fix

app/core/subtitle_processor/spliter.py

@@ -8,7 +8,7 @@
 from ..bk_asr.ASRData import ASRData, from_srt, ASRDataSeg
 from ..utils.logger import setup_logger
 
-logger = setup_logger("subtitle_spliter", level=logging.DEBUG)
+logger = setup_logger("subtitle_spliter")
 
 SEGMENT_THRESHOLD = 500  # 每个分段的最大字数
 FIXED_NUM_THREADS = 4  # 固定的线程数量
@@ -145,12 +145,12 @@ def merge_segments_based_on_sentences(asr_data: ASRData, sentences: List[str]) -
                     new_segments.extend(split_segs)
                 else:
                     new_segments.append(merged_seg)
-            max_shift = 100
+            max_shift = 30
             asr_index = end_seg_index + 1  # 移动到下一个未处理的分段
         else:
             logger.warning(f"无法匹配句子: {sentence}")
-            max_shift = 30
-            asr_index += 1
+            max_shift = 100
+            asr_index = end_seg_index + 1
 
     return ASRData(new_segments)
 
@@ -243,7 +243,6 @@ def split_asr_data(asr_data: ASRData, num_segments: int) -> List[ASRData]:
     """
     total_segs = len(asr_data.segments)
     total_word_count = count_words(asr_data.to_txt())
-    print(f"总字数: {total_word_count}")
     words_per_segment = total_word_count // num_segments
     split_indices = []
 
@@ -349,7 +348,6 @@ def process_segment(asr_data_part):
             txt = asr_data_part.to_txt().replace("\n", "")
             sentences = split_by_llm(txt, model=model, use_cache=True)
             logger.info(f"分段的句子提取完成，共 {len(sentences)} 句")
-            print(f"sentences: {sentences}")
 
             return sentences
 

app/core/thread/transcript_thread.py

@@ -99,10 +99,13 @@ def run(self):
             logger.info("字幕文件已保存到: %s", str(original_subtitle_path))
 
             # 删除音频文件 和 封面
-            audio_save_path.unlink()
-            thumbnail_path = Path(self.task.video_info.thumbnail_path)
-            if thumbnail_path.exists():
-                thumbnail_path.unlink()
+            try:
+                audio_save_path.unlink()
+                thumbnail_path = Path(self.task.video_info.thumbnail_path)
+                if thumbnail_path.exists():
+                    thumbnail_path.unlink()
+            except Exception as e:
+                logger.error("删除音频文件或封面失败: %s", str(e))
 
             self.progress.emit(100, self.tr("转录完成"))
             self.finished.emit(self.task)

app/view/subtitle_style_interface.py

@@ -315,6 +315,11 @@ def _initStyle(self):
 
     def __setValues(self):
         """设置初始值"""
+        # 设置字幕排布
+        self.layoutCard.comboBox.setCurrentText(cfg.get(cfg.subtitle_layout))
+        # 设置字幕样式
+        self.styleNameComboBox.comboBox.setCurrentText(cfg.get(cfg.subtitle_style_name))
+
         # 获取系统字体,设置comboBox的选项
         fontDatabase = QFontDatabase()
         fontFamilies = fontDatabase.families()
@@ -339,9 +344,6 @@ def __setValues(self):
             self.loadStyle(style_files[0])
             self.styleNameComboBox.comboBox.setCurrentText(style_files[0])
 
-        # 设置字幕排布
-        self.layoutCard.comboBox.setCurrentText(cfg.get(cfg.subtitle_layout))
-
     def connectSignals(self):
         """连接所有设置变更的信号到预览更新函数"""
         # 字幕排布

app/view/task_creation_interface.py

@@ -256,12 +256,13 @@ def setup_values(self):
             self.transcription_model_card.value() == TranscribeModelEnum.WHISPER.value or
             self.transcription_model_card.value() == TranscribeModelEnum.WHISPER_API.value
         )
-        InfoBar.warning(
-            self.tr("警告，将使用自带小模型API"),
-            self.tr("为确保字幕修正的准确性，建议到设置中配置自己的API"),
-            duration=8000,
-            parent=self,
-            position=InfoBarPosition.BOTTOM_RIGHT
+        if cfg.api_base == "":
+            InfoBar.warning(
+                self.tr("警告"),
+                self.tr("为确保字幕修正的准确性，建议到设置中配置自己的API"),
+                duration=6000,
+                parent=self,
+                position=InfoBarPosition.BOTTOM_RIGHT
         )
 
     def on_transcription_model_changed(self, value):