Azure AI Translation Text Py

Azure AI Text Translation SDK for Python

Azure AI Translator 文本翻译服务的客户端库，用于实时文本翻译、音译和语言操作。

安装

pip install azure-ai-translation-text

环境变量

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

身份验证

API 密钥与区域

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

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

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

API 密钥与自定义端点

endpoint = os.environ["AZURE_TRANSLATOR_ENDPOINT"]

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

Entra ID（推荐）

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

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

基础翻译

# 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}")

翻译为多种语言

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}")

指定源语言

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

语言检测

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}")

音译

将文本从一种脚本转换为另一种脚本：

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}")

字典查询

查找替代翻译和定义：

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}")

字典示例

获取翻译的使用示例：

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}")

获取支持的语言

# 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}")

断句

识别句子边界：

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}")

翻译选项

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}")

异步客户端

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)

客户端方法

最佳实践

1. 批量翻译 — 在一个请求中发送多个文本（最多 100 个）
2. 指定源语言 — 已知时指定源语言以提高准确性
3. 使用异步客户端 — 适用于高吞吐量场景
4. 缓存语言列表 — 支持的语言不常变化
5. 适当处理敏感内容 — 根据应用程序需求处理
6. 翻译 HTML 内容时使用 html 文本类型
7. 包含对齐信息 — 用于需要词汇映射的应用程序

适用场景

此技能适用于执行概述中描述的工作流或操作。

限制

• 仅当任务明确符合上述范围时使用此技能。
• 不要将输出视为环境特定验证、测试或专家审查的替代品。
• 如果缺少必需的输入、权限、安全边界或成功标准，请停止并请求澄清。