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Machine Translation Python* Demo

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

How It Works

On startup the demo application reads command line parameters and loads a model to OpenVINO™ Runtime plugin.

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.

Preparing to Run

The list of models supported by the demo is in <omz_dir>/demos/machine_translation_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO IR format (*.xml + *.bin).

An example of using the Model Downloader:

omz_downloader --list models.lst

An example of using the Model Converter:

omz_converter --list models.lst

Supported Models

  • machine-translation-nar-de-en-0002
  • machine-translation-nar-en-de-0002
  • machine-translation-nar-en-ru-0002
  • machine-translation-nar-ru-en-0002

NOTE: Refer to the tables Intel's Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.

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 ...]]] [-d DEVICE]
                                   [-o OUTPUT] [--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 or path to the input
                        .txt file. Replaces console input.
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on; CPU or
                        GPU is acceptable. The demo
                        will look for a suitable plugin for device specified.
                        Default value is CPU.
   -o OUTPUT, --output OUTPUT
                        Optional. Path to the output .txt file.
  --output-name OUTPUT_NAME
                        Optional. Name of the models output node.

Required source and target tokenizer files located under tokenizer_src and tokenizer_tgt subfolders of corresponding model description folder. For example, to do inference on a CPU with the OpenVINO™ toolkit pre-trained models, run the following command:

python3 ./machine_translation_demo \
  -d CPU \
  -i "a sample english text" \
  -m <path_to_model>/machine-translation-nar-en-de-0002.xml \
  --tokenizer-src <omz_root>/models/intel/machine-translation-nar-en-de-0002/tokenizer_src \
  --tokenizer-tgt <omz_root>/models/intel/machine-translation-nar-en-de-0002/tokenizer_tgt

Demo Output

The application outputs translated sentences from source to target language. The demo reports

  • Latency: total processing time required to process input data (from reading the data to displaying the results).

See Also