Azure AI Document Translation SDK for Python

Client library for Azure AI Translator document translation service for batch document translation with format preservation.

Installation

pip install azure-ai-translation-document

Environment Variables

AZURE_DOCUMENT_TRANSLATION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
AZURE_DOCUMENT_TRANSLATION_KEY=<your-api-key>  # If using API key

# Storage for source and target documents
AZURE_SOURCE_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas>
AZURE_TARGET_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas>

Authentication

API Key

import os
from azure.ai.translation.document import DocumentTranslationClient
from azure.core.credentials import AzureKeyCredential

endpoint = os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"]
key = os.environ["AZURE_DOCUMENT_TRANSLATION_KEY"]

client = DocumentTranslationClient(endpoint, AzureKeyCredential(key))

Entra ID (Recommended)

from azure.ai.translation.document import DocumentTranslationClient
from azure.identity import DefaultAzureCredential

client = DocumentTranslationClient(
    endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Disclosure and cost boundary

Translation sends documents to Azure and writes output to the selected containers. Confirm authorized document subset, destination, region, retention and budget. Keep SAS URLs out of logs, use short-lived least-privilege access and handle each document’s failure separately; overall job completion does not imply all documents succeeded. SDK/single-document methods and language/format support are version-dependent: inspect current package docs before running fragments.

Basic Document Translation

from azure.ai.translation.document import DocumentTranslationInput, TranslationTarget

source_url = os.environ["AZURE_SOURCE_CONTAINER_URL"]
target_url = os.environ["AZURE_TARGET_CONTAINER_URL"]

# Start translation job
poller = client.begin_translation(
    inputs=[
        DocumentTranslationInput(
            source_url=source_url,
            targets=[
                TranslationTarget(
                    target_url=target_url,
                    language="es"  # Translate to Spanish
                )
            ]
        )
    ]
)

# Wait for completion
result = poller.result()

print(f"Status: {poller.status()}")
print(f"Documents translated: {poller.details.documents_succeeded_count}")
print(f"Documents failed: {poller.details.documents_failed_count}")

Multiple Target Languages

poller = client.begin_translation(
    inputs=[
        DocumentTranslationInput(
            source_url=source_url,
            targets=[
                TranslationTarget(target_url=target_url_es, language="es"),
                TranslationTarget(target_url=target_url_fr, language="fr"),
                TranslationTarget(target_url=target_url_de, language="de")
            ]
        )
    ]
)

Translate Single Document

from azure.ai.translation.document import SingleDocumentTranslationClient

single_client = SingleDocumentTranslationClient(endpoint, AzureKeyCredential(key))

with open("document.docx", "rb") as f:
    document_content = f.read()

result = single_client.translate(
    body=document_content,
    target_language="es",
    content_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
)

# Save translated document
with open("document_es.docx", "wb") as f:
    f.write(result)

Check Translation Status

# Get all translation operations
operations = client.list_translation_statuses()

for op in operations:
    print(f"Operation ID: {op.id}")
    print(f"Status: {op.status}")
    print(f"Created: {op.created_on}")
    print(f"Total documents: {op.documents_total_count}")
    print(f"Succeeded: {op.documents_succeeded_count}")
    print(f"Failed: {op.documents_failed_count}")

List Document Statuses

# Get status of individual documents in a job
operation_id = poller.id
document_statuses = client.list_document_statuses(operation_id)

for doc in document_statuses:
    print(f"Document ID: {doc.id}")  # Avoid logging SAS-bearing source URLs.
    print(f"  Status: {doc.status}")
    print(f"  Translated to: {doc.translated_to}")
    if doc.error:
        print(f"  Error: {doc.error.message}")

Cancel Translation

# Cancel a running translation
client.cancel_translation(operation_id)

Using Glossary

from azure.ai.translation.document import TranslationGlossary

poller = client.begin_translation(
    inputs=[
        DocumentTranslationInput(
            source_url=source_url,
            targets=[
                TranslationTarget(
                    target_url=target_url,
                    language="es",
                    glossaries=[
                        T