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https://github.com/xhluca/dl-translate

Library for translating between 200 languages. Built on 🤗 transformers.
https://github.com/xhluca/dl-translate

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Library for translating between 200 languages. Built on 🤗 transformers.

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README 

        
# DL Translate



[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5230676.svg)](https://doi.org/10.5281/zenodo.5230676)

[![Downloads](https://static.pepy.tech/personalized-badge/dl-translate?period=total&units=abbreviation&left_color=grey&right_color=orange&left_text=Downloads)](https://pepy.tech/project/dl-translate)

[![License](https://img.shields.io/badge/license-MIT-green)](https://github.com/xhluca/dl-translate/blob/main/LICENSE)



*A translation library for 200 languages built on Huggingface `transformers`*



💻 [GitHub Repository](https://github.com/xhluca/dl-translate)


📚 [Documentation](https://xhluca.github.io/dl-translate)


🐍 [PyPi project](https://pypi.org/project/dl-translate/)


🧪 [Colab Demo](https://colab.research.google.com/github/xhluca/dl-translate/blob/main/demos/colab_demo.ipynb) / [Kaggle Demo](https://www.kaggle.com/xhlulu/dl-translate-demo/)



## Quickstart



Install the library with pip:

```

pip install dl-translate

```



To translate some text:



```python

import dl_translate as dlt



mt = dlt.TranslationModel()  # Slow when you load it for the first time



text_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"

mt.translate(text_hi, source=dlt.lang.HINDI, target=dlt.lang.ENGLISH)

```



Above, you can see that `dlt.lang` contains variables representing each of the 200 available languages with auto-complete support. Alternatively, you can specify the language (e.g. "Arabic") or the language code (e.g. "fr" for French):

```python

text_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."

mt.translate(text_ar, source="Arabic", target="fr")

```



If you want to verify whether a language is available, you can check it:

```python

print(mt.available_languages())  # All languages that you can use

print(mt.available_codes())  # Code corresponding to each language accepted

print(mt.get_lang_code_map())  # Dictionary of lang -> code

```



## Usage



### Selecting a device



When you load the model, you can specify the device:

```python

mt = dlt.TranslationModel(device="auto")

```



By default, the value will be `device="auto"`, which means it will use a GPU if possible. You can also explicitly set `device="cpu"` or `device="gpu"`, or some other strings accepted by [`torch.device()`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device). __In general, it is recommend to use a GPU if you want a reasonable processing time.__



### Choosing a different model



By default, the `m2m100` model will be used. However, there are a few options:



* [mBART-50 Large](https://huggingface.co/transformers/master/model_doc/mbart.html):  Allows translations across 50 languages.

* [m2m100](https://huggingface.co/transformers/model_doc/m2m_100.html): Allows translations across 100 languages.

* [nllb-200](https://huggingface.co/docs/transformers/model_doc/nllb) (New in v0.3): Allows translations across 200 languages, and is faster than m2m100 (On RTX A6000, we can see speed up of 3x).



Here's an example:

```python

# The default approval

mt = dlt.TranslationModel("m2m100")  # Shorthand

mt = dlt.TranslationModel("facebook/m2m100_418M")  # Huggingface repo



# If you want to use mBART-50 Large

mt = dlt.TranslationModel("mbart50")

mt = dlt.TranslationModel("facebook/mbart-large-50-many-to-many-mmt")



# Or NLLB-200 (faster and has 200 languages)

mt = dlt.TranslationModel("nllb200")

mt = dlt.TranslationModel("facebook/nllb-200-distilled-600M")

```



Note that the language code will change depending on the model family. To find out the correct language codes, please read the doc page on available languages or run `mt.available_codes()`.



