Practical overview
When it fits
Use Easy Translate when file-based translation, command-line control and model choice matter more than a hosted point-and-click workflow.
Who it is for
- People who need to translate large text files from a command line.
- Developers or technically confident users who want to select translation models and configure compute options.
Documented uses
- Translate a text file by specifying its path, output path, languages and model.
- Process files in a directory, optionally filtering by file extension.
- Choose among supported multilingual translation models or other Hugging Face sequence-to-sequence and causal language models.
- Use the separate translation-evaluation script referenced by the README.
Requirements & limits
- The primary workflow is a Python CLI, with input and output paths and language and model arguments.
- The project uses Hugging Face Transformers and Accelerate; the README lists minimum versions for PyTorch and Accelerate.
- NLLB200 and SeamlessM4T have higher Transformers minimum versions in the README than the general requirement.
- BitsAndBytes and PEFT are optional dependencies for quantization and LoRA model loading respectively.
- The README describes multiple compute and precision options, but suitability depends on the user's environment and selected model.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Translate a text file from the command line
Use the documented single-file workflow to translate text with M2M100.
Before you start
- Python environment with PyTorch 1.10.0 or later.
- Accelerate 0.12.0 or later and Hugging Face Transformers.
- An input text file and the Easy Translate repository.
Steps
Install the documented dependencies, following the PyTorch installation instructions for the environment.
Run the command below to view the script options.
python translate.py -hRun the command below, adapting paths and language codes for your files.
python3 translate.py --sentences_path sample_text/en.txt --output_path sample_text/en2es.translation.m2m100_1.2B.txt --source_lang en --target_lang es --model_name facebook/m2m100_1.2B
Expected outcome
The command specifies an output path for an English-to-Spanish translation using M2M100.
Guide limitations
- The example depends on the input path and model being available in the user's environment.
- The README does not establish resource needs or performance for a particular machine.
Translate matching files in a directory
Configure the documented directory workflow to process text files with M2M100.
Before you start
- Python environment with PyTorch 1.10.0 or later.
- Accelerate 0.12.0 or later and Hugging Face Transformers.
- A directory of input files and the Easy Translate repository.
Steps
Install the documented dependencies.
Run the command below to view the script options.
python translate.py -hUse
--sentences_dirinstead of--sentences_path, set--files_extension txt, and specify an output directory, source and target languages, and model; the README example usessample_text/,sample_text/translations, English, Spanish, andfacebook/m2m100_1.2B.
Expected outcome
The command is configured to translate files with the selected extension into the specified output directory.
Guide limitations
- The example documents selection of
.txtfiles; the README notes an empty extension value selects all files in the directory. - The README does not establish resource needs or performance for a particular machine.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- ikergarcia1996/Easy-Translate · Official homepage title · retained in its source language