111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
"""LLM 翻译节点入口。
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通过标准节点 HTTP 服务接收 SRT，提取纯文本行分批调用 LLM，再把译文回填到
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原 SRT 结构并输出 cn.srt。
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"""
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from __future__ import annotations
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import json
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import os
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import urllib.error
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import urllib.request
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from pathlib import Path
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from wov_sdk.models import InvokeRequest, InvokeResponse, NodeManifest
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from wov_sdk.server import run_node
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# 单次 LLM 请求携带的字幕行数；过大会超出模型上下文，过小则请求次数过多。
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CHUNK_SIZE = 20
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def translate_lines(lines: list[str], params: dict) -> list[str]:
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"""分批调用 LLM 翻译纯文本行，返回顺序一致的译文列表。"""
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# 接口地址、Key 和模型均可通过环境变量配置，默认指向内网兼容接口。
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api_base = os.getenv(
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"LLM_API_BASE",
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"http://192.168.123.70:8080/v1/chat/completions",
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)
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api_key = os.getenv("LLM_API_KEY", "")
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# 单次请求超时可配置，长文本翻译场景下需要放宽。
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request_timeout = float(os.getenv("LLM_TIMEOUT_SECONDS", "600"))
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model = str(params.get("model") or os.getenv("LLM_MODEL", "default"))
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target_language = str(params.get("target_language", "zh-CN"))
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# 系统提示词约束模型只输出译文，保证行数和顺序可回填。
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system_prompt = (
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"你是专业字幕翻译。将用户提供的日文字幕翻译为"
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f"{target_language}。只返回译文，保持行数和顺序，不要添加解释。"
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)
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translated: list[str] = []
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# 按 CHUNK_SIZE 分批发送，避免单次请求超过模型上下文限制。
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for start in range(0, len(lines), CHUNK_SIZE):
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chunk = lines[start : start + CHUNK_SIZE]
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body = {
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "\n".join(chunk)},
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],
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}
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headers = {"Content-Type": "application/json"}
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# 配置了 Key 时附带 Bearer 鉴权头。
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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request = urllib.request.Request(
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api_base,
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data=json.dumps(body).encode("utf-8"),
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headers=headers,
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method="POST",
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)
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with urllib.request.urlopen(request, timeout=request_timeout) as response:
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payload = json.loads(response.read().decode("utf-8"))
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# 兼容 OpenAI Chat Completions 响应格式，取第一条消息内容。
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content = payload["choices"][0]["message"]["content"]
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# 忽略空行，保证译文列表与输入行一一对应。
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translated.extend(
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[line.strip() for line in content.splitlines() if line.strip()]
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)
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return translated
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def invoke(request: InvokeRequest) -> InvokeResponse:
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"""翻译 SRT 文件中的字幕文本，输出 cn.srt。"""
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srt_uri = request.inputs.get("srt_uri")
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if not srt_uri:
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return InvokeResponse(status="failed", error="srt_uri is required")
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srt_path = Path(srt_uri)
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if not srt_path.is_file():
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return InvokeResponse(status="failed", error="srt file not found")
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# 标准 SRT 每 4 行一组：序号、时间轴、文本、空行；文本位于第 3 行。
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lines = srt_path.read_text(encoding="utf-8").splitlines()
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text_indices = list(range(2, len(lines), 4))
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source_lines = [lines[index] for index in text_indices]
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translated_lines = translate_lines(source_lines, request.params)
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# 防止模型返回行数偏差：多出的截断，缺少的用空串补齐。
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translated_lines = translated_lines[: len(source_lines)]
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translated_lines += [""] * max(0, len(source_lines) - len(translated_lines))
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# 只替换文本行，序号、时间轴和空行保持不变。
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for index, text_index in enumerate(text_indices):
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lines[text_index] = translated_lines[index]
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output_dir = Path(request.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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output_path = output_dir / "cn.srt"
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# 末尾补一个换行，让文件满足常见文本工具习惯。
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output_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
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return InvokeResponse(status="completed", outputs={"cn_srt_uri": str(output_path)})
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def main() -> None:
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"""加载节点清单并以本模块的 invoke 处理器启动服务。"""
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manifest_path = Path(__file__).resolve().parent.parent / "node.manifest.json"
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with open(manifest_path, "r", encoding="utf-8") as f:
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manifest = NodeManifest.from_dict(json.load(f))
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run_node(manifest, invoke)
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if __name__ == "__main__":
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main()
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