By default, `dlt.TranslationModel` will download the model from the huggingface repo for [mbart50](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt), [m2m100](https://huggingface.co/facebook/m2m100_418M), or [nllb200](https://huggingface.co/facebook/nllb-200-distilled-600M) and cache it. It's possible to load the model from a path or a model with a similar format, but you will need to specify the `model_family`:

```python

mt = dlt.TranslationModel("/path/to/model/directory/", model_family="mbart50")

mt = dlt.TranslationModel("facebook/m2m100_1.2B", model_family="m2m100")

mt = dlt.TranslationModel("facebook/nllb-200-distilled-600M", model_family="nllb200")

```



Notes:

* Make sure your tokenizer is also stored in the same directory if you load from a file. 

* The available languages will change if you select a different model, so you will not be able to leverage `dlt.lang` or `dlt.utils`.



### Splitting into sentences



It is not recommended to use extremely long texts as it takes more time to process. Instead, you can try to break them down into sentences with the help of `nltk`. First install the library with `pip install nltk`, then run:

```python

import nltk



nltk.download("punkt")



text = "Mr. Smith went to his favorite cafe. There, he met his friend Dr. Doe."

sents = nltk.tokenize.sent_tokenize(text, "english")  # don't use dlt.lang.ENGLISH

" ".join(mt.translate(sents, source=dlt.lang.ENGLISH, target=dlt.lang.FRENCH))

```



### Batch size during translation



It's possible to set a batch size (i.e. the number of elements processed at once) for `mt.translate` and whether you want to see the progress bar or not:



```python

# ...

mt = dlt.TranslationModel()

mt.translate(text, source, target, batch_size=32, verbose=True)

```



If you set `batch_size=None`, it will compute the entire `text` at once rather than splitting into "chunks". We recommend lowering `batch_size` if you do not have a lot of RAM or VRAM and run into CUDA memory error. Set a higher value if you are using a high-end GPU and the VRAM is not fully utilized.



### `dlt.utils` module



An alternative to `mt.available_languages()` is the `dlt.utils` module. You can use it to find out which languages and codes are available:



```python

print(dlt.utils.available_languages('mbart50'))  # All languages that you can use

print(dlt.utils.available_codes('m2m100'))  # Code corresponding to each language accepted

print(dlt.utils.get_lang_code_map('nllb200'))  # Dictionary of lang -> code

```



### Offline usage



Unlike the Google translate or MSFT Translator APIs, this library can be fully used offline. However, you will need to first download the packages and models, and move them to your offline environment to be installed and loaded inside a venv.



First, run in your terminal:

```bash

mkdir dlt

cd dlt

mkdir libraries

pip download -d libraries/ dl-translate

```



Once all the required packages are downloaded, you will need to use huggingface hub to download the files. Install it with `pip install huggingface-hub`. Then, run inside Python:

```python

import shutil

import huggingface_hub as hub



dirname = hub.snapshot_download("facebook/m2m100_418M")

shutil.copytree(dirname, "cached_model_m2m100")  # Copy to a permanent folder

```



Now, move everything in the `dlt` directory to your offline environment. Create a virtual environment and run the following in terminal:

```bash

pip install --no-index --find-links libraries/ dl-translate

```



Now, run inside Python:

```python

import dl_translate as dlt



mt = dlt.TranslationModel("cached_model_m2m100", model_family="m2m100")

```



## Advanced



If you have knowledge of PyTorch and Huggingface Transformers, you can access advanced aspects of the library for more customization:

* **Saving and loading**: If you wish to accelerate the loading time the translation model, you can use `save_obj` and reload it later with `load_obj`. This method is only recommended if you are familiar with `huggingface` and `torch`; please read the docs for more information.

* **Interacting with underlying model and tokenizer**: When initializing `model`, you can pass in arguments for the underlying BART model and tokenizer with `model_options` and `tokenizer_options` respectively. You can also access the underlying `transformers` with `mt.get_transformers_model()`.

* **Keyword arguments for the `generate()` method**: When running `mt.translate`, you can also give `generation_options` that is passed to the `generate()` method of the underlying transformer model.



For more information, please visit the [advanced section of the user guide](https://xhluca.github.io/dl-translate/#advanced).



## Acknowledgement



`dl-translate` is built on top of Huggingface's implementation of two models created by Facebook AI Research.



1. The multilingual BART finetuned on many-to-many translation of over 50 languages, which is [documented here](https://huggingface.co/transformers/master/model_doc/mbart.html) The original paper was written by Tang et. al from Facebook AI Research; you can [find it here](https://arxiv.org/pdf/2008.00401.pdf) and cite it using the following:

    ```

    @article{tang2020multilingual,

        title={Multilingual translation with extensible multilingual pretraining and finetuning},

        author={Tang, Yuqing and Tran, Chau and Li, Xian and Chen, Peng-Jen and Goyal, Naman and Chaudhary, Vishrav and Gu, Jiatao and Fan, Angela},

        journal={arXiv preprint arXiv:2008.00401},

        year={2020}

    }

    ```

2. The transformer model published in [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Fan et. al, which supports over 100 languages. You can cite it here:

   ```

   @misc{fan2020englishcentric,

        title={Beyond English-Centric Multilingual Machine Translation}, 

        author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin},

        year={2020},

        eprint={2010.11125},

        archivePrefix={arXiv},

        primaryClass={cs.CL}

    }

   ```



3. The [no language left behind](https://arxiv.org/abs/2207.04672) model, which extends NMT to 200+ languages. You can cite it here:

    ```

    @misc{nllbteam2022language,

        title={No Language Left Behind: Scaling Human-Centered Machine Translation}, 

        author={NLLB Team and Marta R. Costa-jussà and James Cross and Onur Çelebi and Maha Elbayad and Kenneth Heafield and Kevin Heffernan and Elahe Kalbassi and Janice Lam and Daniel Licht and Jean Maillard and Anna Sun and Skyler Wang and Guillaume Wenzek and Al Youngblood and Bapi Akula and Loic Barrault and Gabriel Mejia Gonzalez and Prangthip Hansanti and John Hoffman and Semarley Jarrett and Kaushik Ram Sadagopan and Dirk Rowe and Shannon Spruit and Chau Tran and Pierre Andrews and Necip Fazil Ayan and Shruti Bhosale and Sergey Edunov and Angela Fan and Cynthia Gao and Vedanuj Goswami and Francisco Guzmán and Philipp Koehn and Alexandre Mourachko and Christophe Ropers and Safiyyah Saleem and Holger Schwenk and Jeff Wang},

        year={2022},

        eprint={2207.04672},

        archivePrefix={arXiv},

        primaryClass={cs.CL}

    }

    ```



`dlt` is a wrapper with useful `utils` to save you time. For huggingface's `transformers`, the following snippet is shown as an example:

```python

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast



article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"

article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."



model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")

tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")



# translate Hindi to French

tokenizer.src_lang = "hi_IN"

encoded_hi = tokenizer(article_hi, return_tensors="pt")

generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"])

tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)

# => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria."



# translate Arabic to English

tokenizer.src_lang = "ar_AR"

encoded_ar = tokenizer(article_ar, return_tensors="pt")

generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])

tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)

# => "The Secretary-General of the United Nations says there is no military solution in Syria."

```



With `dlt`, you can run:

```python

import dl_translate as dlt



article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"

article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."



mt = dlt.TranslationModel()

translated_fr = mt.translate(article_hi, source=dlt.lang.HINDI, target=dlt.lang.FRENCH)

translated_en = mt.translate(article_ar, source=dlt.lang.ARABIC, target=dlt.lang.ENGLISH)

```



Notice you don't have to think about tokenizers, condition generation, pretrained models, and regional codes; you can just tell the model what to translate!



If you are experienced with `huggingface`'s ecosystem, then you should be familiar enough with the example above that you wouldn't need this library. However, if you've never heard of huggingface or mBART, then I hope using this library will give you enough motivation to [learn more about them](https://github.com/huggingface/transformers) :)