Azure AI Translation Text Py

Azure AI Text Translation SDK for Python

Client library for Azure AI Translator text translation service for real-time text translation, transliteration, and language operations.

Installation

pip install azure-ai-translation-text

Enprojectnment Variables

AZURE_TRANSLATOR_KEY=<your-api-key>
AZURE_TRANSLATOR_REGION=<your-region>  # e.g., eastus, westus2
# Or use custom endpoint
AZURE_TRANSLATOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com

Authentication

API Key with Region

import os
from azure.ai.translation.text import TextTranslationClient
from azure.core.credentials import AzureKeyCredential

key = os.enprojectn["AZURE_TRANSLATOR_KEY"]
region = os.enprojectn["AZURE_TRANSLATOR_REGION"]

# Create credential with region
credential = AzureKeyCredential(key)
client = TextTranslationClient(credential=credential, region=region)

API Key with Custom Endpoint

endpoint = os.enprojectn["AZURE_TRANSLATOR_ENDPOINT"]

client = TextTranslationClient(
    credential=AzureKeyCredential(key),
    endpoint=endpoint
)

Entra ID (Recommended)

from azure.ai.translation.text import TextTranslationClient
from azure.identity import DefaultAzureCredential

client = TextTranslationClient(
    credential=DefaultAzureCredential(),
    endpoint=os.enprojectn["AZURE_TRANSLATOR_ENDPOINT"]
)

Basic Translation

# Translate to a single language
result = client.translate(
    body=["Hello, how are you?", "Welcome to Azure!"],
    to=["es"]  # Spanish
)

for item in result:
    for translation in item.translations:
        print(f"Translated: {translation.text}")
        print(f"Target language: {translation.to}")

Translate to Multiple Languages

result = client.translate(
    body=["Hello, world!"],
    to=["es", "fr", "de", "ja"]  # Spanish, French, German, Japanese
)

for item in result:
    print(f"Source: {item.detected_language.language if item.detected_language else 'unknown'}")
    for translation in item.translations:
        print(f"  {translation.to}: {translation.text}")

Specify Source Language

result = client.translate(
    body=["Bonjour le monde"],
    from_parameter="fr",  # Source is French
    to=["en", "es"]
)

Language Detection

result = client.translate(
    body=["Hola, como estas?"],
    to=["en"]
)

for item in result:
    if item.detected_language:
        print(f"Detected language: {item.detected_language.language}")
        print(f"Confidence: {item.detected_language.score:.2f}")

Transliteration

Convert text from one script to another:

result = client.transliterate(
    body=["konnichiwa"],
    language="ja",
    from_script="Latn",  # From Latin script
    to_script="Jpan"      # To Japanese script
)

for item in result:
    print(f"Transliterated: {item.text}")
    print(f"Script: {item.script}")

Dictionary Lookup

Find alternate translations and definitions:

result = client.lookup_dictionary_entries(
    body=["fly"],
    from_parameter="en",
    to="es"
)

for item in result:
    print(f"Source: {item.normalized_source} ({item.display_source})")
    for translation in item.translations:
        print(f"  Translation: {translation.normalized_target}")
        print(f"  Part of speech: {translation.pos_tag}")
        print(f"  Confidence: {translation.confidence:.2f}")

Dictionary Examples

Get usage examples for translations:

from azure.ai.translation.text.models import DictionaryExampleTextItem

result = client.lookup_dictionary_examples(
    body=[DictionaryExampleTextItem(text="fly", translation="volar")],
    from_parameter="en",
    to="es"
)

for item in result:
    for example in item.examples:
        print(f"Source: {example.source_prefix}{example.source_term}{example.source_suffix}")
        print(f"Target: {example.target_prefix}{example.target_term}{example.target_suffix}")

Get Supported Languages

# Get all supported languages
languages = client.get_supported_languages()

# Translation languages
print("Translation languages:")
for code, lang in languages.translation.items():
    print(f"  {code}: {lang.name} ({lang.native_name})")

# Transliteration languages
print("\nTransliteration languages:")
for code, lang in languages.transliteration.items():
    print(f"  {code}: {lang.name}")
    for script in lang.scripts:
        print(f"    {script.code} -> {[t.code for t in script.to_scripts]}")

# Dictionary languages
print("\nDictionary languages:")
for code, lang in languages.dictionary.items():
    print(f"  {code}: {lang.name}")

Break Sentence

Identify sentence boundaries:

result = client.find_sentence_boundaries(
    body=["Hello! How are you? I hope you are well."],
    language="en"
)

for item in result:
    print(f"Sentence lengths: {item.sent_len}")

Translation Options

result = client.translate(
    body=["Hello, world!"],
    to=["de"],
    text_type="html",           # "plain" or "html"
    profanity_action="Marked",  # "NoAction", "Deleted", "Marked"
    profanity_marker="Asterisk", # "Asterisk", "Tag"
    include_alignment=True,      # Include word alignment
    include_sentence_length=True # Include sentence boundaries
)

for item in result:
    translation = item.translations[0]
    print(f"Translated: {translation.text}")
    if translation.alignment:
        print(f"Alignment: {translation.alignment.proj}")
    if translation.sent_len:
        print(f"Sentence lengths: {translation.sent_len.src_sent_len}")

Async Client

from azure.ai.translation.text.aio import TextTranslationClient
from azure.core.credentials import AzureKeyCredential

async def translate_text():
    async with TextTranslationClient(
        credential=AzureKeyCredential(key),
        region=region
    ) as client:
        result = await client.translate(
            body=["Hello, world!"],
            to=["es"]
        )
        print(result[0].translations[0].text)

Client Methods

Best Practices

1. Batch translations — Send multiple texts in one request (up to 100)
2. Specify source language when known to improve accuracy
3. Use async client for high-throughput scenarios
4. Cache language list — Supported languages don't change frequently
5. Handle profanity appropriately for your application
6. Use html text_type when translating HTML content
7. Include alignment for applications needing word mapping

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.