gradio based simple app and ready-to-use web api with FastApi for https://github.com/facebookresearch/fairseq

Screenshots:

• Web api:
• Web app

This api and example app uses CUDA by default if available. there was a wrapper class around huggingface official api named my_translator.py so you can easly integrate it into your own projects.

How to use it?

it is very simple to use, by default this uses CUDA for the computation backend if available otherwise uses CPU.

• (OPTIONAL) Create a virtual environment with anaconda or venv (venc forexample: python3 -m venv ./venv and activate the virtual environment with source ./venv/bin/activate for Linux/macOS and . ./venv/bin/Activate.ps1)
• install the necessary python packages through pip3 install -r ./requirements.txt
• (OPTIONAL) all the translation model will be downloaded from huggingface during program startup if the model is not available locally, download of model will fails if you cannot connect (or slow) to huggingface via your network, to solve this problem you can use third-party huggingface mirror, e.g. use hf-mirror.com through:
  • Bash/Zsh (default by Linux/Mac): export HF_ENDPOINT=https://hf-mirror.com
  • PowerShell (default by Windows): $env:HF_ENDPOINT = "https://hf-mirror.com" (Warning, DON'T USE CMD, USE Powershell or WindowsTerminal with powershell shell engine) and for more information: https://hf-mirror.com/, huggingface.co
• start the app with python3 ./translator_app.py or rest api with python3 ./translator_api.py

How to specify which models we wanna use?

in order to use the wrapper you need to initialize the translator class using my_translator = Translator("1.3b") the literal 1.3b is the simplified model name and all the available values are: "600m", "1.3b", "3.3b", "54b" or model name from hugging face e.g. facebook/nllb-200-distilled-600M or anything else, for further details: please see the source code: my_translator.py