setup install

.github/workflows/pytest.yaml

@@ -25,6 +25,7 @@ jobs:
         run: 
              python -m pip install --upgrade pip
              pip install -r requirements.txt
+             python install .
              pip install pytest
 
            

test/test_diarisation.py

@@ -1,7 +1,7 @@
 import pytest
 import os
 from unittest import mock
-from scraibe import Diariser
+from scraibe import diarisation, Diariser
 
 
 
@@ -15,7 +15,7 @@ def diariser_instance():
     Returns:
         Diariser(Obj): An instance of the Diariser class with a mocked token.
     """
-    with mock.patch.object(Diariser, '_get_token', return_value = 'personal Hugging-Face token')
+    with mock.patch.object(Diariser, '_get_token', return_value = 'HF_TOKEN' ):
         return Diariser('pyannote')
 
 

tests/test_autotranscript.py

@@ -1,120 +0,0 @@
-import pytest
-from scraibe import Transcriber
-from unittest.mock import patch, mock_open
-import os
-
-def test_load_pyannote_model():
-    """
-    Test load_pyannote_test
-    """
-    from pyannote.audio.pipelines.speaker_diarization import SpeakerDiarization
-    from pyannote.audio import Pipeline
-
-    pipeline = Pipeline.from_pretrained("models/pyannote/speaker_diarization/config.yaml")
-    assert isinstance(pipeline, SpeakerDiarization)
-
-# Test Transcribtion class
-
-
-@pytest.fixture
-def transcriber():
-    """
-    Prepare Transcriber for testing
-    Returns: Transcriber Object
-    """
-
-    return Transcriber.load_model("medium", local=True)
-
-
-def test_Transcriber_init(transcriber):
-    """
-    Test Transcriber initialization with a whisper model 
-    """
-    
-    assert isinstance(transcriber, Transcriber)
-
-def test_transcription(transcriber):
-    """
-    Test transcription
-    """
-
-    transcript = transcriber.transcribe("tests/test.wav") 
-    assert isinstance(transcript, str)
-    
-def test_save_transcript_to_file(transcriber):
-    """
-    Test save_transcript_to_file
-    """
-    transcript = transcriber.transcribe("tests/test.wav")
-
-    Transcriber.save_transcript(transcript, "tests/output.txt")
-    
-    assert os.path.exists("tests/output.txt")
-
-    os.remove("tests/output.txt")
-    
-# Test Diaraization class
-
-from scraibe import Diariser
-
-@pytest.fixture
-def diarisation():
-    """
-    Prepare Diarisation for testing
-    Returns: Diarisation Object
-    """
-
-    return Diariser.load_model("models/pyannote/speaker_diarization/config.yaml", local=True)
-
-def test_Diarisation_init(diarisation):
-    """
-    Test Diarisation initialization with a pyannote model 
-    """
-    
-    assert isinstance(diarisation, Diariser)
-
-def test_diarisation(diarisation):
-    """
-    Test diarisation
-    """
-
-    diarisation = diarisation.diarization("tests/test.wav") 
-    assert isinstance(diarisation, dict)
-
-# Test AudioProcessor
-
-from scraibe import AudioProcessor , TorchAudioProcessor
-
-
-def test_AudioProcessor_init():
-    """
-    Test AudioProcessor initialization
-    """
-    audio = AudioProcessor("tests/test.wav")
-    assert isinstance(audio, AudioProcessor)
-
-def test_AudioProcessor_convert():
-    """
-    Test AudioProcessor convert
-    """
-    audio = AudioProcessor("tests/test.wav")
-    audio.convert_audio("tests/test.mp3", format="mp3")
-    assert os.path.exists("tests/test.mp3")
-    
-def test_TorchAudioProcessor_from_file():
-    """
-    Test TorchAudioProcessor initialization
-    """
-    audio = TorchAudioProcessor.from_file("tests/test.wav")
-    
-    assert isinstance(audio, TorchAudioProcessor)
-    
-    os.remove("tests/test.mp3")
-
-
-def test_TorchAudioProcessor_from_ffmpeg():
-    """
-    Test TorchAudioProcessor initialization
-    """
-    audio = TorchAudioProcessor.from_ffmpeg("tests/test.wav")
-    assert isinstance(audio, TorchAudioProcessor)