73110848f4
- 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
140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
"""运行时配置：所有参数从 config.yaml 读取。
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CPU dev / GPU prod 仅靠 device / model / compute_type 三项切换，代码完全不变。
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默认值与 config.example.yaml 对齐，确保无 yaml 时也能用最小配置启动。
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"""
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from __future__ import annotations
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import os
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from functools import lru_cache
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from pathlib import Path
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import yaml
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from pydantic import BaseModel, Field, field_validator
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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DEFAULT_CONFIG_PATH = PROJECT_ROOT / "config.yaml"
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class ServerConfig(BaseModel):
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host: str = "0.0.0.0"
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port: int = 8000
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workers: int = 1
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class StorageConfig(BaseModel):
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upload_dir: str = "/data/uploads"
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work_dir: str = "/data/.work"
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output_dir: str = "/data/outputs"
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chunk_bytes: int = 1024 * 1024
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chunk_session_ttl_seconds: int = 300
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# 缓存保留：超期任务产物（输出字幕 / 中间音频 / 保留的原始视频）连同 DB 记录一并清理
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cache_retention_days: int = 7
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# 定时清理间隔（小时）：启动时跑一次，之后按此间隔循环
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cache_cleanup_interval_hours: int = 24
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class ProcessingConfig(BaseModel):
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delete_original_after_extract: bool = True
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keep_audio: bool = False
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class AsrConfig(BaseModel):
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model: str = "tiny.en" # CPU: tiny.en；GPU: large-v3-turbo
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device: str = "cpu" # cpu | cuda
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compute_type: str = "int8" # cpu: int8；gpu: float16
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language: str = "en"
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word_timestamps: bool = True
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vad_filter: bool = True
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batch_size: int = 8 # BatchedInferencePipeline 的音频块批大小；GPU 建议 16
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class TranslationConfig(BaseModel):
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model: str = "Helsinki-NLP/opus-mt-en-zh" # CPU: opus-mt（轻量）；GPU: facebook/nllb-200-distilled-1.3B
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device: str = "cpu" # cpu | cuda
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src_lang: str = "eng_Latn" # NLLB 语言码：英语
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tgt_lang: str = "zho_Hans" # NLLB 语言码：简体中文
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batch_size: int = 8 # 排序后单批最大条数；CPU: 8，GPU: 32（显存独占可用大 batch）
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max_length: int = 256 # 单条最大生成 token 数
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sort_by_length: bool = True # 按句子长度排序后分批，减少批内 padding 浪费（GPU 收益大）
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class SegmentationConfig(BaseModel):
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max_words_per_line: int = 14
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max_duration_seconds: float = 7.0
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min_duration_seconds: float = 1.0
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max_chars_per_line: int = 42
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class LoggingConfig(BaseModel):
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"""日志配置：控制台 + 内存缓冲的最低级别，以及缓冲条数。
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分层语义：
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- debug：进度详情（任务 [status pct%]、转写/翻译逐批统计、ffmpeg 命令）
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- info：任务流转里程碑（音频提取/ASR/翻译 的开始与完成、模型加载与卸载）
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- error：详细错误（完整 traceback，由 logger.exception 自带）
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"""
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level: str = "info" # debug | info | warning | error
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buffer_size: int = 2000 # /logs 页面内存缓冲条数
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@field_validator("level", mode="before")
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@classmethod
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def _normalize_level(cls, v):
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return str(v).lower() if v else "info"
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class DocsConfig(BaseModel):
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"""/docs Basic Auth 凭据（明文，对齐 server）。"""
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enabled: bool = True
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username: str = "admin"
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password: str = ""
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realm: str = "audio2text docs"
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@field_validator("password", mode="before")
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@classmethod
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def _coerce(cls, v):
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return "" if v is None else str(v)
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class Settings(BaseModel):
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server: ServerConfig = ServerConfig()
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storage: StorageConfig = StorageConfig()
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processing: ProcessingConfig = ProcessingConfig()
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asr: AsrConfig = AsrConfig()
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translation: TranslationConfig = TranslationConfig()
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segmentation: SegmentationConfig = SegmentationConfig()
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logging: LoggingConfig = LoggingConfig()
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docs: DocsConfig = DocsConfig()
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def upload_dir(self) -> Path:
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return Path(self.storage.upload_dir)
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def work_dir(self) -> Path:
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return Path(self.storage.work_dir)
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def output_dir(self) -> Path:
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return Path(self.storage.output_dir)
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def _load_yaml(path: Path) -> dict:
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if not path.exists():
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raise FileNotFoundError(
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f"未找到配置文件 {path}；请先 cp config.example.yaml config.yaml 并填值。"
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)
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return yaml.safe_load(path.read_text(encoding="utf-8")) or {}
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@lru_cache(maxsize=1)
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def get_settings() -> Settings:
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path = Path(os.getenv("CONFIG_PATH", str(DEFAULT_CONFIG_PATH)))
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return Settings.model_validate(_load_yaml(path))
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def reload_settings() -> Settings:
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"""清缓存并重新读取，供脚本与测试使用。"""
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get_settings.cache_clear()
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return get_settings()
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