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theshy
2026-03-21 01:36:28 +08:00
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import os
import time
import subprocess
import json
import shutil
from pathlib import Path
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
from logger import get_system_logger, get_ai_logger, log_exception
# ==========================================
# 接口配置 (Interface Configuration)
# ==========================================
SESSION_DIR = r'./session' # 监控的工作区目录
CHECK_INTERVAL = 2 # 轮询频率
CODEX_CMD = "/home/theshy/.nvm/versions/node/v22.13.0/bin/codex" # Linux 下通常直接用 codex
DONE_FLAG = "transcribe_done.flag" # 监听这个标记
# 初始化日志
logger = get_system_logger('monitorSrt')
# ==========================================
# 定义输出数据的 JSON Schema
SONG_SCHEMA = {
"type": "object",
"properties": {
"songs": {
"type": "array",
"items": {
"type": "object",
"properties": {
"start": {"type": "string"},
"end": {"type": "string"},
"title": {"type": "string"},
"artist": {"type": "string"},
"confidence": {"type": "number"},
"evidence": {"type": "string"}
},
"required": ["start", "end", "title", "artist", "confidence", "evidence"],
"additionalProperties": False
}
}
},
"required": ["songs"],
"additionalProperties": False
}
TASK_PROMPT = """你是音乐片段识别助手。当前目录下有一个字幕文件。
任务：
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）。
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：
- 歌曲开始时间应使用“上一句字幕的结束时间”作为 start_time。
- 这样可以尽量保留歌曲可能存在的前奏。
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.
4. 忽略纯聊天片段。
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。
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。
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
8.歌曲标注规则：
- 可以在歌曲名称后使用括号 () 添加补充说明。
- 常见标注示例：
- (片段)：歌曲演唱时间较短，例如 < 60 秒
- (清唱)：无伴奏演唱
- (副歌)：只演唱副歌部分
- 标注应简洁，仅在确有必要时使用。
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.
最后请严格按照 Schema 生成 JSON 数据。"""
# ==========================================
class SrtHandler(FileSystemEventHandler):
def on_created(self, event):
if not event.is_directory:
src_path = event.src_path
if isinstance(src_path, bytes):
src_path = src_path.decode('utf-8')
if src_path.endswith(DONE_FLAG):
logger.debug(f"检测到转录完成标记: {src_path}")
self.process_with_codex(Path(src_path))
def on_moved(self, event):
dest_path = event.dest_path
if isinstance(dest_path, bytes):
dest_path = dest_path.decode('utf-8')
if not event.is_directory and dest_path.lower().endswith('.srt'):
logger.debug(f"检测到字幕文件移动: {dest_path}")
self.process_with_codex(Path(dest_path))
def process_with_codex(self, srt_path):
work_dir = srt_path.parent
# 避免对同一目录重复调用
if (work_dir / "songs.json").exists():
logger.info(f"songs.json 已存在，跳过: {work_dir.name}")
return
logger.info(f"发现新任务，准备识别歌曲: {work_dir.name}")
# 创建AI日志
ai_log, ai_log_file = get_ai_logger('codex', 'songs')
ai_log.info("="*50)
ai_log.info("Codex 歌曲识别任务开始")
ai_log.info(f"工作目录: {work_dir}")
ai_log.info("="*50)
# 生成临时 Schema 文件
schema_file = work_dir / "song_schema.json"
with open(schema_file, "w", encoding="utf-8") as f:
json.dump(SONG_SCHEMA, f, ensure_ascii=False, indent=2)
# 构建命令行参数 (Linux 下必须使用列表形式)
cmd = [
CODEX_CMD, "exec",
TASK_PROMPT.replace('\n', ' '),
"--full-auto",
"--sandbox", "workspace-write",
"--output-schema", "./song_schema.json",
"-o", "songs.json",
"--skip-git-repo-check",
"--json"
]
logger.info("调用 Codex...")
ai_log.info(f"执行命令: {subprocess.list2cmdline(cmd)}")
try:
start_time = time.time()
# 关键修改：shell=False + 直接传列表，解决 "File name too long" 错误
result = subprocess.run(
cmd,
cwd=str(work_dir),
shell=False,
capture_output=True,
text=True,
encoding='utf-8'
)
elapsed = time.time() - start_time
ai_log.info(f"Codex 执行完成，耗时: {elapsed:.2f}秒")
# 记录输出
if result.stdout:
ai_log.info("=== STDOUT ===")
ai_log.info(result.stdout)
if result.stderr:
ai_log.warning("=== STDERR ===")
ai_log.warning(result.stderr)
if result.returncode == 0:
logger.info(f"Codex 执行成功: {work_dir.name}")
self.generate_txt_fallback(work_dir, ai_log)
else:
logger.error(f"Codex 失败，返回码: {result.returncode}")
ai_log.error(f"Codex 失败，返回码: {result.returncode}")
except Exception as e:
log_exception(logger, e, "Codex 调用异常")
log_exception(ai_log, e, "Codex 执行异常")
ai_log.info("="*50)
ai_log.info("Codex 歌曲识别任务完成")
ai_log.info("="*50)
def generate_txt_fallback(self, work_dir, ai_log):
json_path = work_dir / "songs.json"
txt_path = work_dir / "songs.txt"
try:
if json_path.exists():
with open(json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
songs = data.get('songs', [])
with open(txt_path, 'w', encoding='utf-8') as t:
for s in songs:
start_time = s['start'].split(',')[0].split('.')[0] # 兼容点号和逗号
line = f"{start_time} {s['title']} — {s['artist']}\n"
t.write(line)
logger.info(f"成功生成: {txt_path.name}")
except Exception as e:
log_exception(logger, e, "生成 txt 失败")
def main():
path = Path(SESSION_DIR)
path.mkdir(parents=True, exist_ok=True)
logger.info("="*50)
logger.info("字幕监控模块启动 (Linux 优化版)")
logger.info("="*50)
event_handler = SrtHandler()
# 启动扫描：检查是否有 flag 但没 songs.json 的存量目录
logger.info("正在扫描存量任务...")
for sub_dir in path.iterdir():
if sub_dir.is_dir():
flag = sub_dir / DONE_FLAG
json_file = sub_dir / "songs.json"
if flag.exists() and not json_file.exists():
logger.info(f"发现存量任务: {sub_dir.name}")
event_handler.process_with_codex(flag)
observer = Observer()
observer.schedule(event_handler, str(path), recursive=True)
observer.start()
try:
while True:
time.sleep(CHECK_INTERVAL)
except KeyboardInterrupt:
observer.stop()
observer.join()
if __name__ == "__main__":
main()