215 lines
8.3 KiB
Python
215 lines
8.3 KiB
Python
import os
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import time
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import subprocess
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import json
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import shutil
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from pathlib import Path
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from watchdog.observers import Observer
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from watchdog.events import FileSystemEventHandler
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from logger import get_system_logger, get_ai_logger, log_exception
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# ==========================================
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# 接口配置 (Interface Configuration)
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# ==========================================
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SESSION_DIR = r'./session' # 监控的工作区目录
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CHECK_INTERVAL = 2 # 轮询频率
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CODEX_CMD = "/home/theshy/.nvm/versions/node/v22.13.0/bin/codex" # Linux 下通常直接用 codex
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DONE_FLAG = "transcribe_done.flag" # 监听这个标记
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# 初始化日志
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logger = get_system_logger('monitorSrt')
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# ==========================================
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# 定义输出数据的 JSON Schema
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SONG_SCHEMA = {
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"type": "object",
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"properties": {
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"songs": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"start": {"type": "string"},
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"end": {"type": "string"},
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"title": {"type": "string"},
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"artist": {"type": "string"},
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"confidence": {"type": "number"},
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"evidence": {"type": "string"}
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},
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"required": ["start", "end", "title", "artist", "confidence", "evidence"],
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"additionalProperties": False
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}
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}
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},
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"required": ["songs"],
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"additionalProperties": False
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}
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TASK_PROMPT = """你是音乐片段识别助手。当前目录下有一个字幕文件。
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任务：
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1. 结合字幕内容并允许联网搜索进行纠错（识别同音字、唱错等）。
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2. 识别出直播中唱过的所有歌曲，给出精确的开始和结束时间。歌曲开始时间规则：
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- 歌曲开始时间应使用“上一句字幕的结束时间”作为 start_time。
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- 这样可以尽量保留歌曲可能存在的前奏。
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3. 同一首歌间隔 ≤160s 合并，>160s 分开。若连续识别出相同歌曲，且中间只有短暂对白、空白、转场或无歌词段，应合并为同一首歌.
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4. 忽略纯聊天片段。
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5. 无法确认的歌曲丢弃,宁缺毋滥：你的输出将直接面向最终用户。
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6. 忽略短片段：如果一段演唱持续时间总和少于 15 秒，视为随口哼唱，请直接忽略，不计入列表。
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7. 仔细分析每一句歌词,识别出相关歌曲后, 使用该歌曲歌词上下文对比字幕上下文,确定歌曲起始与停止时间
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8.歌曲标注规则：
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- 可以在歌曲名称后使用括号 () 添加补充说明。
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- 常见标注示例：
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- (片段)：歌曲演唱时间较短，例如 < 60 秒
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- (清唱)：无伴奏演唱
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- (副歌)：只演唱副歌部分
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- 标注应简洁，仅在确有必要时使用。
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9. 通过歌曲起始和结束时间自检, 一般歌曲长度在5分钟以内, 1分钟以上, 可疑片段重新联网搜索检查.
