优化旁白问题
	
		
			
	
		
	
	
		
	
		
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	Gitea Actions Demo / Explore-Gitea-Actions (push) Successful in 2m54s
				
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				| @ -1,5 +1,35 @@ | ||||
| // 以流式方式请求LLM大模型接口,并打印流式返回内容
 | ||||
| 
 | ||||
| // 过滤旁白内容的函数
 | ||||
| function filterNarration(text) { | ||||
|   if (!text) return text; | ||||
|    | ||||
|   // 匹配各种括号内的旁白内容
 | ||||
|   // 包括:()、【】、[]、{}、〈〉、《》等
 | ||||
|   const narrationPatterns = [ | ||||
|     /([^)]*)/g,  // 中文圆括号
 | ||||
|     /\([^)]*\)/g,   // 英文圆括号
 | ||||
|     /【[^】]*】/g,   // 中文方括号
 | ||||
|     /\[[^\]]*\]/g,  // 英文方括号
 | ||||
|     /\{[^}]*\}/g,   // 花括号
 | ||||
|     /〈[^〉]*〉/g,   // 中文尖括号
 | ||||
|     /《[^》]*》/g,   // 中文书名号
 | ||||
|     /<[^>]*>/g     // 英文尖括号
 | ||||
|   ]; | ||||
|    | ||||
|   let filteredText = text; | ||||
|    | ||||
|   // 逐个应用过滤规则
 | ||||
|   narrationPatterns.forEach(pattern => { | ||||
|     filteredText = filteredText.replace(pattern, ''); | ||||
|   }); | ||||
|    | ||||
|   // 清理多余的空格和换行
 | ||||
|   filteredText = filteredText.replace(/\s+/g, ' ').trim(); | ||||
|    | ||||
|   return filteredText; | ||||
| } | ||||
| 
 | ||||
| async function requestLLMStream({ apiKey, model, messages, onSegment }) { | ||||
|   const response = await fetch('https://ark.cn-beijing.volces.com/api/v3/bots/chat/completions', { | ||||
|     method: 'POST', | ||||
| @ -54,7 +84,14 @@ async function requestLLMStream({ apiKey, model, messages, onSegment }) { | ||||
|             // 处理最后的待处理文本(无论长度是否大于5个字)
 | ||||
|             if (pendingText.trim() && onSegment) { | ||||
|               console.log('处理最后的待处理文本:', pendingText.trim()); | ||||
|               await onSegment(pendingText.trim(), true); | ||||
|               // 过滤旁白内容
 | ||||
|               const filteredText = filterNarration(pendingText.trim()); | ||||
|               if (filteredText.trim()) { | ||||
|                 console.log('过滤旁白后的最后文本:', filteredText); | ||||
|                 await onSegment(filteredText, true); | ||||
|               } else { | ||||
|                 console.log('最后的文本被完全过滤,跳过'); | ||||
|               } | ||||
|             } | ||||
|             continue; | ||||
|           } | ||||
| @ -67,10 +104,13 @@ async function requestLLMStream({ apiKey, model, messages, onSegment }) { | ||||
|               pendingText += deltaContent; | ||||
|               console.log('LLM内容片段:', deltaContent); | ||||
|                | ||||
|               // 检查是否包含分段分隔符
 | ||||
|               if (segmentDelimiters.test(pendingText)) { | ||||
|                 // 按分隔符分割文本
 | ||||
|                 const segments = pendingText.split(segmentDelimiters); | ||||
|               // 先过滤旁白,再检查分段分隔符
 | ||||
|               const filteredPendingText = filterNarration(pendingText); | ||||
|                | ||||
|               // 检查过滤后的文本是否包含分段分隔符
 | ||||
|               if (segmentDelimiters.test(filteredPendingText)) { | ||||
|                 // 按分隔符分割已过滤的文本
 | ||||
|                 const segments = filteredPendingText.split(segmentDelimiters); | ||||
|                  | ||||
|                 // 重新组合处理:只处理足够长的完整段落
 | ||||
|                 let accumulatedText = ''; | ||||
| @ -81,7 +121,7 @@ async function requestLLMStream({ apiKey, model, messages, onSegment }) { | ||||
|                   if (segment) { | ||||
|                     accumulatedText += segment; | ||||
|                     // 找到分隔符
 | ||||
|                     const delimiterMatch = pendingText.match(segmentDelimiters); | ||||
|                     const delimiterMatch = filteredPendingText.match(segmentDelimiters); | ||||
|                     if (delimiterMatch) { | ||||
|                       accumulatedText += delimiterMatch[0]; | ||||
|                     } | ||||
| @ -89,17 +129,22 @@ async function requestLLMStream({ apiKey, model, messages, onSegment }) { | ||||
|                     // 如果累积文本长度大于5个字,处理它
 | ||||
|                     if (accumulatedText.length > 8 && onSegment) { | ||||
|                       console.log('检测到完整段落:', accumulatedText); | ||||
|                       // 文本已经过滤过旁白,直接使用
 | ||||
|                       if (accumulatedText.trim()) { | ||||
|                         console.log('处理过滤后的文本:', accumulatedText); | ||||
|                         await onSegment(accumulatedText, false); | ||||
|                       } | ||||
|                       hasProcessed = true; | ||||
|                       accumulatedText = ''; // 重置
 | ||||
|                     } | ||||
|                   } | ||||
|                 } | ||||
|                  | ||||
|                 // 更新pendingText
 | ||||
|                 // 更新pendingText - 使用原始文本但需要相应调整
 | ||||
|                 if (hasProcessed) { | ||||
|                   // 保留未处理的累积文本和最后一个不完整段落
 | ||||
|                   pendingText = accumulatedText + (segments[segments.length - 1] || ''); | ||||
|                   // 计算已处理的原始文本长度,更新pendingText
 | ||||
|                   const processedLength = pendingText.length - (segments[segments.length - 1] || '').length; | ||||
|                   pendingText = pendingText.substring(processedLength); | ||||
|                 } | ||||
|               } | ||||
|             } | ||||
|  | ||||
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