ is a great project to check out!

For a powerful quantum circuit simulator that integrates well with Cirq, we recommend looking at `qsim <https://github.com/quantumlib/qsim>`__.

Finally, `ReCirq <https://github.com/quantumlib/ReCirq>`__ contains real world experiments using Cirq.

Cirq is not an official Google product. Copyright 2019 The Cirq Developers


## H2O Wave

### Realtime Web Apps and Dashboards for Python and R

**New!** *[R Language API](https://wave.h2o.ai/blog/r-api-preview) Build and control Wave dashboards using R!*

H2O Wave is a software stack for building beautiful, low-latency, realtime, browser-based applications and dashboards entirely in Python/R without using HTML, Javascript, or CSS.

H2O Wave excels at capturing information from multiple sources and broadcasting them live over the web, letting you build and deploy realtime analytics with dramatically less effort.

- [Download](https://github.com/h2oai/wave/releases) / [How to install](https://wave.h2o.ai/docs/installation/)
- [Documentation](https://wave.h2o.ai/): [Getting Started](https://wave.h2o.ai/docs/getting-started/) / [Widgets](https://wave.h2o.ai/docs/widgets/overview) / [Gallery and Examples](https://wave.h2o.ai/docs/examples/) / [Python API](https://wave.h2o.ai/docs/api/index/)
- [Developing](https://github.com/h2oai/wave/blob/main/.github/CONTRIBUTING.md#development-setup)
- [Discuss / Support / Q&A / Ideas](https://github.com/h2oai/wave/discussions)
- [Report a problem](https://github.com/h2oai/wave/issues/new/choose)
- [Blog](https://wave.h2o.ai/blog/) / [@h2o_wave](https://twitter.com/h2o_wave)

### Live demos

- [Wave Tour](https://huggingface.co/spaces/h2oai/wave-tour) - 200+ interactive examples.
- [Wave University](https://huggingface.co/spaces/h2oai/wave-university) - Learn Wave basics interactively.
- [Wave Theme Generator](https://huggingface.co/spaces/h2oai/theme-generator) - Create your color theme.

### Responsive Layouts

![Dashboard](website/blog/assets/2021-01-17/dashboards.png)

![Dashboard](website/blog/assets/2021-01-17/dashboard_red.png)

![Dashboard](website/blog/assets/2021-01-17/dashboard_blue.png)

![Dashboard](website/blog/assets/2021-01-17/dashboard_orange.png)

![Dashboard](website/blog/assets/2021-01-17/dashboard_cyan.png)

![Dashboard](website/blog/assets/2021-01-17/dashboard_mint.png)

![Dashboard](website/blog/assets/2021-01-17/dashboard_purple.png)

Code: <https://github.com/h2oai/wave/tree/main/py/demo>

### Themes

![Light Theme](assets/visuals/theme-light.png)

![Neon Theme](assets/visuals/theme-neon.png)

(Coming soon!)

![Dark Theme](assets/visuals/theme-dark.png)

### 80+ components, 250+ examples

More at <https://wave.h2o.ai/docs/examples>

![Components](assets/visuals/components-1.png)

![Visualizations](assets/visuals/components-2.png)

### License

H2O Wave is licensed under the Apache License 2.0. See [LICENSE](LICENSE) for more information.


# Microsoft Azure CLI

[![Python](https://img.shields.io/pypi/pyversions/azure-cli.svg?maxAge=2592000)](https://pypi.python.org/pypi/azure-cli)
[![Build Status](https://dev.azure.com/azure-sdk/public/_apis/build/status/cli/Azure.azure-cli?branchName=dev)](https://dev.azure.com/azure-sdk/public/_build/latest?definitionId=246&branchName=dev)
[![Slack](https://img.shields.io/badge/Slack-azurecli.slack.com-blue.svg)](https://azurecli.slack.com)

A great cloud needs great tools; we're excited to introduce *Azure CLI*, our next generation multi-platform command line experience for Azure.

Take a test run now from [Azure Cloud Shell](https://portal.azure.com/#cloudshell)!

