189 lines
7.2 KiB
Python
189 lines
7.2 KiB
Python
import os
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import time
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import math
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import shutil
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import subprocess
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import sys
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from pathlib import Path
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from groq import Groq
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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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GROQ_API_KEY = "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"
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LANGUAGE = "zh"
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KEEP_TEMP_AUDIO = False
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MAX_FILE_SIZE_MB = 23
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BITRATE_KBPS = 64
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MODEL_NAME = "whisper-large-v3-turbo"
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# 初始化日志
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logger = get_system_logger('video2srt')
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# ==========================================
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client = Groq(api_key=GROQ_API_KEY)
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def format_srt_time(seconds):
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"""秒转 SRT 时间戳格式 (00:00:00,000)"""
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td_hours = int(seconds // 3600)
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td_mins = int((seconds % 3600) // 60)
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td_secs = int(seconds % 60)
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td_millis = int((seconds - int(seconds)) * 1000)
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return f"{td_hours:02}:{td_mins:02}:{td_secs:02},{td_millis:03}"
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def transcribe_with_retry(audio_file, ai_log):
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"""处理 Groq API 速率限制的重试逻辑"""
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ai_log.info(f"开始转录音频: {audio_file.name}")
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ai_log.info(f"模型: {MODEL_NAME}, 语言: {LANGUAGE}")
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retry_count = 0
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while True:
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try:
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start_time = time.time()
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with open(audio_file, "rb") as file:
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response = client.audio.transcriptions.create(
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file=(audio_file.name, file.read()),
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model=MODEL_NAME,
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response_format="verbose_json",
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language=LANGUAGE,
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temperature=0.0
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)
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elapsed = time.time() - start_time
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ai_log.info(f"转录成功，耗时: {elapsed:.2f}秒")
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ai_log.info(f"识别到 {len(response.segments)} 个语音片段")
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return response.segments
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except Exception as e:
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retry_count += 1
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err_str = str(e)
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ai_log.error(f"转录失败 (尝试 {retry_count}): {err_str}")
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if "429" in err_str or "rate_limit" in err_str.lower():
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wait_time = 25
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ai_log.warning(f"触发 API 速率限制，等待 {wait_time} 秒后重试...")
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logger.warning(f"Groq API 速率限制，等待 {wait_time} 秒")
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time.sleep(wait_time)
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else:
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log_exception(ai_log, e, "Groq API 调用失败")
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raise e
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def process_single_video(raw_video_path, video_work_dir):
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"""
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1. 移动视频到工作区
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2. 提取音频并分片
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3. 转录生成 SRT
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"""
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raw_video_path = Path(raw_video_path)
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video_work_dir = Path(video_work_dir)
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video_work_dir.mkdir(parents=True, exist_ok=True)
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logger.info("="*50)
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logger.info(f"开始处理视频: {raw_video_path.name}")
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logger.info("="*50)
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# 创建AI日志
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ai_log, ai_log_file = get_ai_logger('groq', 'transcribe')
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ai_log.info("="*50)
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ai_log.info(f"Groq 转录任务开始")
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ai_log.info(f"视频文件: {raw_video_path.name}")
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ai_log.info(f"工作目录: {video_work_dir}")
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ai_log.info("="*50)
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# --- 新增步骤：将视频搬家到工作区 ---
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target_video_path = video_work_dir / raw_video_path.name
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if not target_video_path.exists():
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logger.info(f"移动视频至工作区: {raw_video_path.name}")
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ai_log.info(f"移动视频: {raw_video_path} -> {target_video_path}")
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shutil.move(str(raw_video_path), str(target_video_path))
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else:
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logger.info(f"视频已在工作区中: {target_video_path.name}")
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ai_log.info(f"视频已存在于工作区: {target_video_path}")
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video_stem = target_video_path.stem
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audio_temp_dir = video_work_dir / "temp_audio"
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audio_temp_dir.mkdir(parents=True, exist_ok=True)
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# 1. 计算切分时长
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seg_duration = math.floor((MAX_FILE_SIZE_MB * 8 * 1024) / BITRATE_KBPS)
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logger.info(f"音频分片时长: {seg_duration} 秒")
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ai_log.info(f"音频分片参数: {seg_duration}秒/片, 比特率: {BITRATE_KBPS}kbps")
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# 2. FFmpeg 提取并分片 (使用工作区内的视频路径)
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logger.info("开始提取音频...")
