llmtranslate

llmtranslate

Overview

llmtranslate is a Python library designed to identify the language of a given text and translate text between multiple languages using OpenAI's GPT-4o. The library is especially useful for translating text containing multiple languages into a single target language.

Features

• Language Detection: Identify the language of a given text in ISO 639-1 format.
• Translation: Translate text containing multiple languages into another language in ISO 639-1 format.

Documentation

Comprehensive documentation, including detailed usage information, is available at https://llm-translate.com

Requirements

To use this library, you must have an OpenAI API key. This key allows the library to utilize OpenAI's GPT-4o for translation and language detection.

Installation

You can install the llmtranslate library from PyPI:

pip install llmtranslate

Usage

Setting the OpenAI API Key

Before using llmtranslate with OpenAI, you need to set your OpenAI API key. You can do this by creating an instance of the TranslatorOpenAI class.

from llmtranslate import TranslatorOpenAI

# Set your OpenAI API key
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o-mini")

Language Detection

To detect the language of a given text:

from llmtranslate import TranslatorOpenAI

# Set your OpenAI API key
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o-mini")

# Detect language
detected_language = translator.get_text_language("Hello world")
if detected_language is not None:
    print(detected_language.ISO_639_1_code)  # Output: 'en'
    print(detected_language.ISO_639_2_code)  # Output: 'eng'
    print(detected_language.ISO_639_3_code)  # Output: 'eng'
    print(detected_language.language_name)  # Output: 'English'

[!IMPORTANT] If the translator does not detect any language, it will return None.
Before using results of translator detection you should check if it returned correct result or None

Translation

To translate text containing multiple languages into another language, you need to provide the ISO 639 language code for the target language. For a list of all ISO 639 language codes, you can refer to this ISO 639-1 code list website.

from llmtranslate import TranslatorOpenAI

# Set your OpenAI API key
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o-mini")

# Translate text
translated_text = translator.translate(
    text="Cześć jak się masz? Meu nome é Adam", 
    to_language="en"  # Use ISO 639-1 code for the target language
)
print(translated_text)  # Output: "Hello how are you? My name is Adam"

Full Example

Here is a complete example demonstrating how to use the library:

from llmtranslate import TranslatorOpenAI

# Initialize the translator with your OpenAI API key
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o-mini")

# Detect language
detected_language = translator.get_text_language("jak ty się nazywasz")
if detected_language is not None:
    print(detected_language.ISO_639_1_code)  # Output: 'pl'
    print(detected_language.ISO_639_2_code)  # Output: 'pol'
    print(detected_language.ISO_639_3_code)  # Output: 'pol'
    print(detected_language.language_name)  # Output 'Polish'

# Translate text
translated_text = translator.translate(
    text="Cześć jak się masz? Meu nome é Adam", 
    to_language="en"
)
print(translated_text)  # Output: "Hello how are you? My name is Adam"

Available OpenAI Models for Translation

The llmtranslate library provides access to various OpenAI models for translation. Below are the supported models and their use cases:

from llmtranslate import TranslatorOpenAI

# Recommended for precise translation, high-precision model
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o")

# A budget-friendly option, balancing cost and quality
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o-mini")

Using Asynchronous Methods

The llmtranslate library provides asynchronous methods to allow you to perform language detection and translation tasks efficiently in an async environment. If your application uses asyncio or another asynchronous framework, you can take full advantage of these async methods to avoid blocking your program while waiting for language detection or translation tasks to complete.

Example of Using Asynchronous Methods

The following example demonstrates how to use the async_get_text_language and async_translate_text methods:

import asyncio
from llmtranslate import TranslatorOpenAI

# Initialize the translator with your OpenAI API key
translator = TranslatorOpenAI(api_key="YOUR_OPENAI_API_KEY", model="gpt-4o-mini")


# Async function to detect language and translate text
async def detect_and_translate():
    # Detect language asynchronously
    detected_language = await translator.async_get_text_language("Hola, ¿cómo estás?")
    if detected_language is not None:
        print(detected_language.ISO_639_1_code)  # Output: 'es'
        print(detected_language.language_name)  # Output: 'Spanish'

    # Translate text asynchronously
    translated_text = await translator.async_translate(
        text="Cześć jak się masz? Meu nome é Adam",
        to_language="en"  # Use ISO 639-1 code for the target language
    )
    print(translated_text)  # Output: "Hello how are you? My name is Adam"


# Run the async function
asyncio.run(detect_and_translate())

Key Asynchronous Methods

1. async_get_text_language(text: str): This method detects the language of the provided text asynchronously.

   • Parameters:
     • text: The input text whose language needs to be detected.
   • Returns: A TextLanguage object containing the detected language's ISO 639-1, ISO 639-2, ISO 639-3 codes, and the language name.

   Example:

   detected_language = await translator.async_get_text_language("Hallo, wie geht's?")
   

2. async_translate_text(text: str, to_language: str): This method translates the input text asynchronously to the specified target language.

   • Parameters:
     • text: The input text to be translated.
     • to_language: The target language in ISO 639-1 code.
   • Returns: A string containing the translated text.

