How To Translate Text In Python [Best 2 Ways APIs & Libraries]

by | Dec 19, 2022 | Artificial Intelligence, Machine Learning, Natural Language Processing

This guide covers how to translate text in Python. Machine translation is a prominent natural language processing (NLP) application that is not very straightforward.

We start by covering what is text translation. The advantages/disadvantages of text translation and the most common use cases. There are two main ways of implementing text translation in Python. We discuss both methods and provide code examples to help you get started.

What is text translation?

Machine translation automatically translates text or speech from one language to another using computer software. Machine translation is based on the use of algorithms and statistical models that are trained on large amounts of translated text data.

There are several different approaches to machine translation, including rule-based machine translation, statistical machine translation, and neural machine translation.

Rule-based machine translation (RBMT) relies on predefined rules to translate text. In contrast, statistical machine translation (SMT) uses statistical models to determine the most likely translation based on the input text and a large dataset. Finally, neural machine translation (NMT) uses artificial neural networks to learn to translate text. This is based on a large dataset of the translated text.

Text translation in Python has many different use cases

Machine translation is widely used in various applications.

Machine translation is widely used in various applications, including website localization, document translation, data analysis, machine learning, and customer service. However, machine translation is not always perfect and may produce errors or less accurate translations than human translation.

Advantages of text translation

There are several advantages to using machine translation:

  1. Speed: Machine translation is much faster than human translation, as it can quickly translate large volumes of text.
  2. Cost: Machine translation is typically less expensive than human translation, especially for large volumes of text.
  3. Consistency: Machine translation can ensure consistency in terminology and style, using a fixed set of rules to translate text.
  4. Availability: Machine translation is available 24/7 and can be accessed from anywhere with an internet connection.
  5. Improved access to information: Machine translation can make it easier for people to access information in languages they do not speak. Allowing them to understand and utilize information from a broader range of sources.

Keep in mind that machine translation is not always perfect. It may produce errors or less accurate translations than human translation. However, machine translation technology is constantly improving and may be suitable for various translation needs.

Disadvantages of text translation

There are several disadvantages to using machine translation:

  1. Inaccuracy: Machine translation is not always accurate. It may produce grammatically incorrect translations, contain errors, or not convey the intended meaning.
  2. Lack of context: Machine translation may need to consider the context in which the text is being used. This can lead to confusing or misleading translations.
  3. Lack of cultural sensitivity: Machine translation may need to translate idioms, slang, or cultural references. This can lead to translations that are inappropriate or offensive.
  4. Limited language support: Machine translation is not currently able to translate all languages and may not support languages that are not widely spoken or written.
  5. Dependence on technology: Machine translation relies on technology, which can be prone to errors or downtime.
  6. Limited human oversight: Machine translation is not consistently reviewed by a human translator. This can lead to errors or mistranslations going unnoticed.

Overall, it is vital to consider machine translation’s limitations and use it with caution, especially for critical or sensitive translations. In some cases, it may be more appropriate to use human translation to ensure the accuracy and appropriateness of the translation.

Use cases

There are many potential use cases for translating text in Python, including:

  1. Website localization: You can use Python to translate a website’s content into multiple languages. Making it more accessible to a broader audience.
  2. Document translation: You can use Python to translate large volumes of documents, such as legal contracts, technical manuals, or marketing materials.
  3. Data analysis: You can use Python to translate text data as part of a larger data analysis project. This allows you to work with text from multiple languages.
  4. Machine learning: You can use Python to translate text data as part of a machine learning project. This allows you to build models that can understand and process text in multiple languages.
  5. Social media analysis: You can use Python to translate text data from social media platforms. This allows you to analyze and understand the sentiment and content of posts in multiple languages.
  6. Customer service: You can use Python to translate customer inquiries or feedback in real-time, providing support in multiple languages.

How to translate text in Python

1. Use a language translation API

There are several APIs and libraries available that can be used to translate text in Python. Some popular options include Google Translate API, Microsoft Translator API, and Yandex Translate API. You must sign up for an API key and install the corresponding library to use one of these APIs. Here’s an example of how to use the Google Translate API to translate text from English to Spanish:

# Set the API key
api_key = "YOUR_API_KEY"

# Set the target language (in this case, Spanish)
target_language = "es"

# Set the text to be translated
text = "hello, world!"

# Create a client object
client = translate.Client(api_key=api_key)

# Call the translate method
translation = client.translate(text, target_language)

# Print the translated text
# Output: "hola, mundo!"


  1. Ease of use: Translation APIs are typically easy to use and can be integrated into a variety of applications and websites with minimal effort.
  2. Accuracy: Translation APIs often use advanced machine learning algorithms to provide highly accurate translations, especially for commonly used phrases and sentences.
  3. Speed: Translation APIs can provide instant translations, making them a convenient and efficient tool for on-demand translation needs.
  4. Cost: Translation APIs can be cost-effective, especially if you only need to translate a small amount of text or if you have a large volume of text to translate.