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ai_log.info("开始 FFmpeg 音频提取")
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output_pattern = str(audio_temp_dir / "part_%03d.mp3")
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cmd = [
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'ffmpeg', '-y', '-i', str(target_video_path),
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'-vn', '-acodec', 'libmp3lame', '-b:a', f'{BITRATE_KBPS}k',
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'-ac', '1', '-ar', '22050',
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'-f', 'segment', '-segment_time', str(seg_duration),
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'-reset_timestamps', '1', output_pattern
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]
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try:
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subprocess.run(cmd, check=True, capture_output=True)
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logger.info("音频提取完成")
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ai_log.info("FFmpeg 音频提取成功")
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except Exception as e:
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log_exception(logger, e, "FFmpeg 音频提取失败")
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log_exception(ai_log, e, "FFmpeg 执行失败")
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raise
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segments = sorted(list(audio_temp_dir.glob("part_*.mp3")))
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logger.info(f"音频分片数量: {len(segments)}")
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ai_log.info(f"生成音频分片: {len(segments)} 个")
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# 3. 转录并实时写入 SRT
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logger.info(f"开始分片转录...")
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ai_log.info("开始批量转录")
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srt_path = video_work_dir / f"{video_stem}.srt"
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global_idx = 1
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with open(srt_path, "w", encoding="utf-8") as srt_file:
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for i, seg in enumerate(segments):
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offset = i * seg_duration
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logger.info(f"转录进度: {i+1}/{len(segments)}")
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ai_log.info(f"转录片段 {i+1}/{len(segments)}: {seg.name}")
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seg_data = transcribe_with_retry(seg, ai_log)
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for chunk in seg_data:
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start = format_srt_time(chunk['start'] + offset)
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end = format_srt_time(chunk['end'] + offset)
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text = chunk['text'].strip()
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srt_file.write(f"{global_idx}\n{start} --> {end}\n{text}\n\n")
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global_idx += 1
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logger.info(f"字幕文件已生成: {srt_path.name}")
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ai_log.info(f"字幕生成完成: 共 {global_idx-1} 条字幕")
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# 4. 清理临时音频
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if not KEEP_TEMP_AUDIO:
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logger.info("清理临时音频文件...")
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ai_log.info("清理临时音频目录")
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shutil.rmtree(audio_temp_dir, ignore_errors=True)
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# --- 新增：生成转录完成标记 ---
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(video_work_dir / "transcribe_done.flag").touch()
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logger.info("生成转录完成标记")
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ai_log.info("生成 transcribe_done.flag")
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logger.info(f"处理完成。工作区目录: {video_work_dir}")
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ai_log.info("="*50)
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ai_log.info("Groq 转录任务完成")
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ai_log.info("="*50)
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logger.info(f"AI日志已保存: {ai_log_file}")
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if __name__ == "__main__":
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if len(sys.argv) >= 3:
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# sys.argv[1]: 原始视频路径 (通常在 stage 下)
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# sys.argv[2]: 目标工作区路径 (通常在 session 下)
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logger.info(f"接收到转录任务: {sys.argv[1]}")
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try:
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process_single_video(sys.argv[1], sys.argv[2])
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except Exception as e:
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log_exception(logger, e, "视频转录失败")
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raise
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else:
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logger.error("缺少参数。用法: python video2srt.py <视频路径> <工作区路径>")
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print("错误: 缺少参数。用法: python video2srt.py <视频路径> <工作区路径>") |