## Installation

Please refer to the [install guide](https://docs.microsoft.com/cli/azure/install-azure-cli) for detailed install instructions.

A list of common install issues and their resolutions are available at [install troubleshooting](https://github.com/Azure/azure-cli/blob/dev/doc/install_troubleshooting.md).

### Developer installation (see below)

- [Docker](#docker)
- [Edge Builds](#edge-builds)
- [Developer Setup](#developer-setup)

## Usage

```bash
$ az [ group ] [ subgroup ] [ command ] {parameters}
```

### Get Started

Please refer to the ["get started" guide](https://docs.microsoft.com/cli/azure/get-started-with-az-cli2) for in-depth instructions.

For usage and help content, pass in the `-h` parameter, for example:

```bash
$ az storage -h
$ az vm create -h
```

### Highlights

Here are a few features and concepts that can help you get the most out of the Azure CLI.

![Azure CLI Highlight Reel](doc/assets/AzBlogAnimation4.gif)

The following examples are showing using the `--output table` format, you can change your default using the `az configure` command.

#### Tab completion

We support tab-completion for groups, commands, and some parameters

```bash
# looking up resource group and name
$ az vm show -g [tab][tab]
AccountingGroup   RGOne  WebPropertiesRG

$ az vm show -g WebPropertiesRG -n [tab][tab]
StoreVM  Bizlogic

$ az vm show -g WebPropertiesRG -n Bizlogic
```

#### Query

You can use the `--query` parameter and the [JMESPath](http://jmespath.org/) query syntax to customize your output.

```bash
$ az vm list --query "[?provisioningState=='Succeeded'].{ name: name, os: storageProfile.osDisk.osType }"
Name                    Os
----------------------  -------
storevm                 Linux
bizlogic                Linux
demo32111vm             Windows
dcos-master-39DB807E-0  Linux
```

#### Exit codes

For scripting purposes, we output certain exit codes for differing scenarios.

|Exit Code   |Scenario   |
|---|---|
|0  |Command ran successfully.   |
|1   |Generic error; server returned bad status code, CLI validation failed, etc.   |
|2   |Parser error; check input to command line.   |
|3   |Missing ARM resource; used for existence check from `show` commands.   |

### Common scenarios and use Azure CLI effectively

Please check [Tips for using Azure CLI effectively](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively). It describes some common scenarios:

- [Output formatting (json, table, or tsv)](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#output-formatting-json-table-or-tsv)
- [Pass values from one command to another](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#pass-values-from-one-command-to-another)
- [Async operations](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#async-operations)
- [Generic update arguments](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#generic-update-arguments)
- [Generic resource commands - `az resource`](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#generic-resource-commands---az-resource)
- [REST API command - `az rest`](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#rest-api-command---az-rest)
- [Quoting issues](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#quoting-issues)
- [Work behind a proxy](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#work-behind-a-proxy)
- [Concurrent builds](https://docs.microsoft.com/en-us/cli/azure/use-cli-effectively#concurrent-builds)

### More samples and snippets

For more usage examples, take a look at our [GitHub samples repo](http://github.com/Azure/azure-cli-samples) or [https://docs.microsoft.com/cli/azure/overview](https://docs.microsoft.com/cli/azure/overview).

### Write and run commands in Visual Studio Code

With the [Azure CLI Tools](https://marketplace.visualstudio.com/items?itemName=ms-vscode.azurecli) Visual Studio Code extension, you can create `.azcli` files and use these features:
- IntelliSense for commands and their arguments.
- Snippets for commands, inserting required arguments automatically.
- Run the current command in the integrated terminal.
- Run the current command and show its output in a side-by-side editor.
- Show documentation on mouse hover.
- Display current subscription and defaults in status bar.
- To enable IntelliSense for other file types like `.ps1` or `.sh`, see [microsoft/vscode-azurecli#48](https://github.com/microsoft/vscode-azurecli/issues/48).

![Azure CLI Tools in Action](https://github.com/microsoft/vscode-azurecli/blob/main/images/in_action.gif?raw=true)

## Data Collection

The software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. You may turn off the telemetry as described in the repository. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft's privacy statement. Our privacy statement is located at https://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices.

