removed comments

autotranscript/diarisation.py

@@ -1,7 +1,7 @@
 from pyannote.audio import Pipeline
-from time import time
+from torch import Tensor
 import os
-from typing import TypeVar
+from typing import TypeVar, Union
 
 Annotation = TypeVar('Annotation') 
 
@@ -9,15 +9,16 @@ PYANNOTE_DEFAULT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)),
                                      "models", "pyannote", 
                                      "speaker_diarization", "config.yaml")
 
-class Diarisation:
+class Diariser:
     def __init__(self, model,*args,**kwargs) -> None:
 
         self.model = model
 
-    def diarization(self, audiofile : str , *args, **kwargs) -> Annotation:
+    def diarization(self, audiofile : Union[str, Tensor] ,
+                    *args, **kwargs) -> Annotation:
         """
         Diarization of audio file
-        :param audiofile: path to audio file
+        :param audiofile: path to audio file or torch.Tensor
         :param args: args for diarization model 
         :param kwargs: kwargs for diarization model
         :return: diarization
@@ -83,17 +84,21 @@ class Diarisation:
             diarization_output["speakers"].append(outp[2])
 
         return diarization_output
+    
     @staticmethod
     def _get_token():
         # check ig .pyannotetoken.txt exists
-        path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '.pyannotetoken')
+        path = os.path.join(os.path.dirname(
+            os.path.realpath(__file__)), '.pyannotetoken')
         if os.path.exists(path):
             with open(path, 'r') as f:
                 token = f.read()
         else:
-            raise ValueError('No token found. Please create a token at https://huggingface.co/settings/token'
+            raise ValueError('No token found.' \
+                'Please create a token at https://huggingface.co/settings/token' \
                 'and save it in a file called .pyannotetoken.txt')
         return token
+    
     @classmethod
     def load_model(cls, model: str = PYANNOTE_DEFAULT_PATH, 
                         token: str = "",
@@ -129,69 +134,7 @@ class Diarisation:
 
     def __repr__(self):
         return f"Diarisation(model={self.model})"
+    
     def __str__(self):
         return f"Diarisation(model={self.model})"
 
-
-if __name__ == '__main__':
-
-    model = Diarisation.load_model()
-    print(model)
-    audiofile = "/home/jacob/PycharmProjects/autotranscript/tests/test.wav"
-    out = model.diarization(audiofile)
-
-    # # deprecated
-    # def create_temporary_wav(self, location_of_temp_folder : str = '.temp'):
-    #     """
-    #     Create temporary wav file for diarization
-    #     :param location_of_temp_folder: folder to save the temporary wav file
-    #         default: .temp
-    #     :param savename: name of the temporary wav file prefix
-    #     :param audiofile: audio file
-    #     :return: temporary wav file
-    #     """
-    #     print("Linne 84 Diarisation.py create_temporary_wav :" /
-    #            "location_of_temp_folder.split('/')[-1]",location_of_temp_folder.split('/')[-1])
-        
-    #     if location_of_temp_folder.split('/')[-1] != '.temp':
-    #         folder =os.path.join(location_of_temp_folder, '.temp')
-    #     else:
-    #         folder = location_of_temp_folder
-        
-    #     if not os.path.exists(folder):
-    #             os.makedirs(folder)
-        
-    #     folder = os.path.realpath(folder)
-
-    #     if not hasattr(self, 'normalized_output') or not hasattr(self, 'diarization_output'):
-    #         raise AttributeError("You need to run the diarization first")
-        
-    #     speaker = set(self.diarization_output["speakers"])
-    #     num_speak_iter = [0 for _ in range(len(speaker))]
-
-    #     for count, outp in enumerate(self.normalized_output):
-    #         print(outp)
-    #         print(self.diarization_output["segments"][outp[0]])
-    #         print(self.diarization_output["segments"][outp[1]])
-
-    #         start = self.diarization_output["segments"][outp[0]].start
-    #         end = self.diarization_output["segments"][outp[1]].end
-
-    #         print("start: ", start)
-    #         print("end: ", end)
-
-    #         start_milliseconds = start * 1000
-    #         end_milliseconds = end * 1000
-
-    #         print("start_milliseconds: ", start_milliseconds)
-    #         print("end_milliseconds: ", end_milliseconds)
-
-    #         print("cut audio")
-
-    #         cut_audio = self.audio_file[start_milliseconds:end_milliseconds]
-
-    #         print("save audio")
-    #         print(f".temp/{count}_speaker_" + str(outp[2]) + ".wav")
-    #         cut_audio.export(f".temp/{count}_speaker_" + str(outp[2]) + ".wav", format="wav")
-
-    #     return os.path.realpath(folder)