Merge pull request #77 from JSchmie/enhance_capability_with_webui

Enhance capability with webui

scraibe/autotranscript.py

@@ -95,7 +95,7 @@ class Scraibe:
         elif isinstance(dia_model, str):
             self.diariser = Diariser.load_model(dia_model, **kwargs)
         else:
-            self.diariser = dia_model
+            self.diariser : Diariser = dia_model
 
         if kwargs.get("verbose"):
             print("Scraibe initialized all models successfully loaded.")
@@ -133,7 +133,7 @@ class Scraibe:
         if kwargs.get("verbose"):
             self.verbose = kwargs.get("verbose")
         # Get audio file as an AudioProcessor object
-        audio_file = self.get_audio_file(audio_file)
+        audio_file : AudioProcessor = self.get_audio_file(audio_file)
         
         # Prepare waveform and sample rate for diarization
         dia_audio = {
@@ -203,7 +203,7 @@ class Scraibe:
         """
         
         # Get audio file as an AudioProcessor object
-        audio_file = self.get_audio_file(audio_file)
+        audio_file : AudioProcessor = self.get_audio_file(audio_file)
         
         # Prepare waveform and sample rate for diarization
         dia_audio = {
@@ -232,9 +232,56 @@ class Scraibe:
                 str:
                     The transcribed text from the audio source.
         """
-        audio_file = self.get_audio_file(audio_file)
+        audio_file : AudioProcessor  = self.get_audio_file(audio_file)
         
         return self.transcriber.transcribe(audio_file.waveform, **kwargs)      
+    
+    def update_transcriber(self, whisper_model : Union[str, whisper], **kwargs) -> None:
+        """
+        Update the transcriber model.
+
+        Args:
+            whisper_model (Union[str, whisper]):
+                The new whisper model to use for transcription.
+            **kwargs:
+                Additional keyword arguments for the transcriber model.
+                
+            Returns:
+                None
+        """
+        _old_model = self.transcriber.model_name
+        
+        if isinstance(whisper_model, str):
+            self.transcriber = Transcriber.load_model(whisper_model, **kwargs)
+        elif isinstance(whisper_model, Transcriber):
+            self.transcriber = whisper_model
+        else:
+            warn(f"Invalid model type. Please provide a valid model. Fallback to old {_old_model} Model.", RuntimeWarning)
+
+        return None
+
+    def update_diariser(self, dia_model : Union[str, DiarisationType], **kwargs) -> None:
+        """
+        Update the diariser model.
+
+        Args:
+            dia_model (Union[str, DiarisationType]):
+                The new diariser model to use for diarization.
+            **kwargs:
+                Additional keyword arguments for the diariser model.
+                
+            Returns:
+                None
+        """
+        if isinstance(dia_model, str):
+            self.diariser = Diariser.load_model(dia_model, **kwargs)
+        elif isinstance(dia_model, Diariser):
+            self.diariser = dia_model
+        else:
+            warn(f"Invalid model type. Please provide a valid model. Fallback to old Model.", RuntimeWarning)
+            
+        return None
+    
     @staticmethod
     def remove_audio_file(audio_file : str,
                           shred : bool = False) -> None:
@@ -269,7 +316,6 @@ class Scraibe:
             print(f"Audiofile {audio_file} removed.")
         
         
-    
     @staticmethod
     def get_audio_file(audio_file : Union[str, torch.Tensor, ndarray],
                         *args, **kwargs) -> AudioProcessor:
@@ -298,6 +344,7 @@ class Scraibe:
         if not isinstance(audio_file, AudioProcessor):
             raise ValueError(f'Audiofile must be of type AudioProcessor,' \
                              f'not {type(audio_file)}')     
+        
         return audio_file
 
     def __repr__(self):

scraibe/transcriber.py

@@ -64,14 +64,18 @@ class Transcriber:
         The class supports various sizes and versions of Whisper models. Please refer to
         the load_model method for available options.
     """
-    def __init__(self, model: whisper ) -> None:
+    def __init__(self, model: whisper , model_name: str  ) -> None:
         """
         Initialize the Transcriber class with a Whisper model.
 
         Args:
             model (whisper): The Whisper model to use for transcription.
+            model_name (str): The name of the model.
         """
+        
         self.model = model
+        
+        self.model_name = model_name
 
     def transcribe(self, audio : Union[str, Tensor, ndarray] ,
                    *args, **kwargs) -> str:
@@ -137,6 +141,7 @@ class Transcriber:
                         - 'medium'
                         - 'large-v1'
                         - 'large-v2'
+                        - 'large-v3'
                         - 'large'
                         
             download_root (str, optional): Path to download the model.
@@ -156,7 +161,7 @@ class Transcriber:
         _model = load_model(model, download_root=download_root,
                             device=device, in_memory=in_memory)
 
-        return cls(_model)
+        return cls(_model, model_name=model)
 
     @staticmethod
     def _get_whisper_kwargs(**kwargs) -> dict:
@@ -179,4 +184,4 @@ class Transcriber:
         return whisper_kwargs
     
     def __repr__(self) -> str:
-        return f"Transcriber(model={self.model})"
+        return f"Transcriber(model_name={self.model_name}, model={self.model})"