mirror of
https://github.com/linyqh/NarratoAI.git
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feat: 添加LLM驱动的字幕翻译功能
新增配置项以配置字幕翻译批次大小和最大并发数 补充中、英文国际化文案支持翻译相关界面 实现核心字幕翻译服务，支持批量处理与并发执行 添加WebUI界面用于触发和监控字幕翻译任务 新增完整单元测试覆盖翻译功能全流程
This commit is contained in:
viccy
parent
b9f07a6a10
commit
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370
app/services/subtitle_translator.py
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370
app/services/subtitle_translator.py
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"""LLM-powered SRT subtitle translation."""
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from __future__ import annotations
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import json
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import os
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from math import ceil
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from typing import Any, Callable
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from loguru import logger
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from app.config import config
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from app.services.llm.migration_adapter import _run_async_safely
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from app.services.llm.unified_service import UnifiedLLMService
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from app.services.subtitle_corrector import (
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_ensure_llm_providers_registered,
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_extract_json_text,
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parse_srt_blocks,
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)
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from app.services.subtitle_text import read_subtitle_text
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from app.utils import utils
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class SubtitleTranslationError(RuntimeError):
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"""Raised when subtitle translation cannot produce a valid SRT."""
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DEFAULT_BATCH_SIZE = 20
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DEFAULT_MAX_WORKERS = 3
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DEFAULT_MAX_REPAIR_ATTEMPTS = 3
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TranslationProgressCallback = Callable[[int, int, str], None]
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def _get_positive_int(value, default: int, *, minimum: int = 1, maximum: int | None = None) -> int:
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try:
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parsed = int(value)
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except (TypeError, ValueError):
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parsed = default
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parsed = max(minimum, parsed)
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if maximum is not None:
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parsed = min(maximum, parsed)
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return parsed
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def _resolve_batch_size(batch_size: int | None = None) -> int:
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if batch_size is None:
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batch_size = config.app.get("subtitle_translate_batch_size", DEFAULT_BATCH_SIZE)
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return _get_positive_int(batch_size, DEFAULT_BATCH_SIZE, minimum=1, maximum=200)
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def _resolve_max_workers(max_workers: int | None = None) -> int:
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if max_workers is None:
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max_workers = config.app.get("subtitle_translate_max_workers", DEFAULT_MAX_WORKERS)
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return _get_positive_int(max_workers, DEFAULT_MAX_WORKERS, minimum=1, maximum=8)
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def _split_blocks(blocks, batch_size: int):
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return [blocks[index:index + batch_size] for index in range(0, len(blocks), batch_size)]
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def _build_translation_prompt(blocks, target_language: str) -> str:
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payload = {str(block.order): block.text for block in blocks}
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return f"""
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请将以下 SRT 字幕文本翻译为{target_language}。
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翻译要求：
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1. 结合全部字幕内容理解语境，输出自然、准确、适合字幕阅读的{target_language}。
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2. 只翻译字幕文本，不要修改时间轴、序号、条目数量或条目顺序。
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3. 保留必要的说话人标记、专有名词、品牌名、代码、数字和换行；除非目标语言中有约定译名。
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4. 不要添加解释、注释、剧情信息或 Markdown。
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5. 空字幕文本保持为空字符串。
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只输出严格 JSON 对象，不要输出 Markdown 或解释文字。必须保留所有输入 key，格式必须为：
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{{"1":"翻译后的字幕文本","2":"翻译后的字幕文本"}}
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待翻译字幕条目：
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{json.dumps(payload, ensure_ascii=False, indent=2)}
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""".strip()
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def _parse_translations(raw_output: str, expected_ids: set[int]) -> dict[int, str]:
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json_text = _extract_json_text(raw_output)
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try:
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data: Any = json.loads(json_text)
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except json.JSONDecodeError as exc:
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raise SubtitleTranslationError("LLM 未返回有效 JSON 字幕翻译结果") from exc
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if isinstance(data, dict) and "items" in data:
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items = data["items"]
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elif isinstance(data, list):
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items = data
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elif isinstance(data, dict):
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items = [{"id": key, "text": value} for key, value in data.items()]
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else:
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raise SubtitleTranslationError("LLM 字幕翻译结果格式无效")
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if not isinstance(items, list):
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raise SubtitleTranslationError("LLM 字幕翻译结果缺少 items 列表")
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translations: dict[int, str] = {}
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for item in items:
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if not isinstance(item, dict):
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continue
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try:
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item_id = int(item.get("id"))
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except (TypeError, ValueError):
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continue
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if item_id in expected_ids:
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translations[item_id] = str(item.get("text") or "").strip()
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missing_ids = sorted(expected_ids - set(translations.keys()))
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if missing_ids:
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raise SubtitleTranslationError(f"LLM 字幕翻译结果缺少字幕条目: {missing_ids[:10]}")
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return translations
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def _build_repair_prompt(
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*,
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blocks,
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target_language: str,
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previous_output: str,
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error_message: str,
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) -> str:
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payload = {str(block.order): block.text for block in blocks}
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return f"""
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你上一轮返回的字幕翻译 JSON 无法通过校验，请修复后重新输出。