   Example:

   translated_text = await translator.async_translate("Bonjour tout le monde", "en")
   

Why Use Asynchronous Methods?

Using asynchronous methods allows your application to handle multiple tasks concurrently, improving efficiency, especially when dealing with large amounts of text or performing multiple translations simultaneously. This non-blocking behavior is ideal for web services, APIs, and any scenario requiring high responsiveness.

Running Asynchronous Functions

Remember that asynchronous methods must be called within an async function. To execute them, you can use asyncio.run() as shown in the examples above.

Setting the Azure OpenAI API Key

If you are using Azure's OpenAI services, you need to set your Azure OpenAI API key along with additional required parameters. Use the TranslatorAzureOpenAI class for this.

from llmtranslate import TranslatorAzureOpenAI

# Set your Azure OpenAI API key and related parameters
translator = TranslatorAzureOpenAI(
  azure_endpoint="YOUR_AZURE_ENDPOINT",
  api_key="YOUR_AZURE_API_KEY",
  api_version="YOUR_API_VERSION",
  azure_deployment="YOUR_AZURE_DEPLOYMENT"
)

Supported Languages

llmtranslate supports all languages supported by GPT-4o. For a complete list of language codes, you can visit the ISO 639-1 website.

Here is a table showing which languages are supported by gpt-4o and gpt4o-mini:

Language Name	Language Code	Supported by gpt-4o	Supported by gpt4o-mini
English	en	Yes	Yes
Mandarin Chinese	zh	Yes	Yes
Hindi	hi	Yes	Yes
Spanish	es	Yes	Yes
French	fr	Yes	Yes
German	de	Yes	Yes
Russian	ru	Yes	Yes
Arabic	ar	Yes	Yes
Italian	it	Yes	Yes
Korean	ko	Yes	Yes
Punjabi	pa	Yes	Yes
Bengali	bn	Yes	Yes
Portuguese	pt	Yes	Yes
Indonesian	id	Yes	Yes
Urdu	ur	Yes	Yes
Persian (Farsi)	fa	Yes	Yes
Vietnamese	vi	Yes	Yes
Polish	pl	Yes	Yes
Samoan	sm	Yes	Yes
Thai	th	Yes	Yes
Ukrainian	uk	Yes	Yes
Turkish	tr	Yes	Yes
Maori	mi	No	No
Norwegian	no	Yes	Yes
Dutch	nl	Yes	Yes
Greek	el	Yes	Yes
Romanian	ro	Yes	Yes
Swahili	sw	Yes	Yes
Hungarian	hu	Yes	Yes
Hebrew	he	Yes	Yes
Swedish	sv	Yes	Yes
Czech	cs	Yes	Yes
Finnish	fi	Yes	Yes
Amharic	am	No	No
Tagalog	tl	Yes	Yes
Burmese	my	Yes	Yes
Tamil	ta	Yes	Yes
Kannada	kn	Yes	Yes
Pashto	ps	Yes	Yes
Yoruba	yo	Yes	Yes
Malay	ms	Yes	Yes
Haitian Creole	ht	Yes	Yes
Nepali	ne	Yes	Yes
Sinhala	si	Yes	Yes
Catalan	ca	Yes	Yes
Malagasy	mg	Yes	Yes
Latvian	lv	Yes	Yes
Lithuanian	lt	Yes	Yes
Estonian	et	Yes	Yes
Somali	so	Yes	Yes
Tigrinya	ti	No	No
Breton	br	No	No
Fijian	fj	Yes	No
Maltese	mt	Yes	Yes
Corsican	co	Yes	Yes
Luxembourgish	lb	Yes	Yes
Occitan	oc	Yes	Yes
Welsh	cy	Yes	Yes
Albanian	sq	Yes	Yes
Macedonian	mk	Yes	Yes
Icelandic	is	Yes	Yes
Slovenian	sl	Yes	Yes
Galician	gl	Yes	Yes
Basque	eu	Yes	Yes
Azerbaijani	az	Yes	Yes
Uzbek	uz	Yes	Yes
Kazakh	kk	Yes	Yes
Mongolian	mn	Yes	Yes
Tibetan	bo	No	No
Khmer	km	Yes	No
Lao	lo	Yes	Yes
Telugu	te	Yes	Yes
Marathi	mr	Yes	Yes
Chichewa	ny	Yes	Yes
Esperanto	eo	Yes	Yes
Kurdish	ku	No	No
Tajik	tg	Yes	Yes
Xhosa	xh	Yes	No
Yiddish	yi	Yes	Yes
Zulu	zu	Yes	Yes
Sundanese	su	Yes	Yes
Tatar	tt	Yes	Yes
Quechua	qu	No	No
Uighur	ug	No	No
Wolof	wo	No	No
Tswana	tn	Yes	Yes

Additional Resources

• PyPI page
• ISO 639-1 Codes
• Github project repository
• Documentation

Authors

• Adam Pawełek
  • LinkedIn
  • Email

License

llmtranslate is licensed under the MIT License. See the LICENSE file for more details.