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使用在 Qualcomm NPU 上运行的神经机器翻译模型进行跨语言文本翻译。
curl http://localhost:8085/audio-analytics/v1/api/translations/models
| 模型 | 翻译方向 |
|---|---|
opus-en-es | 英语 → 西班牙语 |
opus-es-en | 西班牙语 → 英语 |
opus-en-zh | 英语 → 中文 |
opus-zh-en | 中文 → 英语 |
name 值。
| 方法 | 端点 | 描述 |
|---|---|---|
GET | /translations/models | 列出可用的翻译模型 |
POST | /translations/translate | 翻译文本 |
curl -X POST http://localhost:8085/audio-analytics/v1/api/translations/translate \
-H "Content-Type: application/json" \
-d '{
"text": ["Hello world", "How are you?"],
"model": "opus-en-es",
"source_language": "en",
"target_language": "es"
}'
text（必填）：要翻译的字符串数组model（必填）：翻译模型名称source_language（必填）：与所选模型匹配的源语言代码（例如 opus-en-es 对应 en）target_language（必填）：目标语言代码{
"translations": [
{
"translated_text": "Hola mundo",
"source_language": "en",
"target_language": "es"
},
{
"translated_text": "¿Cómo estás?",
"source_language": "en",
"target_language": "es"
}
]
}
curl -X POST http://localhost:8085/audio-analytics/v1/api/translations/translate \
-H "Content-Type: application/json" \
-d '{
"text": ["Hello world", "How are you?", "Good morning"],
"model": "opus-en-es",
"source_language": "en",
"target_language": "es"
}'
{
"translations": [
{"translated_text": "Hola mundo", "source_language": "en", "target_language": "es"},
{"translated_text": "¿Cómo estás?", "source_language": "en", "target_language": "es"},
{"translated_text": "Buenos días.", "source_language": "en", "target_language": "es"}
]
}
import requests
def translate(texts, model="opus-en-es", source_language="en", target_language="es"):
url = "http://localhost:8085/audio-analytics/v1/api/translations/translate"
response = requests.post(url, json={
"text": texts,
"model": model,
"source_language": source_language,
"target_language": target_language
}, timeout=30)
response.raise_for_status()
return [t["translated_text"] for t in response.json()["translations"]]
results = translate(["Hello world", "How are you?"])
print(results)
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