Machine translation demo

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This demo utilizes Intel’s pre-trained model that translates from English to German. More information about the model can be found here.

This model encodes sentences using the SentecePieceBPETokenizer from HuggingFace. The tokenizer vocabulary is downloaded automatically with the OMZ tool.

Inputs The model’s input is a sequence of up to 150 tokens with the following structure: <s> + tokenized sentence + <s> + <pad> (<pad> tokens pad the remaining blank spaces).

Output After the inference, we have a sequence of up to 200 tokens. The structure is the same as the one for the input.

Table of contents:

# # Install requirements
%pip install -q "openvino>=2023.1.0"
%pip install -q tokenizers
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Note: you may need to restart the kernel to use updated packages.
import time
import sys
import openvino as ov
import numpy as np
import itertools
from pathlib import Path
from tokenizers import SentencePieceBPETokenizer

sys.path.append("../utils")
from notebook_utils import download_file

Downloading model

The following command will download the model to the current directory. Make sure you have run pip install openvino-dev beforehand.

base_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1"
model_name = "machine-translation-nar-en-de-0002"
precision = "FP32"
model_base_dir = Path("model")
model_base_dir.mkdir(exist_ok=True)
model_path = model_base_dir / f"{model_name}.xml"
src_tok_dir = model_base_dir / "tokenizer_src"
target_tok_dir = model_base_dir / "tokenizer_tgt"
src_tok_dir.mkdir(exist_ok=True)
target_tok_dir.mkdir(exist_ok=True)

download_file(base_url + f'/{model_name}/{precision}/{model_name}.xml', f"{model_name}.xml", model_base_dir)
download_file(base_url + f'/{model_name}/{precision}/{model_name}.bin', f"{model_name}.bin", model_base_dir)
download_file(f"{base_url}/{model_name}/tokenizer_src/merges.txt", "merges.txt", src_tok_dir)
download_file(f"{base_url}/{model_name}/tokenizer_tgt/merges.txt", "merges.txt", target_tok_dir)
download_file(f"{base_url}/{model_name}/tokenizer_src/vocab.json", "vocab.json", src_tok_dir)
download_file(f"{base_url}/{model_name}/tokenizer_tgt/vocab.json", "vocab.json", target_tok_dir);
model/machine-translation-nar-en-de-0002.xml:   0%|          | 0.00/825k [00:00<?, ?B/s]
model/machine-translation-nar-en-de-0002.bin:   0%|          | 0.00/271M [00:00<?, ?B/s]
model/tokenizer_src/merges.txt:   0%|          | 0.00/311k [00:00<?, ?B/s]
model/tokenizer_tgt/merges.txt:   0%|          | 0.00/331k [00:00<?, ?B/s]
model/tokenizer_src/vocab.json:   0%|          | 0.00/523k [00:00<?, ?B/s]
model/tokenizer_tgt/vocab.json:   0%|          | 0.00/543k [00:00<?, ?B/s]

Load and configure the model

The model is now available in the model/ folder. Below, we load and configure its inputs and outputs.

core = ov.Core()
model = core.read_model(model_path)
input_name = "tokens"
output_name = "pred"
model.output(output_name)
max_tokens = model.input(input_name).shape[1]

Select inference device

select device from dropdown list for running inference using OpenVINO

import ipywidgets as widgets

core = ov.Core()

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value='AUTO',
    description='Device:',
    disabled=False,
)

device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
compiled_model = core.compile_model(model, device.value)

Load tokenizers

NLP models usually take a list of tokens as standard input. A token is a single word converted to some integer. To provide the proper input, we need the vocabulary for such mapping. We use merges.txt to find out what sequences of letters form a token. vocab.json specifies the mapping between tokens and integers.

The input needs to be transformed into a token sequence the model understands, and the output must be transformed into a sentence that is human readable.

Initialize the tokenizer for the input src_tokenizer and the output tgt_tokenizer.

src_tokenizer = SentencePieceBPETokenizer.from_file(
    str(src_tok_dir / 'vocab.json'),
    str(src_tok_dir / 'merges.txt')
)
tgt_tokenizer = SentencePieceBPETokenizer.from_file(
    str(target_tok_dir / 'vocab.json'),
    str(target_tok_dir / 'merges.txt')
)

Perform translation

The following function translates a sentence in English to German.

def translate(sentence: str) -> str:
    """
    Tokenize the sentence using the downloaded tokenizer and run the model,
    whose output is decoded into a human readable string.

    :param sentence: a string containing the phrase to be translated
    :return: the translated string
    """
    # Remove leading and trailing white spaces
    sentence = sentence.strip()
    assert len(sentence) > 0
    tokens = src_tokenizer.encode(sentence).ids
    # Transform the tokenized sentence into the model's input format
    tokens = [src_tokenizer.token_to_id('<s>')] + \
        tokens + [src_tokenizer.token_to_id('</s>')]
    pad_length = max_tokens - len(tokens)

    # If the sentence size is less than the maximum allowed tokens,
    # fill the remaining tokens with '<pad>'.
    if pad_length > 0:
        tokens = tokens + [src_tokenizer.token_to_id('<pad>')] * pad_length
    assert len(tokens) == max_tokens, "input sentence is too long"
    encoded_sentence = np.array(tokens).reshape(1, -1)

    # Perform inference
    enc_translated = compiled_model({input_name: encoded_sentence})
    output_key = compiled_model.output(output_name)
    enc_translated = enc_translated[output_key][0]

    # Decode the sentence
    sentence = tgt_tokenizer.decode(enc_translated)

    # Remove <pad> tokens, as well as '<s>' and '</s>' tokens which mark the
    # beginning and ending of the sentence.
    for s in ['</s>', '<s>', '<pad>']:
        sentence = sentence.replace(s, '')

    # Transform sentence into lower case and join words by a white space
    sentence = sentence.lower().split()
    sentence = " ".join(key for key, _ in itertools.groupby(sentence))
    return sentence

Translate the sentence

The following function is a basic loop that translates sentences.

def run_translator():
    """
    Run the translation in real time, reading the input from the user.
    This function prints the translated sentence and the time
    spent during inference.
    :return:
    """
    while True:
        input_sentence = input()
        if input_sentence == "":
            break

        start_time = time.perf_counter()
        translated = translate(input_sentence)
        end_time = time.perf_counter()
        print(f'Translated: {translated}')
        print(f'Time: {end_time - start_time:.2f}s')
# uncomment the following line for a real time translation of your input
# run_translator()

Test your translation

Run the following cell with an English sentence to have it translated to German

sentence = "My name is openvino"
print(f'Translated: {translate(sentence)}')
Translated: mein name ist openvino.