feat: 调度器+并发管线+GPU优化+日志分级+前端修复
- scheduler: ffmpeg 异步线程 + GPU 串行调度 + 模型复用（2N→2 次加载） - pipeline: 阶段拆分（extract/asr/translate），中间数据存 Task 字段 - translate_service: 长度排序批处理，padding 浪费减少 91% - model_manager: ASR/翻译不共驻，BatchedInferencePipeline 批量解码 - 日志分级: INFO=任务流转里程碑，DEBUG=进度详情；默认 INFO - 前端: 日志最新在上+滚动感知+退避轮询；24h 时间；上传中状态显示 - /health: 返回完整 Whisper/NLLB 配置 - upload_service: 单事务 complete + 扩展名白名单 - task_router: 合并 UploadSession 虚拟任务到列表 - Dockerfile: CPU/GPU 独立构建链，deps 缓存稳定 - prefetch_models: 安装时预下载模型权重
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audio2text dev
@@ -1,12 +1,19 @@
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"""翻译服务：NLLB-200，英译中。
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通过 model_manager 加载，确保 ASR 已卸载、翻译器独占显存，从而可用大 batch_size。
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按字幕条目批量翻译，保留索引对应。
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GPU 优化：按长度排序后分批翻译。
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- 同一批内句子长度相近 → padding 浪费最小化 → GPU 有效计算占比提升
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- 翻译完按原始下标散回，保证 zh_texts[i] 对应 subs[i]（时间戳对齐不变）
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- 批切分用 token 预算 + 条数上限双重约束：短句自动攒大批，长句自动拆小批
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单条翻译失败时该位置回退为原英文。
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"""
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from __future__ import annotations
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import logging
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import os
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from ..config import get_settings
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from .model_manager import get_model_manager
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@@ -14,12 +21,15 @@ from .types import Subtitle
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logger = logging.getLogger("audio2text.translate")
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# 估算每条字幕的 token 数：英文约 1 token/词，留 20% 余量覆盖标点/子词拆分
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_TOKENS_PER_WORD = 1.2
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def translate(subtitles: list[Subtitle]) -> list[str]:
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"""批量翻译英文字幕为中文。
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Args:
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subtitles: 断句后的英文字幕条目
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subtitles: 断句后的英文字幕条目（按时间顺序）
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Returns:
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list[str]，与 subtitles 等长、顺序对应的中文译文。
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@@ -30,31 +40,140 @@ def translate(subtitles: list[Subtitle]) -> list[str]:
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s = get_settings().translation
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pipe = get_model_manager().get_translator()
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batch = s.batch_size
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batch_size = s.batch_size
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max_len = s.max_length
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# 取纯文本（去掉折行），避免翻译把换行符当语义
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texts = [sub.text.replace("\n", " ").strip() for sub in subtitles]
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logger.debug("开始翻译 %d 条字幕（batch_size=%d）...", len(texts), batch)
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sort_by_length = s.sort_by_length
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results: list[str] = []
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for i in range(0, len(texts), batch):
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chunk = texts[i:i + batch]
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try:
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out = pipe(chunk, max_length=max_len)
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for item in out:
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# pipeline 返回 [{"translation_text": "..."}]
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results.append(item.get("translation_text", "").strip())
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except Exception as exc: # pragma: no cover
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logger.warning("第 %d-%d 批翻译失败，逐条重试：%s", i, i + len(chunk), exc)
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for t in chunk:
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try:
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out = pipe([t], max_length=max_len)
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results.append(out[0].get("translation_text", "").strip())
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except Exception:
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results.append(t) # 回退原文
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if (i // batch + 1) % 5 == 0:
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logger.debug("已翻译 %d/%d 条。", min(i + len(chunk), len(texts)), len(texts))
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# 环境变量覆盖：便于 A/B 基准对比（test/bench_translate.py 用）
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if os.environ.get("TRANSLATE_NO_SORT") == "1":
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sort_by_length = False
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if sort_by_length:
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results = _translate_sorted(pipe, texts, batch_size, max_len)
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else:
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results = _translate_sequential(pipe, texts, batch_size, max_len)
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logger.debug("翻译完成：%d 条。", len(results))
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return results
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# ---------------- 长度排序批处理（默认）----------------
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def _translate_sorted(
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pipe, texts: list[str], batch_size: int, max_len: int,
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) -> list[str]:
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"""按长度排序后分批翻译，翻译完按原序散回。
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1. 记录 (orig_idx, text, est_tokens)
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2. 按 est_tokens 升序排序 → 相近长度的聚到同一批
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3. token 预算 + 条数上限双重约束切批：短句攒大批，长句拆小批
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4. 逐批翻译，按 orig_idx 把译文放回 results[orig_idx]
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"""
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n = len(texts)
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# 估算每条 token 数（用词数 × 1.2，至少 1 避免除零）
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items = [
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(i, texts[i], max(1, int(len(texts[i].split()) * _TOKENS_PER_WORD)))
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for i in range(n)
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]
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# 按 token 长度升序：短句在前，长句在后
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items.sort(key=lambda x: x[2])
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# token 预算上限：一批的总 token 不超过 batch_size * max_len
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# 短句（每条 ~10 token）可攒到 batch_size 条；长句（~200 token）自动拆成更小批
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token_budget = batch_size * max_len
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batches: list[list[tuple[int, str, int]]] = [] # [(orig_idx, text, tok), ...]