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目标语言：{target_language}
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校验错误：
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{error_message}
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原始字幕条目：
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{json.dumps(payload, ensure_ascii=False, indent=2)}
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上一轮输出：
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{previous_output}
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请只输出严格 JSON 对象，必须包含并且只包含原始字幕条目的所有 key。
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""".strip()
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def _translate_chunk(
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*,
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chunk,
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chunk_index: int,
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total_chunks: int,
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target_language: str,
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provider: str,
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api_key: str,
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base_url: str,
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temperature: float,
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max_repair_attempts: int,
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) -> dict[int, str]:
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start_order = chunk[0].order
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end_order = chunk[-1].order
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expected_ids = {block.order for block in chunk}
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logger.info(
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f"字幕翻译批次 {chunk_index}/{total_chunks} 开始: "
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f"条目 {start_order}-{end_order}, 共 {len(chunk)} 条"
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)
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prompt = _build_translation_prompt(chunk, target_language)
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last_output = ""
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last_error = ""
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for attempt in range(1, max_repair_attempts + 1):
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if attempt > 1:
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logger.warning(
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f"字幕翻译批次 {chunk_index}/{total_chunks} 第 {attempt} 次修复: {last_error}"
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)
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prompt = _build_repair_prompt(
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blocks=chunk,
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target_language=target_language,
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previous_output=last_output,
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error_message=last_error,
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)
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raw_output = _run_async_safely(
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UnifiedLLMService.generate_text,
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prompt=prompt,
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system_prompt=(
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f"你是一位专业字幕翻译员，擅长在严格保留 JSON key 一一对应的前提下，"
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f"将字幕准确翻译为{target_language}。"
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),
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provider=provider,
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temperature=temperature,
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response_format="json",
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api_key=api_key,
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api_base=base_url,
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)
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last_output = str(raw_output or "")
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try:
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translations = _parse_translations(last_output, expected_ids)
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except SubtitleTranslationError as exc:
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last_error = str(exc)
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if attempt >= max_repair_attempts:
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logger.error(
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f"字幕翻译批次 {chunk_index}/{total_chunks} 失败: "
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f"条目 {start_order}-{end_order}, {last_error}"
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)
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raise
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continue
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logger.info(
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f"字幕翻译批次 {chunk_index}/{total_chunks} 完成: "
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f"条目 {start_order}-{end_order}"
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)
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return translations
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raise SubtitleTranslationError(
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f"字幕翻译批次 {chunk_index}/{total_chunks} 未生成有效结果: 条目 {start_order}-{end_order}"
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)
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def _call_progress_callback(
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progress_callback: TranslationProgressCallback | None,
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completed: int,
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total: int,
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message: str,
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) -> None:
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if not progress_callback:
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return
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try:
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progress_callback(completed, total, message)
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except Exception as exc:
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logger.debug(f"字幕翻译进度回调失败: {exc}")
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def _render_translated_srt(blocks, translations: dict[int, str]) -> str:
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rendered_blocks = []
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for block in blocks:
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translated_text = translations.get(block.order, "")
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rendered_blocks.append(f"{block.index_line}\n{block.time_line}\n{translated_text}")
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return "\n\n".join(rendered_blocks).rstrip() + "\n"
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def translate_srt_content(
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srt_content: str,
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*,
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target_language: str = "中文",
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provider: str = "",
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api_key: str = "",
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base_url: str = "",
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temperature: float = 0.2,
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batch_size: int | None = None,
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max_workers: int | None = None,
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progress_callback: TranslationProgressCallback | None = None,
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) -> str:
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target_language = str(target_language or "").strip() or "中文"
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blocks = parse_srt_blocks(srt_content)
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_ensure_llm_providers_registered()
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resolved_batch_size = _resolve_batch_size(batch_size)
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chunks = _split_blocks(blocks, resolved_batch_size)
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resolved_max_workers = min(_resolve_max_workers(max_workers), len(chunks))
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total_chunks = len(chunks)
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total_blocks = len(blocks)
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logger.info(
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f"开始批量翻译字幕: 共 {total_blocks} 条, {total_chunks} 批, "
