Update modules/whisper/whisper_base.py

modules/whisper/whisper_base.py

@@ -296,9 +296,6 @@ class WhisperBase(ABC):
             file_count_total = 0
             process_multiple = False
             files = ""
-            
-            char_tab = "#TAB#"
-            char_newline = "#NEWLINE#"
 
             if input_multi == "Audio":
                 files = files_audio
@@ -414,16 +411,16 @@ class WhisperBase(ABC):
                 
                 # Add filename & info as first line
                 title_line = ""
-                tabbed_space = char_tab*3
-                if add_timestamp_preview: title_line += char_tab
-                if diarize_speakers: title_line += char_tab
-                title_line += f'{tabbed_space}Media file:{char_tab}{info["input_source_file"]}'
-                title_line += f'{char_newline}{tabbed_space}Language:{char_tab}{info["lang"]} (probability {info["lang_prob"]}%)'
-                if params.is_translate: title_line += f'{char_newline}{tabbed_space}Translation:{char_tab}{info["transcription"]} (Handled by OpenAI Whisper)'
-                if translate_output: title_line += f'{char_newline}{tabbed_space}Translation:{char_tab}{info["translation"]} (Handled by Facebook NLLB)'
-                title_line += char_newline
-
-                total_result += title_line+info["subtitle"]
+                tabbed_space = "\t"*3
+                if add_timestamp_preview: title_line += "#TAB#"
+                if diarize_speakers: title_line += "#TAB#"
+                title_line += f'{tabbed_space}Media file:"\t{info["input_source_file"]}'
+                title_line += f'\n{tabbed_space}Language:\t{info["lang"]} (probability {info["lang_prob"]}%)'
+                if params.is_translate: title_line += f'\n{tabbed_space}Translation:\t{info["transcription"]} (Handled by OpenAI Whisper)'
+                if translate_output: title_line += f'\n{tabbed_space}Translation:\t{info["translation"]} (Handled by Facebook NLLB)'
+                title_line += "#NEWLINE#"
+
+                total_result += title_line+(info["subtitle"].replace("\t","#TAB#")).replace("\n","#NEWLINE#"))
                 total_time += info["time_for_task"]
             
             time_end = datetime.now()
@@ -431,8 +428,6 @@ class WhisperBase(ABC):
             #total_info += f"Total processing time:\t{self.format_time((time_end-time_start).total_seconds())}"
             
             result_str = self.transform_text_to_list(total_result) #Transform to gr.Dataframe format
-            result_str = result_str.replace(char_tab,"\t")
-            result_str = result_str.replace(char_newline,"\n")
             result_file_path = [info['path'] for info in files_to_download.values()]
 
             return [result_str,result_file_path,total_info]
@@ -630,10 +625,10 @@ class WhisperBase(ABC):
     @staticmethod
     def transform_text_to_list(inputdata: str) -> list:
         outputdata = []
-        temp_inputdata = inputdata.split("\n")
+        temp_inputdata = inputdata.split("#NEWLINE#")
         for temp_line in temp_inputdata:
             temp_line_list = []	
-            temp_line_items = temp_line.split("\t")
+            temp_line_items = temp_line.split("#TAB#")
             for temp_line_item in temp_line_items:
                 temp_line_list.append(temp_line_item)	
             outputdata.append(temp_line_list)