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Using Oracle’s Machine Translation Services for NLP Analysis

Ignacio walks you through Oracle MTS, a translation service, using Python and even provides a benchmarking function to test its speed.

Author: Ignacio Martínez



Welcome! This article is an overview of Oracle’s Machine Translation Services (MTS) and how they complement the standard open-source NLP libraries out there today. Currently, this service is focused / created with the intention of helping Oracle internal teams translate texts in a secure way (see below), so this is not a readily available product. However, there are plans for the future to make it available for OCI users.

Oracle Translate is an MTS made available by Oracle’s International Product Solutions. It’s beneficial to use Oracle’s service over others, especially for sensitive information, because other services, such as Google Translate, are known to harvest and collect requesting data which could infringe on company confidentiality and chain of custody of protected documents. Oracle’s service does not store this information, and all transactions happen in a secure Oracle network environment.

Let’s explore some examples of how Oracle’s MT services are different from other translation services like Google’s.

If you don’t yet have an OCI account, you can quickly sign up for one today by registering for an Oracle Cloud Free Tier account.

Afterwards, check for even more Python content.


Authentication is performed against Oracle Cloud resources and handled by Oracle’s Identity Cloud Service (IDCS), using base 64 encoding.

The two main components of Oracle Translate are a client ID and a client secret. Using these components, we can perform authentication following this flow:

# Defining variables
                    IDCSSERVER = '
                                    SCOPE = 'urn:opc:idm:__myscopes__'
                                    # Production endpoint
                                    BASEURL = '<>'
                                    ENDPOINT = '{}/translation/api'.format(BASEURL)
                                    REALTIME_ENDPOINT = '<>'
                                    NLP_ENDPOINT = '<>'
                                    data = load_config_file()
                                    basic_authorization = '{}:{}'.format(
                                    data['mt_translation']['MT_CLIENT_ID'], data['mt_translation']['MT_CLIENT_SECRET']
                                    basic_authorization = base64.b64encode(basic_authorization.encode('ascii')).decode('ascii')
                                    request_url = '{}/oauth2/v1/token/'.format(IDCSSERVER)
                                    request_headers = {
                                    'Authorization': 'Basic {}'.format(basic_authorization),
                                    'content-type': 'application/x-www-form-urlencoded;charset=UTF-8',
                                    request_data = 'grant_type=client_credentials&scope={}'.format(SCOPE)
                                    response =, data=request_data, headers=request_headers)
                                    api_token = response.json().get('access_token')

Our primary objective is to obtain the necessary API token to perform operations. Note that the config.yaml file mentioned to obtain the client ID and secret should follow this hierarchical structure:

                      MT_CLIENT_ID: XXXX
                      MT_CLIENT_SECRET: XXXX

Once we have the API token, we can perform authenticated requests against Oracle’s MT Translation service.

Batch Translation

In this case, we will translate a document in Markdown format this one as an example.

The batch translation endpoint supports translating whole documents instead of making several requests. Also, requests can be submitted for multiple target languages at a time, making it possible that one request is translatable to every available language supported by the API with just one call.

def batch_translation(api_token, file_path, file_name):
                        request_url = '{}/files?service={}&sourceLang={}&scope={}'.format(ENDPOINT, 'mt', args.source_language_code, args.target_language_code)
                        request_headers = {
                            'Authorization': 'Bearer {}'.format(api_token)
                        #print(request_url, request_headers)
                    	files = {
                            'file': (file_name, open(file_path, 'rb'))
                        print('Request URL: {}'.format(request_url))
                        print('Request headers: {}'.format(request_headers))
                        print('Request file: {}'.format(dict(file=files['file'])))
                        response =, headers=request_headers, files=dict(file=files['file']))
                        response_json = response.json()
                        print(response.status_code, response.json())
                        drop = None
                        while drop is None:
                            print('Waiting for translated file to be ready...')
                                response = requests.get(response_json.get('pipeline').get('status'))
                            except Exception as e:
                                drop = response.json().get('map').get('en_drop') # varies with language. I used 'en' as English in this example.
                    			if drop is not None:
                                    print('File result in {}'.format(drop))
                            except KeyError:
                                print('Could not find the drop.')

After executing and periodically waiting for the resulting file to be ready, we get our processed data in about 40 seconds:

Request URL: <>
                    Request file: {'file': ('', <_io.BufferedReader name='../data/'>)}
                    202 {'id': 115162, 'status': 'CREATED', 'service': 'mt', 'source': {'originalPath': '', 'language': 'en'}, 'scope': ['fr'], 'otp_instance_id': 343337, '_links': {'self': '<>', 'otp_status': '<>', 'otp_wordcount': '<>'}, 'pipeline': {'id': 343337, 'status': '<>', 'wordcount': '<>'}}
                    Waiting for translated file to be ready...
                    {'id': 343337, 'state': 'RUNNING', 'pipeline_id': 1, 'start': '2021-11-17T00:24:57Z', 'update_time': '2021-11-17T00:24:57Z', 'end': '', 'running_time_str': '1s', 'updated_ago_in_str': '1s', 'progress': {'preprocessing': 0, 'translation': 0, 'merge_and_delivery': 0}, 'map': {'translated_langs': [], 'deferred_langs': [], 'originalFileName': ''}, 'messages': []}
                    Waiting for translated file to be ready...
                    {'id': 343337, 'state': 'RUNNING', 'pipeline_id': 1, 'start': '2021-11-17T00:24:57Z', 'update_time': '2021-11-17T00:24:57Z', 'end': '', 'running_time_str': '12s', 'updated_ago_in_str': '12s', 'progress': {'preprocessing': 0, 'translation': 0, 'merge_and_delivery': 0}, 'map': {'translated_langs': [], 'deferred_langs': [], 'originalFileName': ''}, 'messages': []}
                    Waiting for translated file to be ready...
                    {'id': 343337, 'state': 'RUNNING', 'pipeline_id': 1, 'start': '2021-11-17T00:24:57Z', 'update_time': '2021-11-17T00:24:57Z', 'end': '', 'running_time_str': '23s', 'updated_ago_in_str': '23s', 'progress': {'preprocessing': 0, 'translation': 0, 'merge_and_delivery': 0}, 'map': {'translated_langs': [], 'deferred_langs': [], 'originalFileName': ''}, 'messages': []}
                    Waiting for translated file to be ready...
                    {'id': 343337, 'state': 'RUNNING', 'pipeline_id': 1, 'start': '2021-11-17T00:24:57Z', 'update_time': '2021-11-17T00:25:21Z', 'end': '', 'running_time_str': '34s', 'updated_ago_in_str': '10s', 'progress': {'preprocessing': 100, 'translation': 1, 'merge_and_delivery': 0}, 'map': {'languages': 1, 'translated_langs': [], 'deferred_langs': [], 'en_drop': '<>/', 'originalFileName': ''}, 'messages': []}
                    File result in <>/

And with the final result we can download the .zip file with our translated file inside the folder.

Real-Time Translation

In this case, we’re going to test with a few examples on the real-time translation endpoint. The list of available languages is defined as:

                    LANGUAGES = [
                        'en', 'fr-CA', 'pl', 'sv', 'ar', 'de', 'ro', 'zh-TW', 'pt-BR', 'it', 'ru', 'nl', 'ja', 'zh-CN', 'fr', 'ko', 'es-ww'
                    # Coming in FY22Q3: Norwegian, Danish, Czech, Finish, Turkish & in FY22Q4: Greek, Hebrew, Thai, Ukrainian, Croatian 

We predefine our languages in a list and only allow these language codes as input:

import argparse
                    parser = argparse.ArgumentParser()
                    parser.add_argument('-s', '--source-language-code', choices=LANGUAGES, required=True)
                    parser.add_argument('-t', '--target-language-code', choices=LANGUAGES, required=True)
                    args = parser.parse_args() 

Let’s define our real-time endpoint as follows:

def real_time_translation(api_token, data):
                        assert type(data) == type(str())
                        request_url = '{}/translate/{}/{}?s={}'.format(REALTIME_ENDPOINT, args.source_language_code, args.target_language_code, urllib.parse.quote(data))
                        request_headers = {
                            'Authorization': 'Bearer {}'.format(api_token)
                        response = requests.get(request_url, headers=request_headers)
                        print(response.status_code, response.content)

Note that we quote our string to avoid inconsistencies with path parameters in the request URL. After executing this code in this fashion:

python --source-language-code "en" --target-language-code "fr"

We see this result:

                    200 b"C'est un exemple"

We were able to translate a request in about 1.5 seconds. I created a benchmark function to measure the average request/response time:

import time
                    def benchmark():
                        api_token = get_access_token
                        for x in range(500):
                            t1 = time.time()
                            real_time_translation(api_token, text)
                            print('[BENCHMARK] +{}'.format(time.time() - t1))

Which produced these results:

[BENCHMARK] +1.5139455795288086
                    [BENCHMARK] +1.480928897857666 
                    [BENCHMARK] +1.4764995574951172
                    [BENCHMARK] +1.5293617248535156
                    [BENCHMARK] +1.4901740550994873
                    [BENCHMARK] +1.5180373191833496
                    [BENCHMARK] +1.489206314086914 
                    [BENCHMARK] +1.4964230060577393

Each request takes about an average of 1.5 seconds to finish.

Docker setup

I’ve included a Docker file that only requires a config.yaml file to run. To download the Docker file, find it in this GitHub directory.

The contents of config.yaml should be like this:

                      MT_SERVICE: mt

Build the image:

docker build --pull --rm -f "nlp-oracle-translation\oracle\Dockerfile" -t oracle_translate "nlp-oracle-translation\oracle"

And run it:

docker run -it -p 443:443 oracle_translate  -s en -t fr -x "This is an example"

Example run:

λ docker run -it -p 443:443 oracle_translate  -s en -t fr -x "I love you"
                    200 Je vous aime !

How can I get started on OCI?

Remember that you can always sign up for free with OCI! Your Oracle Cloud account provides a number of Always Free services and a Free Trial with US$300 of free credit to use on all eligible OCI services for up to 30 days. These Always Free services are available for an unlimited period of time. The Free Trial services may be used until your US$300 of free credits are consumed or the 30 days has expired, whichever comes first. You can sign up here for free.


Copyright (c) 2021 Oracle and/or its affiliates.

Licensed under the Universal Permissive License (UPL), Version 1.0.

See LICENSE for more details.

Written by Ignacio Guillermo Martínez @jasperan, edited by GreatGhostsss