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f"每批最多 {resolved_batch_size} 条, 并发 {resolved_max_workers}, 目标语言: {target_language}"
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)
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translations: dict[int, str] = {}
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completed_blocks = 0
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_call_progress_callback(
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progress_callback,
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0,
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total_blocks,
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f"开始翻译字幕，共 {total_blocks} 条，{total_chunks} 批",
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)
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if total_chunks == 1:
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translations.update(
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_translate_chunk(
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chunk=chunks[0],
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chunk_index=1,
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total_chunks=total_chunks,
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target_language=target_language,
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provider=provider,
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api_key=api_key,
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base_url=base_url,
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temperature=temperature,
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max_repair_attempts=DEFAULT_MAX_REPAIR_ATTEMPTS,
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)
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)
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completed_blocks = total_blocks
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_call_progress_callback(progress_callback, completed_blocks, total_blocks, "字幕翻译完成")
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else:
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with ThreadPoolExecutor(max_workers=resolved_max_workers) as executor:
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future_to_meta = {}
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for index, chunk in enumerate(chunks, start=1):
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future = executor.submit(
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_translate_chunk,
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chunk=chunk,
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chunk_index=index,
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total_chunks=total_chunks,
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target_language=target_language,
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provider=provider,
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api_key=api_key,
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base_url=base_url,
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temperature=temperature,
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max_repair_attempts=DEFAULT_MAX_REPAIR_ATTEMPTS,
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)
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future_to_meta[future] = (index, chunk)
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for future in as_completed(future_to_meta):
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chunk_index, chunk = future_to_meta[future]
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chunk_translations = future.result()
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translations.update(chunk_translations)
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completed_blocks += len(chunk)
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message = (
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f"字幕翻译进度: {completed_blocks}/{total_blocks} 条 "
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f"({ceil(completed_blocks * 100 / total_blocks)}%), "
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f"完成批次 {chunk_index}/{total_chunks}"
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)
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logger.info(message)
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_call_progress_callback(progress_callback, completed_blocks, total_blocks, message)
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missing_ids = sorted({block.order for block in blocks} - set(translations.keys()))
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if missing_ids:
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raise SubtitleTranslationError(f"字幕翻译结果缺少字幕条目: {missing_ids[:10]}")
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translated_srt = _render_translated_srt(blocks, translations)
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logger.info(f"字幕翻译完成，共 {total_blocks} 条")
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return translated_srt
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def write_srt_file(srt_content: str, subtitle_file: str = "") -> str:
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if not subtitle_file:
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subtitle_file = os.path.join(utils.subtitle_dir(), "subtitle_translated.srt")
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parent = os.path.dirname(subtitle_file)
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if parent:
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os.makedirs(parent, exist_ok=True)
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with open(subtitle_file, "w", encoding="utf-8") as f:
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f.write(srt_content)
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return subtitle_file
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def translate_subtitle_file(
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subtitle_file: str,
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output_file: str = "",
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*,
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target_language: str = "中文",
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provider: str = "",
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api_key: str = "",
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base_url: str = "",
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temperature: float = 0.2,
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batch_size: int | None = None,
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max_workers: int | None = None,
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progress_callback: TranslationProgressCallback | None = None,
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) -> str:
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if not subtitle_file or not os.path.isfile(subtitle_file):
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raise SubtitleTranslationError(f"字幕文件不存在: {subtitle_file}")
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decoded = read_subtitle_text(subtitle_file)
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translated_srt = translate_srt_content(
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decoded.text,
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target_language=target_language,
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provider=provider,
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api_key=api_key,
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base_url=base_url,
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temperature=temperature,
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batch_size=batch_size,
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max_workers=max_workers,
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progress_callback=progress_callback,
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)
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return write_srt_file(translated_srt, output_file)
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157
app/services/test_subtitle_translator_unittest.py
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157
app/services/test_subtitle_translator_unittest.py
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import json
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import tempfile
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import unittest
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from pathlib import Path
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from unittest import mock
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from app.services import subtitle_translator as translator
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SAMPLE_SRT = """1
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00:00:01,000 --> 00:00:03,000
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Hello, everyone.
