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
@@ -1,25 +1,31 @@
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"""转写管线编排：提取音频 → ASR → 断句 → 翻译 → 写 SRT。
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"""转写管线各阶段：提取音频 → ASR → 断句 → 翻译 → 写 SRT。
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任务状态机：
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queued → extracting → transcribing → segmenting → translating → done
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任一步失败 → failed
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阶段拆分供 scheduler 调度：ffmpeg 阶段独立线程（CPU），GPU 阶段（ASR+翻译）
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由 scheduler 串行化并在切换模型前查队列复用已加载模型。
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模型不共驻：ASR 与翻译分阶段加载，翻译时先卸载 Whisper 释放显存跑大 batch。
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管线在后台线程跑（每个任务一个线程），通过 DB 更新状态与进度。
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每个阶段函数接收 db Session + Task，更新状态/进度，写入中间产物：
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extract_phase: queued → extracting → (写 wav_path, status 置 transcribing)
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asr_phase: transcribing → segmenting → (写 segments_json, status 置 translating)
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translate_phase: translating → done (写 SRT)
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阶段间传递的中间数据存在 Task.wav_path / Task.segments_json，避免跨线程传对象。
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"""
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from __future__ import annotations
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import json
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import logging
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import threading
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import traceback
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from dataclasses import asdict
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from datetime import datetime, timezone
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from pathlib import Path
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from ..config import get_settings
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from ..database import get_session_local
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from ..models.task import Task
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from . import ffmpeg_service, asr_service, segmenter, translate_service, srt_writer
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from ..models.task import (
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STATUS_EXTRACTING, STATUS_TRANSCRIBING, STATUS_SEGMENTING,
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STATUS_TRANSLATING, STATUS_DONE, STATUS_FAILED,
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)
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from . import asr_service, ffmpeg_service, segmenter, srt_writer, translate_service
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from .types import Subtitle
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logger = logging.getLogger("audio2text.pipeline")
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@@ -35,35 +41,17 @@ P_TRANSLATE_END = 98.0
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P_DONE = 100.0
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def enqueue_task(task_id: int) -> None:
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"""把任务交给后台线程处理（非阻塞，供 upload_service.complete 调用）。"""
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t = threading.Thread(target=_run_task, args=(task_id,), daemon=True)
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t.start()
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logger.info("任务 %d 已入队（后台线程 %s）。", task_id, t.name)
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# ---------------- 阶段 1：提取音频（CPU，可并行）----------------
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def extract_phase(db, task) -> None:
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"""ffmpeg 提取 16k mono wav，写 task.wav_path，状态置 transcribing。
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def _run_task(task_id: int) -> None:
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"""后台执行完整管线。所有异常都被捕获并写入 task.error。"""
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db = get_session_local()()
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try:
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task = db.get(Task, task_id)
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if task is None:
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logger.error("任务 %d 不存在。", task_id)
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return
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_pipeline(db, task)
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except Exception as exc:
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logger.exception("任务 %d 失败：%s", task_id, exc)
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_mark_failed(db, task_id, str(exc))
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finally:
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db.close()
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def _pipeline(db, task: Task) -> None:
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由 scheduler 在独立线程调用（与 GPU 阶段并行）。提取完即可让 GPU 调度线程接管。
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"""
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s = get_settings()
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src = s.upload_dir() / task.source_path
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# ---------- 1. 提取音频 ----------
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_set_status(db, task, "extracting", P_EXTRACT)
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logger.info("任务 %d [音频提取开始] %s", task.id, src.name)
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_set_status(db, task, STATUS_EXTRACTING, P_EXTRACT)
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wav = s.work_dir() / f"task_{task.id}.wav"
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ffmpeg_service.extract_audio(src, wav)
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@@ -75,61 +63,110 @@ def _pipeline(db, task: Task) -> None:
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except OSError as exc:
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logger.warning("删除原始视频失败 %s: %s", src, exc)
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# ---------- 2. 语音识别 ----------
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_set_status(db, task, "transcribing", P_TRANSCRIBE_START)
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segments = asr_service.transcribe(wav)
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_set_status(db, task, "transcribing", P_TRANSCRIBE_END,
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# 记录 wav 路径，状态置 transcribing（待 GPU 调度线程接管 ASR）
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task.wav_path = str(wav)
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task.status = STATUS_TRANSCRIBING
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task.progress = P_TRANSCRIBE_START
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task.updated_at = datetime.now(timezone.utc)
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db.commit()
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logger.info("任务 %d [音频提取完成] → 待 ASR：%s", task.id, wav.name)
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# ---------------- 阶段 2：ASR + 断句（GPU）----------------
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def asr_phase(db, task) -> None:
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"""加载 Whisper 转写 + 断句，写 task.segments_json，状态置 translating。
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由 GPU 调度线程调用。model_manager 保证 ASR 与翻译器不共驻。
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"""
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wav_path = Path(task.wav_path) if task.wav_path else None
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if wav_path is None or not wav_path.is_file():
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raise FileNotFoundError(f"音频不存在：{wav_path}（task {task.id}）")
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logger.info("任务 %d [ASR 开始] %s", task.id, wav_path.name)
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_set_status(db, task, STATUS_TRANSCRIBING, P_TRANSCRIBE_START)
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segments = asr_service.transcribe(wav_path)
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_set_status(db, task, STATUS_TRANSCRIBING, P_TRANSCRIBE_END,
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note=f"识别出 {len(segments)} 段")
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# ---------- 3. 断句 + 时间戳重算 ----------
