Add github action to automatically push to pypi on Release x.y.z comm…

…it (#681)

* Add github action to automatically push to pypi on Release x.y.z commit

* some housekeeping for pypi upload

* add version.py

Co-authored-by: Jong Wook Kim <[email protected]>

.github/workflows/python-publish.yml

@@ -0,0 +1,37 @@
+name: Release
+
+on:
+ push:
+ branches:
+ - main
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+ - uses: actions-ecosystem/action-regex-match@v2
+ id: regex-match
+ with:
+ text: ${{ github.event.head_commit.message }}
+ regex: '^Release ([^ ]+)'
+ - name: Set up Python
+ uses: actions/setup-python@v2
+ with:
+ python-version: '3.8'
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install setuptools wheel twine
+ - name: Release
+ if: ${{ steps.regex-match.outputs.match != '' }}
+ uses: softprops/action-gh-release@v1
+ with:
+ tag_name: v${{ steps.regex-match.outputs.group1 }}
+ - name: Build and publish
+ if: ${{ steps.regex-match.outputs.match != '' }}
+ env:
+ TWINE_USERNAME: __token__
+ TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+ run: |
+ python setup.py sdist
+ twine upload dist/*

MANIFEST.in

@@ -1,3 +1,6 @@
+include requirements.txt
+include README.md
+include LICENSE
 include whisper/assets/*
 include whisper/assets/gpt2/*
 include whisper/assets/multilingual/*

README.md

@@ -2,22 +2,22 @@
 
 [[Blog]](https://openai.com/blog/whisper)
 [[Paper]](https://arxiv.org/abs/2212.04356)
-[[Model card]](model-card.md)
+[[Model card]](https://github.com/openai/whisper/blob/main/model-card.md)
 [[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)
 
 Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
 
 
 ## Approach
 
-![Approach](approach.png)
+![Approach](https://raw.githubusercontent.com/openai/whisper/main/approach.png)
 
 A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
 
 
 ## Setup
 
-We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. The following command will pull and install the latest commit from this repository, along with its Python dependencies 
+We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. The following command will pull and install the latest commit from this repository, along with its Python dependencies:
 
  pip install git+https://github.com/openai/whisper.git 
 
@@ -68,7 +68,7 @@ For English-only applications, the `.en` models tend to perform better, especial
 
 Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of Fleurs dataset, using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356). The smaller is better.
 
-![WER breakdown by language](language-breakdown.svg)
+![WER breakdown by language](https://raw.githubusercontent.com/openai/whisper/main/language-breakdown.svg)
 
 
 
@@ -90,7 +90,7 @@ Run the following to view all available options:
 
  whisper --help
 
-See [tokenizer.py](whisper/tokenizer.py) for the list of all available languages.
+See [tokenizer.py](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py) for the list of all available languages.
 
 
 ## Python usage
@@ -140,4 +140,4 @@ Please use the [🙌 Show and tell](https://github.com/openai/whisper/discussion
 
 ## License
 
-The code and the model weights of Whisper are released under the MIT License. See [LICENSE](LICENSE) for further details.
+The code and the model weights of Whisper are released under the MIT License. See [LICENSE](https://github.com/openai/whisper/blob/main/LICENSE) for further details.

setup.py

@@ -3,11 +3,19 @@
 import pkg_resources
 from setuptools import setup, find_packages
 
+
+def read_version(fname="whisper/version.py"):
+ exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
+ return locals()["__version__"]
+
+
 setup(
- name="whisper",
+ name="openai-whisper",
  py_modules=["whisper"],
- version="1.0",
+ version=read_version(),
  description="Robust Speech Recognition via Large-Scale Weak Supervision",
+ long_description=open("README.md", encoding="utf-8").read(),
+ long_description_content_type="text/markdown",
  readme="README.md",
  python_requires=">=3.7",
  author="OpenAI",
@@ -20,9 +28,9 @@
  open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
  )
  ],
- entry_points = {
- 'console_scripts': ['whisper=whisper.transcribe:cli'],
+ entry_points={
+ "console_scripts": ["whisper=whisper.transcribe:cli"],
  },
  include_package_data=True,
- extras_require={'dev': ['pytest']},
+ extras_require={"dev": ["pytest"]},
 )

whisper/__init__.py

@@ -12,6 +12,7 @@
 from .decoding import DecodingOptions, DecodingResult, decode, detect_language
 from .model import Whisper, ModelDimensions
 from .transcribe import transcribe
+from .version import __version__
 
 
 _MODELS = {

whisper/version.py

@@ -0,0 +1 @@
+__version__ = "20230117"