78b87bfb24
原来进度只在阶段结束时跳一次（ASR 5%→55%、翻译 60%→98%），长视频时 进度条卡住不动。现在每完成一个批次就更新进度。 ASR（asr_service.py）: - 用 info.duration_after_vad 算总 chunk 数（ceil(时长/30s)） - 消费生成器时按 seg.end 跨 30s chunk 边界回调 on_progress - 30s 粒度自然节流，长视频约几十次更新 翻译（translate_service.py）: - _translate_sorted / _translate_sequential 每批完成后回调 on_progress(done, total) - 批数循环前已知（len(batches)），每批都回调 pipeline.py: - asr_phase / translate_phase 定义闭包回调，把 (current,total) 占比映射到 对应进度区间（ASR 5%→55%、翻译 60%→98%），调 _set_status 写 DB（DEBUG 级） 验证（test/55.mp4, 640 条字幕）: - ASR: 5%→19.9%→23.8%→33.7%→48.6%→55% 平滑增长 ✅ - 翻译: 20 批，65.3%→68.9%→74.2%→79.6%→84.9%→90.2%→93.8%→98% ✅ - 翻译卡在 60% 的 ~50s 是模型加载时间（卸载ASR+加载NLLB），属调度器层面
208 lines
8.0 KiB
Python
208 lines
8.0 KiB
Python
"""转写管线各阶段：提取音频 → ASR → 断句 → 翻译 → 写 SRT。
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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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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 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 ..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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# 进度锚点（各阶段在 0-100 中的占比）
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P_EXTRACT = 5.0
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P_TRANSCRIBE_START = 5.0
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P_TRANSCRIBE_END = 55.0
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P_SEGMENT_START = 55.0
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P_SEGMENT_END = 60.0
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P_TRANSLATE_START = 60.0
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P_TRANSLATE_END = 98.0
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P_DONE = 100.0
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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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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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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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# 按配置决定是否删原始视频（提取成功后）
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if s.processing.delete_original_after_extract and src.is_file():
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try:
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src.unlink()
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logger.info("已删除原始视频 %s（delete_original_after_extract=true）。", src.name)
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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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# 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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# 进度回调：按 30s chunk 细分 ASR 进度（5%→55% 区间）
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# current=已处理 chunk 数, total=总 chunk 数
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def on_asr_progress(current: int, total: int) -> None:
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frac = current / total if total else 0.0
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progress = P_TRANSCRIBE_START + (P_TRANSCRIBE_END - P_TRANSCRIBE_START) * frac
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_set_status(db, task, STATUS_TRANSCRIBING, progress)
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segments = asr_service.transcribe(wav_path, on_progress=on_asr_progress)
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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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_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, STATUS_SEGMENTING, P_SEGMENT_END,
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note=f"重组为 {len(subs)} 条字幕")
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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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# 进度回调：按批次细分翻译进度（60%→98% 区间）
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# done=已翻译条数, total=总条数
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def on_translate_progress(done: int, total: int) -> None:
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frac = done / total if total else 0.0
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progress = P_TRANSLATE_START + (P_TRANSLATE_END - P_TRANSLATE_START) * frac
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_set_status(db, task, STATUS_TRANSLATING, progress)
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zh_texts = translate_service.translate(subs, on_progress=on_translate_progress)
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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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# 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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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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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 = 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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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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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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# ---------------- 工具函数 ----------------
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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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db.commit()
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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.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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"""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 = 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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except Exception: # pragma: no cover
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logger.error("写入失败状态时又失败：\n%s", traceback.format_exc())
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