Auto fixes from PEP8, fixes from flake8.

scraibe/transcriber.py

@@ -28,11 +28,11 @@ from whisper import Whisper
 from whisper import load_model as whisper_load_model
 from whisperx.asr import WhisperModel
 from whisperx import load_model as whisperx_load_model
-from typing import TypeVar , Union , Optional
+from typing import TypeVar, Union, Optional
 from torch import Tensor, device
 from numpy import ndarray
 from inspect import getfullargspec
-from abc import ABC, abstractmethod
+from abc import abstractmethod
 
 from .misc import WHISPER_DEFAULT_PATH
 whisper = TypeVar('whisper')
@@ -66,6 +66,7 @@ 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, model_name: str) -> None:
         """
         Initialize the Transcriber class with a Whisper model.
@@ -74,13 +75,13 @@ class Transcriber:
             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
 
     @abstractmethod
-    def transcribe(self, audio: Union[str, Tensor, ndarray] ,
+    def transcribe(self, audio: Union[str, Tensor, ndarray],
                    *args, **kwargs) -> str:
         """
         Transcribe an audio file.
@@ -95,9 +96,9 @@ class Transcriber:
             str: The transcript as a string.
         """
         pass
-    
+
     @staticmethod
-    def save_transcript(transcript : str , save_path : str) -> None:
+    def save_transcript(transcript: str, save_path: str) -> None:
         """
         Save a transcript to a file.
 
@@ -111,7 +112,7 @@ class Transcriber:
 
         with open(save_path, 'w') as f:
             f.write(transcript)
-            
+
         print(f'Transcript saved to {save_path}')
 
     @classmethod
@@ -176,10 +177,10 @@ class Transcriber:
             dict: Keyword arguments for whisper model.
         """
         pass
-    
+
     def __repr__(self) -> str:
         return f"Transcriber(model_name={self.model_name}, model={self.model})"
-    
+
 
 class WhisperTranscriber(Transcriber):
     def __init__(self, model: whisper, model_name: str) -> None:
@@ -233,10 +234,10 @@ class WhisperTranscriber(Transcriber):
                         - 'large-v2'
                         - 'large-v3'
                         - 'large'
-                        
+
             download_root (str, optional): Path to download the model.
                                             Defaults to WHISPER_DEFAULT_PATH.
-                                            
+
             device (Optional[Union[str, torch.device]], optional): 
                                         Device to load model on. Defaults to None.
             in_memory (bool, optional): Whether to load model in memory. 
@@ -266,7 +267,8 @@ class WhisperTranscriber(Transcriber):
         _kwargs = getfullargspec(Whisper.transcribe).kwonlyargs
         _possible_kwargs = _args + _kwargs
 
-        whisper_kwargs = {k: v for k, v in kwargs.items() if k in _possible_kwargs}
+        whisper_kwargs = {k: v for k,
+                          v in kwargs.items() if k in _possible_kwargs}
 
         if (task := kwargs.get("task")):
             whisper_kwargs["task"] = task
@@ -280,7 +282,7 @@ class WhisperTranscriber(Transcriber):
 class WhisperXTranscriber(Transcriber):
     def __init__(self, model: whisper, model_name: str) -> None:
         super().__init__(model, model_name)
-        
+
     def transcribe(self, audio: Union[str, Tensor, ndarray],
                    *args, **kwargs) -> str:
         """
@@ -296,7 +298,7 @@ class WhisperXTranscriber(Transcriber):
             str: The transcript as a string.
         """
         kwargs = self._get_whisper_kwargs(**kwargs)
-        
+
         if isinstance(audio, Tensor):
             audio = audio.cpu().numpy()
         result = self.model.transcribe(audio, *args, **kwargs)
@@ -304,8 +306,7 @@ class WhisperXTranscriber(Transcriber):
         for seg in result['segments']:
             text += seg['text']
         return text
-        
-        
+
     @classmethod
     def load_model(cls,
                    model: str = "medium",
@@ -330,10 +331,10 @@ class WhisperXTranscriber(Transcriber):
                         - 'large-v2'
                         - 'large-v3'
                         - 'large'
-                        
+
             download_root (str, optional): Path to download the model.
                                             Defaults to WHISPER_DEFAULT_PATH.
-                                            
+
             device (Optional[Union[str, torch.device]], optional): 
                                         Device to load model on. Defaults to None.
             in_memory (bool, optional): Whether to load model in memory. 
@@ -364,7 +365,8 @@ class WhisperXTranscriber(Transcriber):
         _kwargs = getfullargspec(WhisperModel.transcribe).kwonlyargs
         _possible_kwargs = _args + _kwargs
 
-        whisper_kwargs = {k: v for k, v in kwargs.items() if k in _possible_kwargs}
+        whisper_kwargs = {k: v for k,
+                          v in kwargs.items() if k in _possible_kwargs}
 
         if (task := kwargs.get("task")):
             whisper_kwargs["task"] = task