diff --git a/autotranscript/autotranscipt.py b/autotranscript/autotranscipt.py
new file mode 100644
index 0000000..c1225af
--- /dev/null
+++ b/autotranscript/autotranscipt.py
@@ -0,0 +1,125 @@
+from audio import AudioProcessor , TorchAudioProcessor
+
+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')
+
+
+class AutoTranscribe:
+    def __init__(self,
+                whisper_model: Union[bool, str, whisper] = None,
+                dia_model : Union[bool, str, diarisation] = None,
+                dia_kwargs : dict = {},
+                whisper_kwargs : dict = {}) -> None:
+        """
+        AutoTranscribe class
+        
+        This class is the core Api Class of the autotranscript package.
+        It allows to transcribe audio files with a whisper model and
+        pyannote diarization model. 
+        
+        Therefore it is do a fully automatic transcription of audio files.
+        
+        :param whisper_model: path to whisper model or whisper model
+        :param dia_model: path to pyannote diarization model
+        :param dia_kwargs: kwargs for pyannote diarization model
+        :param whisper_kwargs: kwargs for whisper model      
+        
+        """
+        
+        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:
+            self.transcriber = whisper_model
+
+        if dia_model is None:
+            self.diariser = Diariser.load_model()
+        elif isinstance(dia_model, str):
+            self.diariser = Diariser.load_model(dia_model, **dia_kwargs)
+        else:
+            self.diariser = dia_model
+
+        print("AutoTranscribe initialized all models successfully loaded.")
+            
+    def transcribe(self, audiofile : Union[str, torch.Tensor],
+                   *args, **kwargs) -> Transcript:
+        """
+        Transcribe audiofile with whisper model and pyannote diarization model
+        
+        :param audiofile: path to audiofile or torch.Tensor
+        :return: Transcript object
+        """
+        
+        audiofile = self.get_audiofile(audiofile)
+        
+        final_transcript = dict()
+        
+        dia_audio = {"waveform" : 
+                        audiofile.waveform.reshape(1,len(audiofile.waveform)), 
+                    "sample_rate": audiofile.sr}
+       
+        print("Starting diarisation.")
+        
+        diarisation = self.diariser.diarization( dia_audio,
+                                                *args , **kwargs)
+        
+        print("Diarisation finished. Starting transcription.")
+        
+        for i in trange(len(diarisation["segments"]), desc= "Transcribing"):
+            
+            seg = diarisation["segments"][i]
+            
+            audio = audiofile.cut(seg[0], seg[1])
+            
+            transcript = self.transcriber.transcribe(audio, *args , **kwargs)
+            
+            final_transcript[i] = {"speaker" : diarisation["speakers"][i],
+                                   "text" : transcript}
+
+        pprint(final_transcript)   
+        #return Transcript(transcript, diarisation)
+    
+    @staticmethod
+    def get_audiofile(audiofile : Union[str, torch.Tensor],
+                        *args, **kwargs) -> TorchAudioProcessor:
+        """
+        Get audiofile as TorchAudioProcessor
+
+        :param audiofile: path to audiofile or torch.Tensor
+            :type audiofile: Union[str, torch.Tensor]
+        :return: object of audiofile containes
+                 waveform and sample_rate in torch.Tensor format.
+            :rtype: TorchAudioProcessor
+        """
+        if isinstance(audiofile, str):
+            try:
+               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])
+        
+        if isinstance(audiofile, AudioProcessor):
+            audiofile = TorchAudioProcessor.from_audio_processor(audiofile)
+        
+        if not isinstance(audiofile, TorchAudioProcessor):
+            raise ValueError(f'Audiofile must be of type TorchAudioProcessor,' \
+                             f'not {type(audiofile)}')     
+        return audiofile
+    
+
+if __name__ == "__main__":
+    
+    AudioTranscriber = AutoTranscribe()
+    AudioTranscriber.transcribe("/home/jacob/PycharmProjects/autotranscript/tests/Kathi_interview.mp3" , num_speaker=2)
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