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2
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00:00:04,000 --> 00:00:06,000
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We are going to Beijing.
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"""
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BATCH_SAMPLE_SRT = """1
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00:00:01,000 --> 00:00:02,000
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Line one.
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2
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00:00:02,000 --> 00:00:03,000
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Line two.
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3
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00:00:03,000 --> 00:00:04,000
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Line three.
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4
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00:00:04,000 --> 00:00:05,000
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Line four.
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5
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00:00:05,000 --> 00:00:06,000
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Line five.
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"""
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class SubtitleTranslatorTests(unittest.TestCase):
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def test_translate_srt_content_preserves_timecodes_and_rebuilds_text(self):
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llm_output = {
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"items": [
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{"id": 1, "text": "大家好。"},
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{"id": 2, "text": "我们要去北京。"},
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]
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}
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with (
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mock.patch("app.services.subtitle_translator._ensure_llm_providers_registered"),
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mock.patch(
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"app.services.subtitle_translator._run_async_safely",
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return_value=json.dumps(llm_output, ensure_ascii=False),
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) as run_llm,
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):
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translated = translator.translate_srt_content(
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SAMPLE_SRT,
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target_language="中文",
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provider="openai",
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api_key="sk-test",
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base_url="https://llm.example/v1",
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)
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self.assertIn("00:00:01,000 --> 00:00:03,000", translated)
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self.assertIn("大家好。", translated)
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self.assertIn("我们要去北京。", translated)
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self.assertNotIn("Hello, everyone.", translated)
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call_kwargs = run_llm.call_args.kwargs
|
||||
self.assertEqual("openai", call_kwargs["provider"])
|
||||
self.assertEqual("sk-test", call_kwargs["api_key"])
|
||||
self.assertEqual("https://llm.example/v1", call_kwargs["api_base"])
|
||||
self.assertEqual("json", call_kwargs["response_format"])
|
||||
self.assertIn("专业字幕翻译员", call_kwargs["system_prompt"])
|
||||
self.assertIn("翻译为中文", call_kwargs["prompt"])
|
||||
|
||||
