432 lines
18 KiB
Python
432 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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音视频处理器 - MaxKB集成层
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"""
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from typing import List
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from common.handle.base_split_handle import BaseSplitHandle
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from common.utils.logger import maxkb_logger
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class MediaSplitHandle(BaseSplitHandle):
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"""
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音视频处理器 - MaxKB集成层
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"""
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def __init__(self):
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super().__init__()
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self.adapter = None
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def support(self, file, get_buffer, **kwargs):
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"""检查是否支持该文件类型"""
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file_name = file.name.lower()
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# 支持的音频格式
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audio_exts = ('.mp3', '.wav', '.m4a', '.flac', '.aac', '.ogg', '.wma')
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# 支持的视频格式
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video_exts = ('.mp4', '.avi', '.mov', '.mkv', '.webm', '.flv', '.wmv')
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return any(file_name.endswith(ext) for ext in audio_exts + video_exts)
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def handle(self, file, pattern_list: List, with_filter: bool, limit: int,
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get_buffer, save_image, **kwargs):
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"""处理音视频文件"""
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maxkb_logger.info(f"MediaSplitHandle.handle called with file: {file.name}")
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maxkb_logger.info(f"Split parameters - limit: {limit}, patterns: {pattern_list}, with_filter: {with_filter}")
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# 检查是否需要实际处理
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use_actual_processing = kwargs.get('use_actual_processing', False)
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stt_model_id = kwargs.get('stt_model_id')
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if use_actual_processing and stt_model_id:
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# 进行实际处理
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result = self._handle_actual_processing(file, get_buffer, **kwargs)
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# 应用智能分块（如果需要）
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chunk_limit = limit if limit > 0 else 1000 # 默认1000字符
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if len(result.get('content', [])) > 0:
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result = self._apply_smart_split(result, chunk_limit, with_filter)
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return result
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else:
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# 使用默认文本
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result = self._handle_default_text(file, **kwargs)
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# 即使是默认文本也应用分块（如果有limit参数）
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if limit > 0:
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result = self._apply_smart_split(result, limit, with_filter)
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return result
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def _get_audio_default_segments(self, file_name: str) -> List[dict]:
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"""生成音频文件的默认段落"""
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base_name = file_name.split('.')[0]
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return [
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{
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'title': '开场介绍',
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'content': f'这是音频文件 "{base_name}" 的第一段内容演示。本段包含了会议的开场介绍和主要议题的说明。\n\n主要内容：\n- 会议目的和议程说明\n- 参会人员介绍\n- 会议背景和重要性\n- 预期成果和目标设定',
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'start_time': 0,
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'end_time': 180
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},
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{
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'title': '主要内容讨论',
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'content': f'这是音频文件 "{base_name}" 的第二段内容演示。本段详细讨论了项目的进展情况和下一步的工作计划。\n\n主要内容：\n- 项目当前进展汇报\n- 关键问题和挑战分析\n- 解决方案讨论\n- 资源需求和分配',
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'start_time': 180,
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'end_time': 360
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},
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{
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'title': '总结与行动项',
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'content': f'这是音频文件 "{base_name}" 的第三段内容演示。本段总结了会议的主要结论和行动项，明确了责任人和时间节点。\n\n主要内容：\n- 会议要点总结\n- 行动项和责任分配\n- 时间节点和里程碑\n- 后续跟进计划',
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'start_time': 360,
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'end_time': 540
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}
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]
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def _get_video_default_segments(self, file_name: str) -> List[dict]:
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"""生成视频文件的默认段落"""
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base_name = file_name.split('.')[0]
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return [
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{
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'title': '开场介绍',
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'content': f'这是视频文件 "{base_name}" 的第一段内容演示。本段包含了视频的开场介绍和主要内容概述。\n\n主要内容：\n- 产品/服务介绍\n- 功能特性概述\n- 目标用户群体\n- 使用场景说明',
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'start_time': 0,
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'end_time': 120
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},
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{
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'title': '功能演示',
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'content': f'这是视频文件 "{base_name}" 的第二段内容演示。本段详细展示了产品的功能特性和使用方法。\n\n主要内容：\n- 核心功能演示\n- 操作步骤说明\n- 使用技巧和注意事项\n- 常见问题解答',
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'start_time': 120,
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'end_time': 300
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},
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{
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'title': '总结与联系方式',
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'content': f'这是视频文件 "{base_name}" 的第三段内容演示。本段总结了产品的主要优势和适用场景，提供了联系方式。\n\n主要内容：\n- 产品优势总结\n- 价格和套餐信息\n- 适用场景和行业\n- 联系方式和售后服务',
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'start_time': 300,
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'end_time': 420
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}
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]
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def _get_media_default_segments(self, file_name: str) -> List[dict]:
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"""生成其他媒体文件的默认段落"""
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base_name = file_name.split('.')[0]
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return [
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{
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'title': '文件概述',
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'content': f'这是媒体文件 "{base_name}" 的第一段内容演示。本段包含了文件的基本信息和主要内容概述。\n\n主要内容：\n- 文件基本信息\n- 内容类型说明\n- 主要用途和价值\n- 处理建议和注意事项',
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'start_time': 0,
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'end_time': 120
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},
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{
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'title': '详细内容',
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'content': f'这是媒体文件 "{base_name}" 的第二段内容演示。本段详细介绍了文件的核心内容和关键信息。\n\n主要内容：\n- 核心内容分析\n- 关键信息提取\n- 重要要点总结\n- 后续处理建议',
