Delete logic.py

logic.py

@@ -1,73 +0,0 @@
-from phonemizer.separator import Separator
-from phonemizer import phonemize
-# from phonemizer.backend.espeak.wrapper import EspeakWrapper
-from Levenshtein import distance as levenshtein_distance    
-from scoring import calculate_fluency_and_pronunciation
-
-import whisper 
-import torch 
-
-device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-
-model = whisper.load_model("base.en", device=device)
-separator = Separator(phone=None, word='',)
-
-# EspeakWrapper.set_library(r"C:\Program Files\eSpeak NG\libespeak-ng.dll")
-
-def transcribe(audio):
-    result = model.transcribe(audio, word_timestamps=False, no_speech_threshold=0.4,  compression_ratio_threshold=2, temperature=0)
-    return {'language': result['language'], 'text': result['text']}
-
-def text2phoneme(text):
-    return phonemize(text.lower().split(), backend='espeak' , separator=separator, strip=True, with_stress=False, tie=False, language='en-us')
-
-def rate_pronunciation(expected_phonemes, actual_phonemes):
-    expected_phonemes = expected_phonemes
-    actual_phonemes = actual_phonemes
-    # Calculate the Levenshtein distance between the two phoneme sequences
-    results = []
-    for i, base_word in enumerate(actual_phonemes):
-        best_dist = float('inf')
-        if i <= len(expected_phonemes): 
-            for j in range(max(0, i-1), i + min(3, len(expected_phonemes) - i)):
-                dist = levenshtein_distance(expected_phonemes[j], base_word,)
-                if dist < best_dist:
-                    best_dist = dist
-                if best_dist == 0:  # Early stopping on perfect match
-                    break
-        error_threshold = len(base_word) * 0.40
-        if best_dist == 0:
-           results.append(3) 
-        elif best_dist <= error_threshold:
-            results.append(2) 
-        else:
-            results.append(1) 
-    return results
-
-
-
-
-def Speaker_speech_analysis(audio_path, text):
-    pre_transcribtion = transcribe(audio_path)['text']
-    print(pre_transcribtion)
-    transcribtion = text2phoneme(pre_transcribtion)
-    text_phone    = text2phoneme(text)
-    scores        = rate_pronunciation(transcribtion, text_phone)
-    FP_scores     = calculate_fluency_and_pronunciation(audio_path, len(pre_transcribtion.split()), scores, len(text.split()))
-    word_scores = [(word, s) for word, s in zip(text.split(), scores)]
-    
-    FP_scores['word_scores'] = word_scores
-    return FP_scores
-
-if __name__ == '__main__':
-    
-    text = 'i have ADHD '
-    text = text2phoneme(text)
-    file_path = r'user_recording.wav'
-    trans = transcribe(file_path)['text']
-    print(trans)
-    trans = text2phoneme(trans)
-    print('base:', text)
-    print('predicted:', trans)
-    result = rate_pronunciation(trans, text)
-    print(result)