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最后请严格按照 Schema 生成 JSON 数据。"""
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# ==========================================
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class SrtHandler(FileSystemEventHandler):
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def on_created(self, event):
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if not event.is_directory:
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src_path = event.src_path
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if isinstance(src_path, bytes):
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src_path = src_path.decode('utf-8')
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if src_path.endswith(DONE_FLAG):
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logger.debug(f"检测到转录完成标记: {src_path}")
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self.process_with_codex(Path(src_path))
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def on_moved(self, event):
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dest_path = event.dest_path
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if isinstance(dest_path, bytes):
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dest_path = dest_path.decode('utf-8')
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if not event.is_directory and dest_path.lower().endswith('.srt'):
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logger.debug(f"检测到字幕文件移动: {dest_path}")
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self.process_with_codex(Path(dest_path))
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def process_with_codex(self, srt_path):
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work_dir = srt_path.parent
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# 避免对同一目录重复调用
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if (work_dir / "songs.json").exists():
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logger.info(f"songs.json 已存在，跳过: {work_dir.name}")
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return
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logger.info(f"发现新任务，准备识别歌曲: {work_dir.name}")
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# 创建AI日志
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ai_log, ai_log_file = get_ai_logger('codex', 'songs')
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ai_log.info("="*50)
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ai_log.info("Codex 歌曲识别任务开始")
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ai_log.info(f"工作目录: {work_dir}")
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ai_log.info("="*50)
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# 生成临时 Schema 文件
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schema_file = work_dir / "song_schema.json"
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with open(schema_file, "w", encoding="utf-8") as f:
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json.dump(SONG_SCHEMA, f, ensure_ascii=False, indent=2)
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# 构建命令行参数 (Linux 下必须使用列表形式)
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cmd = [
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CODEX_CMD, "exec",
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TASK_PROMPT.replace('\n', ' '),
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"--full-auto",
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"--sandbox", "workspace-write",
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"--output-schema", "./song_schema.json",
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"-o", "songs.json",
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"--skip-git-repo-check",
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"--json"
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]
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logger.info("调用 Codex...")
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ai_log.info(f"执行命令: {subprocess.list2cmdline(cmd)}")
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try:
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start_time = time.time()
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# 关键修改：shell=False + 直接传列表，解决 "File name too long" 错误
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result = subprocess.run(
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cmd,
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cwd=str(work_dir),
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shell=False,
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capture_output=True,
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text=True,
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encoding='utf-8'
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)
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elapsed = time.time() - start_time
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ai_log.info(f"Codex 执行完成，耗时: {elapsed:.2f}秒")
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# 记录输出
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if result.stdout:
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ai_log.info("=== STDOUT ===")
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ai_log.info(result.stdout)
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if result.stderr:
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ai_log.warning("=== STDERR ===")
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ai_log.warning(result.stderr)
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if result.returncode == 0:
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logger.info(f"Codex 执行成功: {work_dir.name}")
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self.generate_txt_fallback(work_dir, ai_log)
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else:
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logger.error(f"Codex 失败，返回码: {result.returncode}")
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ai_log.error(f"Codex 失败，返回码: {result.returncode}")
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except Exception as e:
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log_exception(logger, e, "Codex 调用异常")
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log_exception(ai_log, e, "Codex 执行异常")
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ai_log.info("="*50)
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ai_log.info("Codex 歌曲识别任务完成")
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ai_log.info("="*50)
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def generate_txt_fallback(self, work_dir, ai_log):
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json_path = work_dir / "songs.json"
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txt_path = work_dir / "songs.txt"
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try:
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if json_path.exists():
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with open(json_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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songs = data.get('songs', [])
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with open(txt_path, 'w', encoding='utf-8') as t:
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for s in songs:
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start_time = s['start'].split(',')[0].split('.')[0] # 兼容点号和逗号
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line = f"{start_time} {s['title']} — {s['artist']}\n"
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t.write(line)
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logger.info(f"成功生成: {txt_path.name}")
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except Exception as e:
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log_exception(logger, e, "生成 txt 失败")
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def main():
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path = Path(SESSION_DIR)
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path.mkdir(parents=True, exist_ok=True)
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logger.info("="*50)
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logger.info("字幕监控模块启动 (Linux 优化版)")
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logger.info("="*50)
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event_handler = SrtHandler()
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# 启动扫描：检查是否有 flag 但没 songs.json 的存量目录
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logger.info("正在扫描存量任务...")
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for sub_dir in path.iterdir():
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if sub_dir.is_dir():
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flag = sub_dir / DONE_FLAG
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json_file = sub_dir / "songs.json"
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if flag.exists() and not json_file.exists():
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logger.info(f"发现存量任务: {sub_dir.name}")
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event_handler.process_with_codex(flag)
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observer = Observer()
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observer.schedule(event_handler, str(path), recursive=True)
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observer.start()
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try:
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while True:
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time.sleep(CHECK_INTERVAL)
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except KeyboardInterrupt:
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observer.stop()
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observer.join()
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if __name__ == "__main__":
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main()
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