def test_translate_srt_content_rejects_missing_items(self):
|
||||
llm_output = {"items": [{"id": 1, "text": "大家好。"}]}
|
||||
|
||||
with (
|
||||
mock.patch("app.services.subtitle_translator._ensure_llm_providers_registered"),
|
||||
mock.patch(
|
||||
"app.services.subtitle_translator._run_async_safely",
|
||||
return_value=json.dumps(llm_output, ensure_ascii=False),
|
||||
),
|
||||
):
|
||||
with self.assertRaises(translator.SubtitleTranslationError):
|
||||
translator.translate_srt_content(SAMPLE_SRT, provider="openai")
|
||||
|
||||
def test_translate_subtitle_file_writes_translated_srt(self):
|
||||
llm_output = {
|
||||
"items": [
|
||||
{"id": 1, "text": "大家好。"},
|
||||
{"id": 2, "text": "我们要去北京。"},
|
||||
]
|
||||
}
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
input_file = Path(tmp_dir) / "input.srt"
|
||||
output_file = Path(tmp_dir) / "output.srt"
|
||||
input_file.write_text(SAMPLE_SRT, encoding="utf-8")
|
||||
|
||||
with (
|
||||
mock.patch("app.services.subtitle_translator._ensure_llm_providers_registered"),
|
||||
mock.patch(
|
||||
"app.services.subtitle_translator._run_async_safely",
|
||||
return_value=json.dumps(llm_output, ensure_ascii=False),
|
||||
),
|
||||
):
|
||||
result_path = translator.translate_subtitle_file(
|
||||
str(input_file),
|
||||
str(output_file),
|
||||
target_language="中文",
|
||||
provider="openai",
|
||||
)
|
||||
|
||||
self.assertEqual(str(output_file), result_path)
|
||||
self.assertIn("大家好。", output_file.read_text(encoding="utf-8"))
|
||||
|
||||
def test_translate_srt_content_batches_requests_and_reports_progress(self):
|
||||
progress_events = []
|
||||
|
||||
def fake_run_llm(*args, **kwargs):
|
||||
payload_text = kwargs["prompt"].rsplit("待翻译字幕条目：", 1)[1].strip()
|
||||
payload = json.loads(payload_text)
|
||||
translated = {key: f"译文{key}" for key in payload}
|
||||
return json.dumps(translated, ensure_ascii=False)
|
||||
|
||||
with (
|
||||
mock.patch("app.services.subtitle_translator._ensure_llm_providers_registered"),
|
||||
mock.patch(
|
||||
"app.services.subtitle_translator._run_async_safely",
|
||||
side_effect=fake_run_llm,
|
||||
) as run_llm,
|
||||
):
|
||||
translated = translator.translate_srt_content(
|
||||
BATCH_SAMPLE_SRT,
|
||||
target_language="中文",
|
||||
provider="openai",
|
||||
batch_size=2,
|
||||
max_workers=1,
|
||||
progress_callback=lambda completed, total, message: progress_events.append(
|
||||
(completed, total, message)
|
||||
),
|
||||
)
|
||||
|
||||
self.assertEqual(3, run_llm.call_count)
|
||||
self.assertIn("译文1", translated)
|
||||
self.assertIn("译文5", translated)
|
||||
self.assertEqual((0, 5), progress_events[0][:2])
|
||||
self.assertEqual((5, 5), progress_events[-1][:2])
|
||||
self.assertTrue(any("完成批次" in event[2] for event in progress_events))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@ -5,6 +5,8 @@
|
||||
llm_vision_timeout = 120 # 视觉模型基础超时时间
|
||||
llm_text_timeout = 180 # 文本模型基础超时时间（解说文案生成等复杂任务需要更长时间）
|
||||
llm_max_retries = 3 # API 重试次数
|
||||
subtitle_translate_batch_size = 20 # 字幕翻译每批处理的字幕条数
|
||||
subtitle_translate_max_workers = 3 # 字幕翻译最大并发批次数
|
||||
|
||||
##########################################
|
||||
# 🚀 LLM 配置 - 使用 OpenAI 兼容统一接口
|
||||
|
||||
@ -205,6 +205,14 @@ def _format_file_list_for_display(paths, max_items=3):
|
||||
return f"{visible_names} +{len(file_names) - max_items}"
|
||||
|
||||
|
||||
def _safe_filename_fragment(value, fallback="translated"):
|
||||
fragment = "".join(
|
||||
char if char.isalnum() or char in {"-", "_"} else "_"
|
||||
for char in str(value or "").strip()
|
||||
).strip("_")
|
||||
return fragment or fallback
|
||||
|
||||
|
||||
def _read_subtitle_file(path):
|
||||
try:
|
||||
return read_subtitle_text(path).text
|
||||
@ -1211,23 +1219,41 @@ def render_fun_asr_transcription(tr):
|
||||
# 上传字幕面板会在本轮渲染中更新 session_state，这里重新读取一次，保证按钮状态同步。
|
||||
subtitle_paths = _selected_subtitle_paths()
|
||||
can_transcribe = backend != "upload" and bool(media_paths)
|
||||
can_correct_subtitles = bool(subtitle_paths)
|