  1. Limited flexibility: Translation APIs often have limited customization options and may not be able to handle more complex or specialized translation tasks.
  2. Quality may vary: The quality of translations produced by a translation API may vary depending on the specific API and the language pairs it supports.
  3. Dependence on external service: Using a translation API requires an internet connection and reliance on an external service, which may not always be reliable.
  4. Potential privacy concerns: Some users may be concerned about the privacy implications of sending their text to an external translation API for processing.

2. Translate text in Python using libraries

Several machine translation libraries and tools can be used to translate text in Python, such as spaCy and Moses. These tools may require more setup and may not support as many languages as the APIs mentioned above.


  1. Customization: Libraries allow for greater customization and flexibility in the translation process, as they can be tailored to specific needs and requirements.
  2. Offline use: Libraries can be used offline, making them a convenient option for situations where an internet connection is unavailable.
  3. Control over the translation process: Using a library gives you more control over the translation process and allows you to fine-tune the translation to your specific needs.
  4. Performance: Libraries can often provide faster translations than online translation APIs, especially for larger volumes of text.


  1. Setup and maintenance: Setting up and maintaining a translation library can be time-consuming and require specialized technical knowledge.
  2. Cost: Libraries may require a one-time purchase or ongoing licensing fees, which can be a disadvantage for organizations with limited budgets.
  3. Limited language support: Libraries may only support a limited number of languages, whereas online translation APIs often support a wider range of language pairs.
  4. Limited updates: Libraries may not receive updates as frequently as online translation APIs, which can result in less accurate translations over time.

Libraries to translate text in Python

1. Translate text in Python with SpaCy

spaCy supports various languages, including English, Spanish, French, German, Chinese, and many others. You can find a complete list of the languages that spaCy support on the library’s documentation page.

To use spaCy to translate text to a particular language, you will need to have the appropriate language model installed on your system. You can install language models using the spacy command-line tool, as shown in the following example:

# To install the English language model
!python -m spacy download en_core_web_sm

# To install the Spanish language model
!python -m spacy download es_core_web_sm

Once you have installed the desired language models, you can use the spacy.load() function to load them into your Python script.

Sure! Here’s an example of how to use the spaCy library to translate text from English to Spanish:

# First, install and import the library
!pip install spacy
import spacy

# Load the language models
nlp_en = spacy.load("en_core_web_sm")
nlp_es = spacy.load("es_core_web_sm")

# Define the text to be translated
text = "hello, world!"

# Parse the text using the English language model
doc = nlp_en(text)

# Use the translate method to translate the text
translated_doc = doc.translate(to_lang="es")

# Print the translated text
print(translated_doc.text)  # Output: "hola, mundo!"

Remember that you must have the appropriate language models installed on your system to use this example. You can find more information about installing and using spaCy on the library’s documentation page.

2. Translate text in Python with Moses

To use the Moses library to translate text in Python, you will need to install the moses library and the moses translation server. Here’s an example of how to use Moses to translate text from English to Spanish:

# First, install the moses library and translation server
!pip install moses
!apt-get install -y moses
# Next, import the required libraries
from moses import MosesDetokenizer, MosesTokenizer

# Set the text to be translated
text = "hello, world!"

# Tokenize the text
tokenizer = MosesTokenizer()
tokens = tokenizer.tokenize(text)

# Translate the tokens using the translation server
translation = MosesDetokenizer().detokenize(tokens, 'es')

# Print the translated text
print(translation)  # Output: "hola, mundo!"

Remember that you must have the appropriate language models installed on the translation server to use this example. You can find more information about installing and using Moses on the library’s documentation page.

Key Takeaways

There are many advantages of automatic text translation but also several disadvantages. Whether or not you choose automated text translation often comes down to a speed/cost/accuracy analysis.

There are several excellent options when choosing the automated route. You could either use an external API or choose to use installable packages and libraries. Again, there is no best answer here, but the ultimate choice will depend on your use case and each approach’s pro/con analysis.

What approach have you ended up choosing? Let us know in the comments.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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