Add descriptions for isort

chintu619 · May 18, 2022 · 6974dd4 · 6974dd4
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -64,21 +64,21 @@ To port models from zenodo using Hugging Face hub,
 1. Create a Hugging Face account - https://huggingface.co/
 2. Request to be added to espnet organisation - https://huggingface.co/espnet
 3. Go to `egs2/RECIPE/*/scripts/utils` and run `./upload_models_to_hub.sh "ZENODO_MODEL_NAME"`
-   
+
 To upload models using Huggingface-cli follow the following steps:
 You can also refer to https://huggingface.co/docs/transformers/model_sharing
 1. Create a Hugging Face account - https://huggingface.co/
-2. Request to be added to espnet organisation - https://huggingface.co/espnet 
-3. Run huggingface-cli login (You can get the token request at this step under setting > Access Tokens > espnet token  
+2. Request to be added to espnet organisation - https://huggingface.co/espnet
+3. Run huggingface-cli login (You can get the token request at this step under setting > Access Tokens > espnet token
 4. `huggingface-cli repo create your-model-name --organization espnet`
 5. `git clone https://huggingface.co/username/your-model-name` (clone this outside ESPNet to avoid issues as this a git repo)
 6. `cd your-model-name`
 7. `git lfs install`
-8. copy contents from exp diretory of your recipe into this directory (Check other models of similar task under ESPNet to confirm your directory structure) 
+8. copy contents from exp diretory of your recipe into this directory (Check other models of similar task under ESPNet to confirm your directory structure)
 9. `git add . `
 10. `git commit -m "Add model files"`
 11. `git push`
-12. Check if the inference demo on HF is running successfully to verify the upload      
+12. Check if the inference demo on HF is running successfully to verify the upload
 
 #### 1.3.3 Additional requirements for new recipe
 
@@ -91,12 +91,13 @@ to its differences.
 - If a recipe for a new corpus is proposed, you should add its name and information to:
 https://github.com/espnet/espnet/blob/master/egs/README.md if it's a ESPnet1 recipe,
 or https://github.com/espnet/espnet/blob/master/egs2/README.md + `db.sh` if it's a ESPnet2 recipe.
-   
+
 #### 1.3.4 Checklist before you submit the recipe-based PR
 
 - [ ] be careful about the name for the recipe. It is recommended to follow naming conventions of the other recipes
 - [ ] common/shared files are linked with **soft link** (see Section 1.3.3)
-- [ ] modified or new python scripts should be passed through **latest** black formating (by using python package black). The command to be executed could be `black espnet espnet2 test utils setup.py egs*/*/*/local egs2/TEMPLATE/asr1/pyscripts`
+- [ ] modified or new python scripts should be passed through **latest** black formating (by using python package black). The command to be executed could be `black espnet espnet2 test utils setup.py egs*/*/*/local egs2/TEMPLATE/*/pyscripts tools/*.py ci/*.py`
+- [ ] modified or new python scripts should be passed through **latest** isort formating (by using python package isort). The command to be executed could be `isort espnet espnet2 test utils setup.py egs*/*/*/local egs2/TEMPLATE/*/pyscripts tools/*.py ci/*.py`
 - [ ] cluster settings should be set as **default** (e.g., cmd.sh conf/slurm.conf conf/queue.conf conf/pbs.conf)
 - [ ] update `egs/README.md` or `egs2/README.md` with corresponding recipes
 - [ ] add corresponding entry in `egs2/TEMPLATE/db.sh` for a new corpus
@@ -135,7 +136,7 @@ $ pip install -e ".[test]"
 
 ### 4.1 Python
 
-Then you can run the entire test suite using [flake8](http://flake8.pycqa.org/en/latest/), [autopep8](https://github.com/hhatto/autopep8), [black](https://github.com/psf/black) and [pytest](https://docs.pytest.org/en/latest/) with [coverage](https://pytest-cov.readthedocs.io/en/latest/reporting.html) by
+Then you can run the entire test suite using [flake8](http://flake8.pycqa.org/en/latest/), [autopep8](https://github.com/hhatto/autopep8), [black](https://github.com/psf/black), [isort](https://github.com/PyCQA/isort) and [pytest](https://docs.pytest.org/en/latest/) with [coverage](https://pytest-cov.readthedocs.io/en/latest/reporting.html) by
 ``` console
 ./ci/test_python.sh
 ```

diff --git a/README.md b/README.md
@@ -18,7 +18,8 @@
 [![GitHub license](https://img.shields.io/github/license/espnet/espnet.svg)](https://github.com/espnet/espnet)
 [![codecov](https://codecov.io/gh/espnet/espnet/branch/master/graph/badge.svg)](https://codecov.io/gh/espnet/espnet)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
-[![Mergify Status](https://img.shields.io/endpoint.svg?url=https://gh.mergify.io/badges/espnet/espnet&style=flat)](https://mergify.io)
+[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
+[![Mergify Status](https://img.shields.io/endpoint.svg?url=https://api.mergify.com/v1/badges/espnet/espnet&style=flat)](https://mergify.com)
 [![Gitter](https://badges.gitter.im/espnet-en/community.svg)](https://gitter.im/espnet-en/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
 
 [**Docs**](https://espnet.github.io/espnet/)
@@ -78,11 +79,11 @@ ESPnet uses [pytorch](http://pytorch.org/) as a deep learning engine and also fo
 - Self-supervised learning representations as features, using upstream models in [S3PRL](https://github.com/s3prl/s3prl) in frontend.
   - Set `frontend` to be `s3prl`
   - Select any upstream model by setting the `frontend_conf` to the corresponding name.
-- Transfer Learning : 
+- Transfer Learning :
   - easy usage and transfers from models previously trained by your group, or models from [ESPnet huggingface 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 
+- Restricted Self-Attention based on [Longformer](https://arxiv.org/abs/2004.05150) as an encoder for long sequences
 
 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)
@@ -579,10 +580,10 @@ We list the performance on various SLU tasks and dataset using the metric report
 | 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)                | 
+| 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`.
 
 
@@ -737,7 +738,7 @@ 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.
 
 </div></details>

diff --git a/ci/test_python.sh b/ci/test_python.sh
@@ -5,11 +5,16 @@
 
 set -euo pipefail
 
-modules="espnet espnet2 test utils setup.py egs*/*/*/local egs2/TEMPLATE/asr1/pyscripts"
+modules="espnet espnet2 test utils setup.py egs*/*/*/local egs2/TEMPLATE/*/pyscripts tools/*.py ci/*.py"
 
 # black
 if ! black --check ${modules}; then
-    printf 'Please apply:\n    $ black %s\n' "${modules}"
+    printf '[INFO] Please apply black:\n    $ black %s\n' "${modules}"
+    exit 1
+fi
+# isort
+if ! isort -c -v ${modules}; then
+    printf '[INFO] Please apply isort:\n    $ isort %s\n' "${modules}"
     exit 1
 fi