||||
can_manage_subtitles = bool(subtitle_paths)
|
||||
saved_target_language = str(config.ui.get("subtitle_translate_target_language", "中文") or "中文")
|
||||
|
||||
with subtitle_cols[1]:
|
||||
action_cols = st.columns(2)
|
||||
with action_cols[0]:
|
||||
transcribe_clicked = st.button(
|
||||
tr("Transcribe subtitles"),
|
||||
key="fun_asr_transcribe",
|
||||
disabled=not can_transcribe,
|
||||
use_container_width=True,
|
||||
)
|
||||
with action_cols[1]:
|
||||
correct_clicked = st.button(
|
||||
tr("Calibrate subtitles"),
|
||||
key="subtitle_correct",
|
||||
disabled=not can_correct_subtitles,
|
||||
use_container_width=True,
|
||||
)
|
||||
transcribe_clicked = st.button(
|
||||
tr("Transcribe subtitles"),
|
||||
key="fun_asr_transcribe",
|
||||
disabled=not can_transcribe,
|
||||
use_container_width=True,
|
||||
)
|
||||
|
||||
subtitle_action_cols = st.columns(3, vertical_alignment="bottom")
|
||||
with subtitle_action_cols[0]:
|
||||
target_language = st.text_input(
|
||||
tr("Subtitle target language"),
|
||||
value=saved_target_language,
|
||||
key="subtitle_translate_target_language",
|
||||
placeholder=tr("Subtitle target language placeholder"),
|
||||
)
|
||||
with subtitle_action_cols[1]:
|
||||
translate_clicked = st.button(
|
||||
tr("Translate subtitles"),
|
||||
key="subtitle_translate",
|
||||
disabled=not can_manage_subtitles,
|
||||
use_container_width=True,
|
||||
)
|
||||
with subtitle_action_cols[2]:
|
||||
correct_clicked = st.button(
|
||||
tr("Calibrate subtitles"),
|
||||
key="subtitle_correct",
|
||||
disabled=not can_manage_subtitles,
|
||||
use_container_width=True,
|
||||
)
|
||||
|
||||
target_language = str(target_language or "").strip() or "中文"
|
||||
|
||||
if correct_clicked:
|
||||
from app.services import subtitle_corrector
|
||||
@ -1278,6 +1304,87 @@ def render_fun_asr_transcription(tr):
|
||||
st.error(f"{tr('Subtitle calibration failed')}: {str(e)}")
|
||||
return
|
||||
|
||||
if translate_clicked:
|
||||
from app.services import subtitle_translator
|
||||
|
||||
text_provider = config.app.get('text_llm_provider', 'openai').lower()
|
||||
text_api_key = config.app.get(f'text_{text_provider}_api_key')
|
||||
text_base_url = config.app.get(f'text_{text_provider}_base_url')
|
||||
|
||||
translated_paths = []
|
||||
try:
|
||||
config.ui["subtitle_translate_target_language"] = target_language
|
||||
config.save_config()
|
||||
|
||||
spinner_text = tr("Translating subtitles...").format(language=target_language)
|
||||
with st.spinner(spinner_text):
|
||||
progress_bar = st.progress(0)
|
||||
progress_caption = st.empty()
|
||||
target_suffix = _safe_filename_fragment(target_language)
|
||||
for index, subtitle_path in enumerate(subtitle_paths, start=1):
|
||||
subtitle_name = (
|
||||
f"{os.path.splitext(os.path.basename(subtitle_path))[0]}"
|
||||
f"_translated_{target_suffix}.srt"
|
||||
)
|
||||
output_path = _unique_file_path(utils.subtitle_dir(), subtitle_name)
|
||||
subtitle_file_label = os.path.basename(subtitle_path)
|
||||
|
||||
def update_translation_progress(
|
||||
completed,
|
||||
total,
|
||||
message,
|
||||
file_index=index,
|
||||
file_label=subtitle_file_label,
|
||||
):
|
||||
total = max(int(total or 0), 1)
|
||||
completed = max(0, min(int(completed or 0), total))
|
||||
file_progress = completed / total
|
||||
overall_progress = ((file_index - 1) + file_progress) / max(len(subtitle_paths), 1)
|
||||
progress_bar.progress(min(overall_progress, 1.0))
|
||||
progress_caption.caption(
|
||||
tr("Subtitle translation progress").format(
|
||||
file=file_label,
|
||||
completed=completed,
|
||||
total=total,
|
||||
message=message,
|
||||
)
|
||||
)
|
||||
|
||||
translated_path = subtitle_translator.translate_subtitle_file(
|
||||
subtitle_file=subtitle_path,
|
||||
output_file=output_path,
|
||||
target_language=target_language,
|
||||
provider=text_provider,
|
||||
api_key=text_api_key,
|
||||
base_url=text_base_url,
|
||||
progress_callback=update_translation_progress,
|
||||
)
|
