Machine Translation Python* Demo

This README describes the Machine Translation demo application that uses a non-autoregressive Transformer model for inference.

How It Works

Upon the start-up the demo application reads command line parameters and loads a network to Inference Engine.

The program provides an interactive CLI interface that gets a sentence in the source language as an input and returns its translation to the target language.

Running the Demo

Running the application with the -h option yields the following usage message:

usage: machine_translation_demo.py [-h] -m MODEL --tokenizer-src TOKENIZER_SRC
                                   --tokenizer-tgt TOKENIZER_TGT
                                   [-i [INPUT [INPUT ...]]]
                                   [--output-name OUTPUT_NAME]
 
optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Required. Path to an .xml file with a trained model
  --tokenizer-src TOKENIZER_SRC
                        Required. Path to the folder with src tokenizer that
                        contains vocab.json and merges.txt.
  --tokenizer-tgt TOKENIZER_TGT
                        Required. Path to the folder with tgt tokenizer that
                        contains vocab.json and merges.txt.
  -i [INPUT [INPUT ...]], --input [INPUT [INPUT ...]]
                        Optional. Text for translation. Replaces console input.
  --output-name OUTPUT_NAME
                        Optional. Name of the models output node.

To run the demo, you can use Intel's pretrained model. To download pretrained models, use the OpenVINO™ Model Downloader. The list of models supported by the demo is in the models.lst file in the demo's directory.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

See Also

• Using Open Model Zoo demos
• Model Optimizer
• Model Downloader