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'start_time': 120,
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'end_time': 240
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}
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]
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def _handle_default_text(self, file, **kwargs) -> dict:
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"""使用默认文本处理音视频文件"""
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maxkb_logger.info(f"Using default text for media processing: {file.name}")
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# 获取文件名和类型
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file_name = file.name
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file_ext = file_name.lower().split('.')[-1]
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# 判断媒体类型
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audio_exts = {'mp3', 'wav', 'm4a', 'flac', 'aac', 'ogg', 'wma'}
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video_exts = {'mp4', 'avi', 'mov', 'mkv', 'webm', 'flv', 'wmv'}
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if file_ext in audio_exts:
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media_type = "音频"
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default_segments = self._get_audio_default_segments(file_name)
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elif file_ext in video_exts:
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media_type = "视频"
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default_segments = self._get_video_default_segments(file_name)
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else:
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media_type = "媒体"
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default_segments = self._get_media_default_segments(file_name)
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maxkb_logger.info(f"Processing {media_type} file: {file_name}")
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maxkb_logger.info(f"Generating {len(default_segments)} default segments")
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# 转换为MaxKB段落格式
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paragraphs = []
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for i, segment_data in enumerate(default_segments):
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paragraph = {
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'content': segment_data['content'],
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'title': segment_data['title'],
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'metadata': {
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'start_time': segment_data.get('start_time'),
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'end_time': segment_data.get('end_time'),
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'index': i,
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'is_demo': True,
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'media_type': media_type,
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'file_name': file_name
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}
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}
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paragraphs.append(paragraph)
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# 添加处理元数据
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metadata = {
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'media_processing_status': 'success',
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'media_type': media_type,
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'is_demo_content': True,
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'processing_mode': 'default_text'
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}
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maxkb_logger.info(f"Successfully created {len(paragraphs)} default paragraphs for {file_name}")
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return {
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'name': file.name,
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'content': paragraphs,
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'metadata': metadata
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}
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def _handle_actual_processing(self, file, get_buffer, **kwargs) -> dict:
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"""实际处理音视频文件"""
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maxkb_logger.info(f"Starting actual processing for media file: {file.name}")
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# 初始化适配器
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if not self.adapter:
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from .media_adapter import MediaAdapter
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from .media_adapter.logger import MediaLogger
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logger_wrapper = MediaLogger(maxkb_logger)
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self.adapter = MediaAdapter(logger=logger_wrapper)
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# 获取文件内容
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buffer = get_buffer(file)
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# 获取模型ID和工作空间ID
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stt_model_id = kwargs.get('stt_model_id')
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llm_model_id = kwargs.get('llm_model_id')
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workspace_id = kwargs.get('workspace_id')
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maxkb_logger.info(f"Extracted from kwargs - stt_model_id: {stt_model_id}, llm_model_id: {llm_model_id}, workspace_id: {workspace_id}")
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# 处理选项
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options_param = kwargs.get('options', {})
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options = {
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'language': options_param.get('language', kwargs.get('language', 'auto')),
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'segment_duration': options_param.get('segment_duration', kwargs.get('segment_duration', 300)),
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'enable_punctuation': options_param.get('enable_punctuation', kwargs.get('enable_punctuation', True)),
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'enable_summary': True,
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'extract_keyframes': options_param.get('extract_keyframes', kwargs.get('extract_keyframes', False))
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}
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try:
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# 调用适配器处理
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result = self.adapter.process_media(
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file_content=buffer,
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file_name=file.name,
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stt_model_id=stt_model_id,
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llm_model_id=llm_model_id,
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workspace_id=workspace_id,
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options=options
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)
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# 转换为MaxKB段落格式
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paragraphs = []
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for segment in result.get('segments', []):
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# 使用优化后的文本（如果有）
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text = segment.get('enhanced_text', segment.get('text', ''))
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# 添加时间戳信息
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if segment.get('start_time') is not None:
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time_info = f"[{self._format_time(segment['start_time'])} - {self._format_time(segment['end_time'])}]"
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text = f"{time_info}\n{text}"
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# 添加摘要（如果有）
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if segment.get('summary'):
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text = f"## 摘要\n\n{segment['summary']}\n\n---\n\n{text}"
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maxkb_logger.info(f"Adding summary to paragraph: {segment['summary'][:50]}...")
