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Add github action to automatically push to pypi on Release x.y.z commit (#681)

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* 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 <jongwook@nyu.edu>
This commit is contained in:
Romain Beaumont
2023-01-18 00:50:26 +01:00
committed by GitHub
parent f0083e7eb2
commit b9f9b433ae
6 changed files with 61 additions and 11 deletions
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README.md
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@@ -2,7 +2,7 @@
[[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.
@@ -10,14 +10,14 @@ Whisper is a general-purpose speech recognition model. It is trained on a large
## 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.