relativ imports

test/test_audio.py

@@ -1,5 +1,5 @@
 import pytest
-from .audio import AudioProcessor
+from ..scraibe.audio import AudioProcessor
 import torch
 
 

test/test_autotranscript.py

@@ -1,14 +1,16 @@
 import pytest
 import torch
-from scraibe import Scraibe, Diariser, Transcriber, Transcript, AudioProcessor
-from unittest.mock import MagicMOck, patch
+from ..scraibe import Scraibe, Diariser, Transcriber, Transcript, AudioProcessor
+from unittest.mock import MagicMock, patch
 
 
 """
 @pytest.fixture
-def example_audio_file(tmp_path):
-    audio_path = tmp_path 
+def example_audio_file(audio_test_2.mp4):
+    audio_path = audio_test_2.mp4 
 """
+
+
 @pytest.fixture
 def create_scraibe_instance():
     return Scraibe()
@@ -23,7 +25,7 @@ def test_scraibe_init(create_scraibe_instance):
 
 def test_scraibe_autotranscribe(create_scraibe_instance, example_audio_file):
     model = create_scraibe_instance
-    transcript = example_audio_file
+    transcript = model.autotranscribe(example_audio_file)
     assert isinstance(transcript, Transcript)
 
 def test_scraibe_diarization(create_scraibe_instance, example_audio_file):

test/test_diarisation.py

@@ -1,7 +1,7 @@
 import pytest
 import os
 from unittest import mock
-from scraibe import Diariser
+from ..scraibe import Diariser
 
 
 

test/test_transcriber.py

@@ -1,29 +1,29 @@
 import pytest
 from unittest.mock import patch
-from scraibe import Transcriber
-
+from ..scraibe import Transcriber
 
 
 
+""" 
 @pytest.mark.parametrize("audio_file, expected_transcription",[("path_to_test_audiofile", "test_transcription")] )
 @patch("scraibe.Transcriber.load_model")
 
 def test_transcriber(mock_load_model, audio_file, expected_transcription):
-    """_summary_
+    
 
     Args:
         mock_load_model (_type_): _description_
         audio_file (_type_): _description_
         expected_transcription (_type_): _description_
-    """    
+       
     mock_model = mock_load_model.return_value
     mock_model.transcribe.return_value ={"text": expected_transcription} 
 
     transcriber = Transcriber.load_model(model="medium")
 
     transcription_result = transcriber.transcribe(audio=audio_file)
 
-    assert transcription_result == expected_transcription
+    assert transcription_result == expected_transcription """