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: [-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