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paragraph = {
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'content': text,
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'title': f"段落 {segment.get('index', 0) + 1}",
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'metadata': {
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'start_time': segment.get('start_time'),
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'end_time': segment.get('end_time'),
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'index': segment.get('index'),
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'is_demo': False,
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'media_type': 'actual'
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}
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}
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paragraphs.append(paragraph)
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# 添加成功处理的标记
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metadata = result.get('metadata', {})
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metadata['media_processing_status'] = 'success'
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metadata['is_demo_content'] = False
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metadata['processing_mode'] = 'actual_processing'
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maxkb_logger.info(f"Successfully processed {file.name}, generated {len(paragraphs)} actual paragraphs")
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return {
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'name': file.name,
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'content': paragraphs,
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'metadata': metadata
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}
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except Exception as e:
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maxkb_logger.error(f"实际处理音视频文件失败: {str(e)}")
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# 返回错误信息
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return {
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'name': file.name,
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'content': [{
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'content': f'实际处理失败: {str(e)}',
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'title': '错误'
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}],
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'metadata': {'error': str(e), 'media_processing_status': 'failed'}
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}
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def get_content(self, file, save_image):
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"""获取文件内容（用于预览）"""
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try:
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file_name = file.name
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# 判断媒体类型
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file_ext = file_name.lower().split('.')[-1]
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video_exts = {'mp4', 'avi', 'mov', 'mkv', 'webm', 'flv', 'wmv'}
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if file_ext in video_exts:
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return f"[视频文件: {file_name}]\n\n该文件需要进行音频提取和语音识别处理。"
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else:
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return f"[音频文件: {file_name}]\n\n该文件需要进行语音识别处理。"
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except Exception as e:
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return f"读取文件失败: {str(e)}"
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def _format_time(self, seconds: float) -> str:
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"""格式化时间"""
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if seconds is None:
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return "00:00"
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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secs = int(seconds % 60)
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if hours > 0:
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return f"{hours:02d}:{minutes:02d}:{secs:02d}"
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else:
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return f"{minutes:02d}:{secs:02d}"
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def smart_split_transcription(self, text, limit=1000, overlap=100):
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"""智能分割转录文本"""
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if len(text) <= limit:
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return [text]
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# 预处理：移除过多的空行
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text = '\n'.join(line for line in text.split('\n') if line.strip())
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chunks = []
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start = 0
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while start < len(text):
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# 计算当前块的结束位置
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end = min(start + limit, len(text))
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# 如果不是最后一块，尝试在句号处断开
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if end < len(text):
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# 寻找最佳断点
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best_end = end
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# 优先在句号处断开
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last_period = text.rfind('。', start, end)
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if last_period > start + limit // 2:
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best_end = last_period + 1
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else:
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# 其次在换行符处断开
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last_newline = text.rfind('\n', start, end)
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if last_newline > start + limit // 3:
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best_end = last_newline + 1
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end = best_end
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# 提取当前块
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chunk = text[start:end].strip()
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if chunk:
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chunks.append(chunk)
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# 计算下一块的开始位置（考虑重叠）
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start = max(end - overlap, len(chunks[-1]) if chunks else 0)
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# 避免无限循环
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if start >= len(text):
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break
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return chunks
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def _apply_smart_split(self, result: dict, limit: int = 1000, with_filter: bool = False):
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"""应用智能分块到转录结果"""
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overlap = 100 # 前后重叠字符数
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new_paragraphs = []
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for paragraph in result.get('content', []):
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content = paragraph.get('content', '')
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# 应用文本过滤（如果需要）
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if with_filter:
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content = self._clean_text(content)
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if content:
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# 应用智能分块
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chunks = self.smart_split_transcription(content, limit, overlap)
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# 如果只有一个块且没有变化，保持原样
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if len(chunks) == 1 and chunks[0] == content:
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new_paragraphs.append(paragraph)
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else:
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# 创建新的段落
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for idx, chunk in enumerate(chunks):
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# 保留原始元数据，但更新分段相关信息
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metadata = paragraph.get('metadata', {}).copy()
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metadata.update({
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'chunk_index': idx,
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'total_chunks': len(chunks),
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'split_method': 'smart_transcription',
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'split_limit': limit,
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'split_overlap': overlap,
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'with_filter': with_filter
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})
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new_paragraph = {
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'content': chunk,
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'title': f"{paragraph.get('title', '段落')} - 第{idx + 1}部分" if len(chunks) > 1 else paragraph.get('title', '段落'),
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'metadata': metadata
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}
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new_paragraphs.append(new_paragraph)
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else:
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new_paragraphs.append(paragraph)
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# 更新结果
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result['content'] = new_paragraphs
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# 更新元数据
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metadata = result.get('metadata', {})
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metadata['smart_split_applied'] = True
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metadata['total_chunks'] = len(new_paragraphs)
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result['metadata'] = metadata
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maxkb_logger.info(f"Applied smart transcription split: {len(new_paragraphs)} chunks")
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return result
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def _clean_text(self, text):
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"""清理文本：去掉重复多余符号空格、空行、制表符"""
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import re
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# 移除多余的空白字符
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text = re.sub(r'\s+', ' ', text)
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# 移除开头和结尾的空白
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text = text.strip()
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# 移除重复的标点符号
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text = re.sub(r'([。！？，])\1+', r'\1', text)
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# 移除多余的换行
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text = re.sub(r'\n{3,}', '\n\n', text)
|
||
|
||
return text |