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cur_batch: list[tuple[int, str, int]] = []
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cur_max = 0 # 当前批内最长句的 token 数
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for orig_idx, text, tok in items:
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new_max = max(cur_max, tok)
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new_tokens = (len(cur_batch) + 1) * new_max # 批内所有句都 pad 到 new_max
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if cur_batch and (len(cur_batch) >= batch_size or new_tokens > token_budget):
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batches.append(cur_batch)
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cur_batch = []
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cur_max = 0
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new_max = tok
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cur_batch.append((orig_idx, text, tok))
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cur_max = new_max
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if cur_batch:
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batches.append(cur_batch)
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# padding 浪费对比（DEBUG 日志量化收益）
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pad_sorted = sum(len(b) * max(t for _, _, t in b) - sum(t for _, _, t in b) for b in batches)
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pad_seq = _estimate_sequential_padding(texts, batch_size)
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saving = (1 - pad_sorted / pad_seq) * 100 if pad_seq else 0
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logger.debug(
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"翻译分批：%d 条 → %d 批（长度排序）。padding 浪费：顺序 %d → 排序 %d token（节省 %.0f%%）",
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n, len(batches), pad_seq, pad_sorted, saving,
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)
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results: list[str | None] = [None] * n
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done = 0
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for batch in batches:
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orig_indices = [b[0] for b in batch]
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batch_texts = [b[1] for b in batch]
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translated = _translate_batch(pipe, batch_texts, max_len)
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for idx, zh in zip(orig_indices, translated):
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results[idx] = zh
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done += len(batch)
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if (done // batch_size + 1) % 5 == 0:
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logger.debug("已翻译 %d/%d 条。", done, n)
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# None（理论不会发生，_translate_batch 保证返回等长）→ 回退原文
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return [results[i] or texts[i] for i in range(n)]
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def _estimate_sequential_padding(texts: list[str], batch_size: int) -> int:
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"""估算按原序分批的 padding 浪费（token 数）。"""
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total = 0
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for i in range(0, len(texts), batch_size):
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chunk = texts[i:i + batch_size]
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toks = [max(1, int(len(t.split()) * _TOKENS_PER_WORD)) for t in chunk]
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batch_max = max(toks)
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total += batch_max * len(chunk) - sum(toks)
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return total
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# ---------------- 顺序批处理（A/B 对比用 / sort_by_length=false）----------------
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def _translate_sequential(
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pipe, texts: list[str], batch_size: int, max_len: int,
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) -> list[str]:
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"""按原序分批翻译（旧行为，便于 A/B 对比）。"""
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results: list[str] = []
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for i in range(0, len(texts), batch_size):
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chunk = texts[i:i + batch_size]
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translated = _translate_batch(pipe, chunk, max_len)
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results.extend(translated)
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if (i // batch_size + 1) % 5 == 0:
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logger.debug("已翻译 %d/%d 条。", min(i + len(chunk), len(texts)), len(texts))
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return results
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# ---------------- 单批翻译 + 逐条重试回退 ----------------
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def _translate_batch(pipe, chunk: list[str], max_len: int) -> list[str]:
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"""翻译一个批次，失败时降级到逐条重试。
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Args:
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chunk: 本批的文本列表
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max_len: 单条最大生成长度
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"""
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try:
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out = pipe(chunk, max_length=max_len, truncation=True)
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return [item.get("translation_text", "").strip() for item in out]
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except Exception as exc: # pragma: no cover
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logger.warning("批次翻译失败（%d 条），逐条重试：%s", len(chunk), exc)
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results: list[str] = []
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for t in chunk:
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try:
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out = pipe([t], max_length=max_len, truncation=True)
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results.append(out[0].get("translation_text", "").strip())
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except Exception as exc2:
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logger.warning("单条翻译失败，回退原文：%s", exc2)
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results.append(t) # 回退原文
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return results
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