added SCRAIBE_TORCH_DEVICE to Scraibe Class to handle torch device setting

scraibe/autotranscript.py

@@ -40,6 +40,7 @@ from .audio import AudioProcessor
 from .diarisation import Diariser
 from .transcriber import Transcriber, load_transcriber, whisper
 from .transcript_exporter import Transcript
+from .misc import SCRAIBE_TORCH_DEVICE
 
 
 DiarisationType = TypeVar('DiarisationType')
@@ -116,6 +117,9 @@ class Scraibe:
         else:
             self.params = {}
             
+        self.device = kwargs.get(
+            "device", SCRAIBE_TORCH_DEVICE)
+
     def autotranscribe(self, audio_file: Union[str, torch.Tensor, ndarray],
                        remove_original: bool = False,
                        **kwargs) -> Transcript:
@@ -141,7 +145,7 @@ class Scraibe:
 
         # Prepare waveform and sample rate for diarization
         dia_audio = {
-            "waveform": audio_file.waveform.reshape(1, len(audio_file.waveform)),
+            "waveform": audio_file.waveform.reshape(1, len(audio_file.waveform)).to(self.device),
             "sample_rate": audio_file.sr
         }
         
@@ -165,8 +169,6 @@ class Scraibe:
         if self.verbose:
             print("Diarisation finished. Starting transcription.")
 
-        audio_file.sr = torch.Tensor([audio_file.sr]).to(
-            audio_file.waveform.device)
 
         # Transcribe each segment and store the results
         final_transcript = dict()
@@ -213,7 +215,7 @@ class Scraibe:
 
         # Prepare waveform and sample rate for diarization
         dia_audio = {
-            "waveform": audio_file.waveform.reshape(1, len(audio_file.waveform)),
+            "waveform": audio_file.waveform.reshape(1, len(audio_file.waveform)).to(self.device),
             "sample_rate": audio_file.sr
         }
 
@@ -323,8 +325,7 @@ class Scraibe:
             print(f"Audiofile {audio_file} removed.")
 
     @staticmethod
-    def get_audio_file(audio_file: Union[str, torch.Tensor, ndarray],
-                       *args, **kwargs) -> AudioProcessor:
+    def get_audio_file(audio_file: Union[str, torch.Tensor, ndarray]) -> AudioProcessor:
         """Gets an audio file as TorchAudioProcessor.
 
         Args: