Include loading of default value of file_format from config file.

app.py

@@ -53,7 +53,7 @@ def create_pipeline_inputs(self):
             dd_lang = gr.Dropdown(choices=self.whisper_inf.available_langs + [AUTOMATIC_DETECTION],
                                   value=AUTOMATIC_DETECTION if whisper_params["lang"] == AUTOMATIC_DETECTION.unwrap()
                                   else whisper_params["lang"], label=_("Language"))
-            dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt", "LRC"], value="SRT", label=_("File Format"))
+            dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt", "LRC"], value=whisper_params["file_format"], label=_("File Format"))
         with gr.Row():
             cb_translate = gr.Checkbox(value=whisper_params["is_translate"], label=_("Translate to English?"),
                                        interactive=True)

configs/default_parameters.yaml

@@ -1,5 +1,6 @@
 whisper:
   model_size: "large-v2"
+  file_format: "SRT"
   lang: "Automatic Detection"
   is_translate: false
   beam_size: 5

modules/whisper/base_transcription_pipeline.py

@@ -71,6 +71,7 @@ def update_model(self,
     def run(self,
             audio: Union[str, BinaryIO, np.ndarray],
             progress: gr.Progress = gr.Progress(),
+            file_format: str = "SRT",
             add_timestamp: bool = True,
             *pipeline_params,
             ) -> Tuple[List[Segment], float]:
@@ -86,6 +87,8 @@ def run(self,
             Audio input. This can be file path or binary type.
         progress: gr.Progress
             Indicator to show progress directly in gradio.
+        file_format: str
+            Subtitle file format between ["SRT", "WebVTT", "txt", "lrc"]
         add_timestamp: bool
             Whether to add a timestamp at the end of the filename.
         *pipeline_params: tuple
@@ -168,6 +171,7 @@ def run(self,
 
         self.cache_parameters(
             params=params,
+            file_format=file_format,
             add_timestamp=add_timestamp
         )
         return result, elapsed_time
@@ -224,6 +228,7 @@ def transcribe_file(self,
                 transcribed_segments, time_for_task = self.run(
                     file,
                     progress,
+                    file_format,
                     add_timestamp,
                     *pipeline_params,
                 )
@@ -298,6 +303,7 @@ def transcribe_mic(self,
             transcribed_segments, time_for_task = self.run(
                 mic_audio,
                 progress,
+                file_format,
                 add_timestamp,
                 *pipeline_params,
             )
@@ -364,6 +370,7 @@ def transcribe_youtube(self,
             transcribed_segments, time_for_task = self.run(
                 audio,
                 progress,
+                file_format,
                 add_timestamp,
                 *pipeline_params,
             )
@@ -513,14 +520,16 @@ def validate_gradio_values(params: TranscriptionPipelineParams):
     @staticmethod
     def cache_parameters(
         params: TranscriptionPipelineParams,
-        add_timestamp: bool
+        file_format: str = "SRT",
+        add_timestamp: bool = True
     ):
         """Cache parameters to the yaml file"""
         cached_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)
         param_to_cache = params.to_dict()
 
         cached_yaml = {**cached_params, **param_to_cache}
         cached_yaml["whisper"]["add_timestamp"] = add_timestamp
+        cached_yaml["whisper"]["file_format"] = file_format
 
         supress_token = cached_yaml["whisper"].get("suppress_tokens", None)
         if supress_token and isinstance(supress_token, list):