semvec/pypi-packages huggingface dataset:
django-postgres-extensions-ng 0.9.5
Extra features for django.contrib.postgres

Django Postgres Extensions adds a lot of functionality to Django.contrib.postgres, specifically in relation to ArrayField, HStoreField and JSONField, including much better form fields for dealing with these field types. The app also includes an Array Many To Many Field, so you can store the relationship in an array column instead of requiring an extra database table.

Check out http://django-postgres-extensions.readthedocs.io/en/latest/ to get started.
bpc 0.1.3
Burmese text normalizer, wordbreak, converter, cleaner and phonemizer for speech related tasks.
Requires Python: >=3.6

# <div align="center"> BURMESE PHONEMIZER AND CLEANER(BPC) </div>

<div align="center">

[![Python package](https://github.com/1chimaruGin/Burmese_Phomizer_and_Cleaner/actions/workflows/python-package.yml/badge.svg)](https://github.com/1chimaruGin/Burmese_Phomizer_and_Cleaner/actions/workflows/python-package.yml)
[![Total alerts](https://img.shields.io/lgtm/alerts/g/1chimaruGin/Burmese_Phomizer_and_Cleaner.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/1chimaruGin/Burmese_Phomizer_and_Cleaner/alerts/)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/1chimaruGin/Burmese_Phomizer_and_Cleaner.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/1chimaruGin/Burmese_Phomizer_and_Cleaner/context:python)

<p>Burmese Language data prepartion for speech related tasks.</p>
<p>
</p>
</div>

## Installation

```bash
$ pip install bpc
```
or
```bash
$ pip install git+git://github.com:1chimaruGin/Burmese_Phomizer_and_Cleaner.git
```

## Usage

**For text Cleaning**
```python
from bpc import Cleaner

cc = Cleaner()
cc.clean_text("မင်္ဂလာပါ? မင်္ဂလာပါ။ ၀န်းရံ ဝ၁၂၃၄ 5B")

# output: မင်္ဂလာပါ မင်္ဂလာပါ။ ဝန်းရံ ၀၁၂၃၄ ၅ဘီ
```

**For phonemization**

```python
from bpc import BurmesePhoneme

bp = BurmesePhonemizer()
bp.text_to_phone("မင်္ဂလာပါ")

# output: ['m', 'ŋ', 'ɡ', 'l', 't', 's', 'p', 'ˈe']
```

**For data preparation**

```python
from bpc.dataset import PrepareDataset

dataset = PrepareDataset()
dataset.prepare_data(path='path/to/dataset', method='kfold', save=True)
```
## References

* https://github.com/espnet/espnet
* https://github.com/bootphon/phonemizer

## 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
}

@article{Bernard2021,
  doi = {10.21105/joss.03958},
  url = {https://doi.org/10.21105/joss.03958},
  year = {2021},
  publisher = {The Open Journal},
  volume = {6},
  number = {68},
  pages = {3958},
  author = {Mathieu Bernard and Hadrien Titeux},
  title = {Phonemizer: Text to Phones Transcription for Multiple Languages in Python},
  journal = {Journal of Open Source Software}
}
```
ultocr 0.3.1
text detection + text recognition
Requires Python: >=3.6

### Quickstart
```bash
pip install torch==1.7.0+cu101 torchvision==0.8.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install --upgrade ultocr  # install our project with package

# for inference phase
from ultocr.inference import OCR
from PIL import Image
model = OCR(det_model='DB', reg_model='MASTER')
image = Image.open('..')  # ..is the path of image
result = model.get_result(image)
```
Or view in google colab [demo](https://colab.research.google.com/drive/1rncaTU_S8mjVusaOq3u9L_FT8ADSECeC?usp=sharing)

### Install
```bash
git clone https://github.com/cuongngm/text-in-image
pip install torch==1.7.0+cu101 torchvision==0.8.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
bash scripts/download_weights.sh
```
### Prepare data


### Pretrained model
| Model                                                                                        | size(MB) |
|----------------------------------------------------------------------------------------------|------ |
| [DB](https://drive.google.com/file/d/1IhNXc1aaiuNtcC-LniwgfYylc6Du5LIX/view?usp=sharing)     |140    
| [MASTER](https://drive.google.com/file/d/1xQccsZiNVWoB1g59fi11RHn1CkpjRlVN/view?usp=sharing) |261     


### Train
Custom params in each config file of config folder then:

Single gpu training:
```bash
python train.py --config config/db_resnet50.yaml --use_dist False
# tracking with mlflow
mlflow run text-in-image -P config=config/db_resnet50.yaml -P use_dist=False -P device=1
```
Multi gpu training:
```bash
# assume we have 2 gpu
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=2 --master_addr=127.0.0.1 --master_post=5555 train.py --config config/db_resnet50.yaml
```

### Serve and Inference
```bash
python run.py
```
Then, open your browser at http://127.0.0.1:8000/docs. Request url of the image, the result is as follows:
<!--
![](assets/fastapi.png)
![](assets/fastapi.png)
-->

<div align=center>
<img src="assets/fastapi.png" width="800" height="200" />
</div>

### Todo
- [x] Multi gpu training
- [x] Tracking experiments with Mlflow
- [x] Model serving with FastAPI
- [ ] Add more text detection and recognition model

### Reference
- [DB_text_minimal](https://github.com/huyhoang17/DB_text_minimal)
- [pytorchOCR](https://github.com/BADBADBADBOY/pytorchOCR)
- [MASTER-pytorch](https://github.com/wenwenyu/MASTER-pytorch)
- [DBNet.pytorch](https://github.com/WenmuZhou/DBNet.pytorch)
fabricity 1.15.1
[Fabric3 fork] Fabricity is a simple, Pythonic tool for remote execution and deployment.


Fabric3 is a fork of `Fabric <http://fabfile.org>`_ to provide compatability
with Python 3.9+.

The goal is to stay 100% compatible with the original Fabric.  Any new releases
of Fabric will also be released here.  Please file issues for any differences
you find. Known differences are `documented on github
<https://github.com/torico-tokyo/fabricity>`.

To find out what's new in this version of Fabric, please see `the changelog
<http://fabfile.org/changelog.html>`_ of the original Fabric.

For more information, please see the Fabric website or execute ``fab --help``.