### Telemetry Configuration

Telemetry collection is on by default. To opt out, please run `az config set core.collect_telemetry=no` to turn it off.

## Reporting issues and feedback

If you encounter any bugs with the tool please file an issue in the [Issues](https://github.com/Azure/azure-cli/issues) section of our GitHub repo.

To provide feedback from the command line, try the `az feedback` command.

\[Microsoft internal] You may contact the developer team via azpycli@microsoft.com.

## Developer installation

### Docker

We maintain a Docker image preconfigured with the Azure CLI.
See our [Docker tags](https://mcr.microsoft.com/v2/azure-cli/tags/list) for available versions.

```bash
$ docker run -u $(id -u):$(id -g) -v ${HOME}:/home/az -e HOME=/home/az --rm -it mcr.microsoft.com/azure-cli:<version>
```

### Edge builds

If you want to get the latest build from the `dev` branch, you can use our "edge" builds.

You can download the latest builds by following the links below:

|      Package      | Link                                       |
|:-----------------:|:-------------------------------------------|
|        MSI        | https://aka.ms/InstallAzureCliWindowsEdge  |
| Homebrew Formula  | https://aka.ms/InstallAzureCliHomebrewEdge |
| Ubuntu Bionic Deb | https://aka.ms/InstallAzureCliBionicEdge   |
| Ubuntu Focal Deb  | https://aka.ms/InstallAzureCliFocalEdge    |
| Ubuntu Jammy Deb  | https://aka.ms/InstallAzureCliJammyEdge    |
|      RPM el8      | https://aka.ms/InstallAzureCliRpmEl8Edge   |

On Windows, you need to uninstall the official version before installing the edge build. (See https://github.com/Azure/azure-cli/issues/25607#issuecomment-1452855212)

You can easily install the latest Homebrew edge build with the following command:

```bash
# You need to uninstall the stable version with `brew uninstall azure-cli` first
curl --location --silent --output azure-cli.rb https://aka.ms/InstallAzureCliHomebrewEdge
brew install --build-from-source azure-cli.rb
```

You can install the edge build on Ubuntu Jammy with the following command:

```bash
curl --location --silent --output azure-cli_jammy.deb https://aka.ms/InstallAzureCliJammyEdge && dpkg -i azure-cli_jammy.deb
```

And install the edge build with rpm package on RHEL 8 or CentOS Stream 8:

```bash
dnf install -y $(curl --location --silent --output /dev/null --write-out %{url_effective} https://aka.ms/InstallAzureCliRpmEl8Edge)
```

Here's an example of installing edge builds with pip3 in a virtual environment. The `--upgrade-strategy=eager` option will install the edge builds of dependencies as well.

```bash
$ python3 -m venv env
$ . env/bin/activate
$ pip3 install --pre azure-cli --extra-index-url https://azurecliprod.blob.core.windows.net/edge --upgrade-strategy=eager
```

To upgrade your current edge build pass the `--upgrade` option. The `--no-cache-dir` option is also recommended since
the feed is frequently updated.

```bash
$ pip3 install --upgrade --pre azure-cli --extra-index-url https://azurecliprod.blob.core.windows.net/edge --no-cache-dir --upgrade-strategy=eager
```

The edge build is generated for each PR merged to the `dev` branch as a part of the Azure DevOps Pipelines.

### Get builds of arbitrary commit or PR

If you would like to get builds of arbitrary commit or PR, see:

[Try new features before release](doc/try_new_features_before_release.md)

## Developer setup

If you would like to setup a development environment and contribute to the CLI, see:

[Configuring Your Machine](https://github.com/Azure/azure-cli/blob/dev/doc/configuring_your_machine.md)

[Authoring Command Modules](https://github.com/Azure/azure-cli/tree/dev/doc/authoring_command_modules)

[Code Generation](https://github.com/Azure/aaz-dev-tools)

## Contribute code

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

If you would like to become an active contributor to this project please
follow the instructions provided in [Microsoft Open Source Guidelines](https://opensource.microsoft.com/collaborate).