README.md

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Easy-translate is a script for translating large text files in your machine using the [M2M100 models](https://arxiv.org/pdf/2010.11125.pdf) from Facebook/Meta AI.

M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
The model that can directly translate between the 9,900 directions of 100 languages.

Easy-Translate is built on top of 🤗HuggingFace's 
[Transformers](https://huggingface.co/docs/transformers/index) and 
🤗HuggingFace's [Accelerate](https://huggingface.co/docs/accelerate/index) library. We support:

 * CPU / GPU / multi-GPU / TPU acceleration
 * BF16 / FP16 / FB32 precision.
 * Automatic batch size finder: Forget CUDA OOM errors. Set an initial batch size, if it doesn't fit, we will automatically adjust it.
 * Sharded Data Parallel to load huge models sharded on multiple GPUs (See: https://huggingface.co/docs/accelerate/fsdp).

Test the 🔌 Online Demo here: https://huggingface.co/spaces/Iker/Translate-100-languages

## Supported languages
See the [Supported languages table](supported_languages.md) for a table of the supported languages and their ids.

**List of supported languages:** 
Afrikaans, Amharic, Arabic, Asturian, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greeek, English, Spanish, Estonian, Persian, Fulah, Finnish, French, WesternFrisian, Irish, Gaelic, Galician, Gujarati, Hausa, Hebrew, Hindi, Croatian, Haitian, Hungarian, Armenian, Indonesian, Igbo, Iloko, Icelandic, Italian, Japanese, Javanese, Georgian, Kazakh, CentralKhmer, Kannada, Korean, Luxembourgish, Ganda, Lingala, Lao, Lithuanian, Latvian, Malagasy, Macedonian, Malayalam, Mongolian, Marathi, Malay, Burmese, Nepali, Dutch, Norwegian, NorthernSotho, Occitan, Oriya, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Albanian, Serbian, Swati, Sundanese, Swedish, Swahili, Tamil, Thai, Tagalog, Tswana, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Wolof, Xhosa, Yiddish, Yoruba, Chinese, Zulu

## Supported Models

 * **Facebook/m2m100_418M**: https://huggingface.co/facebook/m2m100_418M

 * **Facebook/m2m100_1.2B**: https://huggingface.co/facebook/m2m100_1.2B

 * **Facebook/m2m100_12B**: https://huggingface.co/facebook/m2m100-12B-avg-5-ckpt
    
 * Any other m2m100 model from HuggingFace's Hub: https://huggingface.co/models?search=m2m100


## Requirements:

```
Pytorch >= 1.10.0
See: https://pytorch.org/get-started/locally/

Accelerate >= 0.7.1
pip install --upgrade accelerate

HuggingFace Transformers 
pip install --upgrade transformers
```

## Translate a file

Run `python translate.py -h` for more info. 

#### Using a single CPU / GPU:
```bash
accelerate launch translate.py \
--sentences_path sample_text/en.txt \
--output_path sample_text/en2es.translation.txt \
--source_lang en \
--target_lang es \
--model_name facebook/m2m100_1.2B
```

#### Multi-GPU:
See Accelerate documentation for more information (multi-node, TPU, Sharded model...): https://huggingface.co/docs/accelerate/index  
You can use the Accelerate CLI to configure the Accelerate environment (Run 
`accelerate config` in your terminal) instead of using the 
`--multi_gpu and --num_processes` flags.

```bash
accelerate launch --multi_gpu --num_processes 2 --num_machines 1 translate.py \
--sentences_path sample_text/en.txt \
--output_path sample_text/en2es.translation.txt \
--source_lang en \
--target_lang es \
--model_name facebook/m2m100_1.2B
```

#### Automatic batch size finder:
We will automatically find a batch size that fits in your GPU memory.
The default initial batch size is 128 (You can set it with the `--starting_batch_size 128` flag).
If we find an Out Of Memory error, we will automatically decrease the batch size until we find a working one.



#### Choose precision:
Use the `--precision` flag to choose the precision of the model. You can choose between: bf16, fp16 and 32. 

```bash
accelerate launch translate.py \
--sentences_path sample_text/en.txt \
--output_path sample_text/en2es.translation.txt \
--source_lang en \
--target_lang es \
--model_name facebook/m2m100_1.2B \
--precision fp16 
```

## Evaluate translations

Work in progress...