npx skills add ...
npx skills add microsoft/skills --skill azure-ai-translation-text-py
npx skills add microsoft/skills --skill azure-ai-translation-text-py
Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications. Triggers: "text translation", "translator", "translate text", "transliterate", "TextTranslationClient".
The same skill content is published under more than one repo. The install counts are split across them; any of these commands works.
Client library for Azure AI Translator text translation service for real-time text translation, transliteration, and language operations.
🔑 Two rules apply to every code sample below:
- Prefer
DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
- Local dev:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
- Sync:
with <Client>(...) as client:- Async:
async with <Client>(...) as client:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
New code should use DefaultAzureCredential above. The Translator service has two specifics that make API-key auth still common in existing deployments:
https://<resource>.cognitiveservices.azure.com). If you only have the global endpoint (https://api.cognitive.microsofttranslator.com), you must either provision a custom subdomain or stay on the key-based path until you do.Ocp-Apim-Subscription-Region header and is required whenever you use a multi-service or global Translator key.Convert text from one script to another:
Find alternate translations and definitions:
Get usage examples for translations:
Identify sentence boundaries:
| Method | Description |
|---|---|
translate | Translate text to one or more languages |
transliterate | Convert text between scripts |
detect | Detect language of text |
find_sentence_boundaries | Identify sentence boundaries |
lookup_dictionary_entries | Dictionary lookup for translations |
lookup_dictionary_examples | Get usage examples |
get_supported_languages | List supported languages |
azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.with Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.| File | Contents |
|---|---|
| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.ai.translation.text import TextTranslationClient
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
with TextTranslationClient(
endpoint=os.environ["AZURE_TRANSLATOR_ENDPOINT"],
credential=credential,
) as client:
result = client.translate(body=["Hello, world!"], to=["es"])import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.translation.text import TextTranslationClient
# Key + region against the global endpoint (most common keyed setup)
with TextTranslationClient(
credential=AzureKeyCredential(os.environ["AZURE_TRANSLATOR_KEY"]),
region=os.environ["AZURE_TRANSLATOR_REGION"],
) as client:
result = client.translate(body=["Hello, world!"], to=["es"])
# Key against a custom subdomain endpoint (no region required)
with TextTranslationClient(
endpoint=os.environ["AZURE_TRANSLATOR_ENDPOINT"],
credential=AzureKeyCredential(os.environ["AZURE_TRANSLATOR_KEY"]),
) as client:
result = client.translate(body=["Hello, world!"], to=["es"])# 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}")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}")result = client.translate(
body=["Bonjour le monde"],
from_parameter="fr", # Source is French
to=["en", "es"]
)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}")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}")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}")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 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}")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}")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}")from azure.ai.translation.text.aio import TextTranslationClient
from azure.identity.aio import DefaultAzureCredential
async def translate_text():
async with DefaultAzureCredential() as credential:
async with TextTranslationClient(
credential=credential,
endpoint=endpoint,
) as client:
result = await client.translate(
body=["Hello, world!"],
to=["es"]
)
print(result[0].translations[0].text)