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_set_status(db, task, "segmenting", P_SEGMENT_START)
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_set_status(db, task, STATUS_SEGMENTING, P_SEGMENT_START)
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subs = segmenter.resegment(segments)
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_set_status(db, task, "segmenting", P_SEGMENT_END,
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_set_status(db, task, STATUS_SEGMENTING, P_SEGMENT_END,
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note=f"重组为 {len(subs)} 条字幕")
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# ---------- 4. 翻译 ----------
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_set_status(db, task, "translating", P_TRANSLATE_START)
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# 翻译阶段：model_manager 会自动卸载 ASR、加载翻译器（独占显存）
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# 序列化断句结果供翻译阶段用（dataclass → JSON）
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task.segments_json = json.dumps([asdict(s) for s in subs], ensure_ascii=False)
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task.status = STATUS_TRANSLATING
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task.progress = P_TRANSLATE_START
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task.updated_at = datetime.now(timezone.utc)
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db.commit()
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logger.info("任务 %d [ASR 完成] → 待翻译：%d 条字幕", task.id, len(subs))
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# ---------------- 阶段 3：翻译 + 写 SRT（GPU）----------------
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def translate_phase(db, task) -> None:
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"""加载 NLLB 翻译 + 写 SRT，状态置 done。由 GPU 调度线程调用。"""
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if not task.segments_json:
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raise ValueError(f"任务 {task.id} 无 segments_json，无法翻译")
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logger.info("任务 %d [翻译开始] %d 条字幕", task.id, len(json.loads(task.segments_json)))
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_set_status(db, task, STATUS_TRANSLATING, P_TRANSLATE_START)
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subs = [_dict_to_subtitle(d) for d in json.loads(task.segments_json)]
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zh_texts = translate_service.translate(subs)
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_set_status(db, task, "translating", P_TRANSLATE_END,
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_set_status(db, task, STATUS_TRANSLATING, P_TRANSLATE_END,
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note=f"翻译 {len(zh_texts)} 条")
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# ---------- 5. 写 SRT ----------
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out_dir = s.output_dir()
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# 写 SRT
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out_dir = s_output_dir()
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stem = Path(task.filename).stem
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en_path = out_dir / f"task_{task.id}/{stem}.en.srt"
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zh_path = out_dir / f"task_{task.id}/{stem}.zh.srt"
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bi_path = out_dir / f"task_{task.id}/{stem}.srt"
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srt_writer.write_srt(subs, en_path)
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# 中文 SRT（用译文 + 同时间戳）
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zh_subs = [Subtitle(text=zh, start=sub.start, end=sub.end)
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for zh, sub in zip(zh_texts, subs)]
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srt_writer.write_srt(zh_subs, zh_path)
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srt_writer.write_bilingual_srt(subs, zh_texts, bi_path)
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# 记录相对路径
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task.en_srt_path = str(en_path.relative_to(out_dir))
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task.zh_srt_path = str(zh_path.relative_to(out_dir))
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task.bilingual_srt_path = str(bi_path.relative_to(out_dir))
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task.status = "done"
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task.status = STATUS_DONE
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task.progress = P_DONE
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task.updated_at = datetime.now(timezone.utc)
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db.commit()
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# 清理中间音频
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if not s.processing.keep_audio and wav.is_file():
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s = get_settings()
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if not s.processing.keep_audio and task.wav_path:
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try:
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wav.unlink()
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Path(task.wav_path).unlink()
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except OSError:
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pass
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logger.info("任务 %d 完成：%s", task.id, bi_path.name)
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logger.info("任务 %d [完成] %s", task.id, bi_path.name)
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# ---------------- DB 状态更新 ----------------
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# ---------------- 工具函数 ----------------
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def _set_status(db, task: Task, status: str, progress: float, note: str = "") -> None:
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def s_output_dir() -> Path:
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return get_settings().output_dir()
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def _dict_to_subtitle(d: dict) -> Subtitle:
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return Subtitle(text=d["text"], start=d["start"], end=d["end"])
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def _set_status(db, task, status: str, progress: float, note: str = "") -> None:
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"""更新任务状态/进度并落库。
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日志级别策略：
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- 带 note 的（如“识别出 N 段”）是阶段内里程碑 → INFO
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- 仅进度百分比更新（同状态同阶段）→ DEBUG，避免 INFO 被进度刷屏
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"""
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task.status = status
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task.progress = progress
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task.updated_at = datetime.now(timezone.utc)
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@@ -137,15 +174,17 @@ def _set_status(db, task: Task, status: str, progress: float, note: str = "") ->
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if note:
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logger.info("任务 %d [%s %.0f%%] %s", task.id, status, progress, note)
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else:
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logger.info("任务 %d [%s %.0f%%]", task.id, status, progress)
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logger.debug("任务 %d [%s %.0f%%]", task.id, status, progress)
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def _mark_failed(db, task_id: int, error: str) -> None:
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def mark_failed(db, task_id: int, error: str) -> None:
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"""标记任务失败（对外公开，供 scheduler 调用）。"""
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from ..models.task import Task
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try:
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task = db.get(Task, task_id)
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if task is None:
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return
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task.status = "failed"
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task.status = STATUS_FAILED
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task.error = error[:2000]
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task.updated_at = datetime.now(timezone.utc)
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db.commit()
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