||||
translated_paths.append(translated_path)
|
||||
progress_bar.progress(index / len(subtitle_paths))
|
||||
|
||||
progress_caption.empty()
|
||||
progress_bar.empty()
|
||||
|
||||
_set_subtitle_state(translated_paths)
|
||||
success_placeholder = st.empty()
|
||||
if len(translated_paths) == 1:
|
||||
success_placeholder.success(
|
||||
tr("Subtitle translation succeeded").format(file=os.path.basename(translated_paths[0]))
|
||||
)
|
||||
else:
|
||||
success_placeholder.success(
|
||||
tr("Subtitle translation succeeded for multiple files").format(
|
||||
count=len(translated_paths),
|
||||
files=_format_file_list_for_display(translated_paths),
|
||||
)
|
||||
)
|
||||
time.sleep(3)
|
||||
success_placeholder.empty()
|
||||
except Exception as e:
|
||||
logger.error(f"字幕翻译失败: {traceback.format_exc()}")
|
||||
st.error(f"{tr('Subtitle translation failed')}: {str(e)}")
|
||||
return
|
||||
|
||||
if not transcribe_clicked:
|
||||
return
|
||||
|
||||
|
||||
@ -501,6 +501,9 @@
|
||||
"Selected video files do not exist": "These selected video files do not exist. Please select or upload them again: {files}",
|
||||
"Transcribe subtitles": "Transcribe Subtitles",
|
||||
"Calibrate subtitles": "Calibrate Subtitles",
|
||||
"Translate subtitles": "Translate Subtitles",
|
||||
"Subtitle target language": "Target language",
|
||||
"Subtitle target language placeholder": "Chinese",
|
||||
"Please enter Ali Bailian API Key": "Please enter the Ali Bailian API Key first",
|
||||
"Please enter local FunASR-Pack API URL": "Please enter the local FunASR-Pack API URL first",
|
||||
"Please enter local FireRedASR API URL": "Please enter the local ASR API URL first",
|
||||
@ -515,6 +518,11 @@
|
||||
"Subtitle calibration succeeded": "Subtitle calibration succeeded: {file}",
|
||||
"Subtitle calibration succeeded for multiple files": "Subtitle calibration succeeded for {count} files: {files}",
|
||||
"Subtitle calibration failed": "Subtitle calibration failed",
|
||||
"Translating subtitles...": "Translating subtitles to {language} with the LLM, please wait...",
|
||||
"Subtitle translation succeeded": "Subtitle translation succeeded: {file}",
|
||||
"Subtitle translation succeeded for multiple files": "Subtitle translation succeeded for {count} files: {files}",
|
||||
"Subtitle translation failed": "Subtitle translation failed",
|
||||
"Subtitle translation progress": "{file}: {completed}/{total} items · {message}",
|
||||
"Transcribed subtitles storage hint": "Previously transcribed subtitles are saved in {path}; drag a file from that folder to upload",
|
||||
"Tavily Search Settings": "Tavily Web Search",
|
||||
"Tavily API Key": "Tavily API Key",
|
||||
|
||||
@ -440,6 +440,9 @@
|
||||
"Selected video files do not exist": "以下视频文件不存在，请重新选择或上传: {files}",
|
||||
"Transcribe subtitles": "转录字幕",
|
||||
"Calibrate subtitles": "校准字幕",
|
||||
"Translate subtitles": "翻译字幕",
|
||||
"Subtitle target language": "目标语言",
|
||||
"Subtitle target language placeholder": "中文",
|
||||
"Please enter Ali Bailian API Key": "请先输入阿里百炼 API Key",
|
||||
"Please enter local FunASR-Pack API URL": "请先输入本地 FunASR-Pack API 地址",
|
||||
"Please enter local FireRedASR API URL": "请先输入本地ASR API 地址",
|
||||
@ -454,6 +457,11 @@
|
||||
"Subtitle calibration succeeded": "字幕校准成功: {file}",
|
||||
"Subtitle calibration succeeded for multiple files": "字幕校准成功，共 {count} 个文件: {files}",
|
||||
"Subtitle calibration failed": "字幕校准失败",
|
||||
"Translating subtitles...": "正在使用大模型翻译字幕为 {language}，请稍候...",
|
||||
"Subtitle translation succeeded": "字幕翻译成功: {file}",
|
||||
"Subtitle translation succeeded for multiple files": "字幕翻译成功，共 {count} 个文件: {files}",
|
||||
"Subtitle translation failed": "字幕翻译失败",
|
||||
"Subtitle translation progress": "{file}: {completed}/{total} 条 · {message}",
|
||||
"Transcribed subtitles storage hint": "之前转录生成的字幕保存在 {path}，可从该目录拖入上传",
|
||||
"Tavily Search Settings": "Tavily 联网搜索",
|
||||
"Tavily API Key": "Tavily API Key",
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user