autotrancript works

autotranscript/autotranscipt.py

@@ -1,13 +1,11 @@
-from audio import AudioProcessor , TorchAudioProcessor
+from audio import AudioProcessor
-
 from diarisation import Diariser
 from transcriber import Transcriber, whisper
-from whisper import Whisper
 from transcript_exporter import Transcript
 from typing import Union , TypeVar
 from tqdm import trange
-from pprint import pprint
 import torch
+
 diarisation = TypeVar('diarisation')
 
 
@@ -35,6 +33,7 @@ class AutoTranscribe:
         
         if whisper_model is None:
             self.transcriber = Transcriber.load_model("medium", local=True)
+            
         elif isinstance(whisper_model, str):
             self.transcriber = Transcriber.load_model(whisper_model, **whisper_kwargs)
         else:
@@ -55,7 +54,8 @@ class AutoTranscribe:
         Transcribe audiofile with whisper model and pyannote diarization model
         
         :param audiofile: path to audiofile or torch.Tensor
-        :return: Transcript object
+        :return: Transcript object which contains the transcript and can be used to 
+                export the transcript to differnt formats.
         """
         
         audiofile = self.get_audiofile(audiofile)
@@ -68,11 +68,13 @@ class AutoTranscribe:
        
         print("Starting diarisation.")
         
-        diarisation = self.diariser.diarization( dia_audio,
+        diarisation = self.diariser.diarization(dia_audio,
                                                 *args , **kwargs)
         
         print("Diarisation finished. Starting transcription.")
         
+        audiofile.sr = torch.Tensor([audiofile.sr]).to(audiofile.waveform.device)
+        
         for i in trange(len(diarisation["segments"]), desc= "Transcribing"):
             
             seg = diarisation["segments"][i]
@@ -84,12 +86,11 @@ class AutoTranscribe:
             final_transcript[i] = {"speaker" : diarisation["speakers"][i],
                                    "text" : transcript}
 
-        pprint(final_transcript)   
+        return Transcript(transcript, diarisation)
-        #return Transcript(transcript, diarisation)
     
     @staticmethod
     def get_audiofile(audiofile : Union[str, torch.Tensor],
-                        *args, **kwargs) -> TorchAudioProcessor:
+                        *args, **kwargs) -> AudioProcessor:
         """
         Get audiofile as TorchAudioProcessor
 
@@ -99,22 +100,15 @@ class AutoTranscribe:
                  waveform and sample_rate in torch.Tensor format.
             :rtype: TorchAudioProcessor
         """
+        
         if isinstance(audiofile, str):
-            try:
+            audiofile = AudioProcessor.from_file(audiofile)   
-               audiofile = TorchAudioProcessor.from_file(audiofile)   
-            except: 
-                print("Could not load audiofile with torch audio." \
-                        "Trying ffmpeg. using pydub.")
-                audiofile = TorchAudioProcessor.from_ffmpeg(audiofile)
         
         if isinstance(audiofile, torch.Tensor):
-            audiofile = TorchAudioProcessor(audiofile[0], audiofile[1])
+            audiofile = AudioProcessor(audiofile[0], audiofile[1])
         
-        if isinstance(audiofile, AudioProcessor):
+        if not isinstance(audiofile, AudioProcessor):
-            audiofile = TorchAudioProcessor.from_audio_processor(audiofile)
+            raise ValueError(f'Audiofile must be of type AudioProcessor,' \
-        
-        if not isinstance(audiofile, TorchAudioProcessor):
-            raise ValueError(f'Audiofile must be of type TorchAudioProcessor,' \
                              f'not {type(audiofile)}')     
         return audiofile
     
@@ -122,4 +116,4 @@ class AutoTranscribe:
 if __name__ == "__main__":
     
     AudioTranscriber = AutoTranscribe()
-    AudioTranscriber.transcribe("/home/jacob/PycharmProjects/autotranscript/tests/Kathi_interview.mp3" , num_speaker=2)
+    AudioTranscriber.transcribe("tests/test.wav")