Delete scoring.py

scoring.py

@@ -1,95 +0,0 @@
-import numpy as np
-import librosa
-
-def calculate_expected_value(scores):
-    # First calculate the probability of each unique score
-    unique_scores, counts = np.unique(scores, return_counts=True)
-    probabilities = counts / len(scores)
-    
-    # Then calculate the expected value as the sum of scores times their probabilities   
-    expected_value = np.dot(unique_scores, probabilities)
-    return expected_value
-
-
-def calculate_fluency_score(audio_path, total_words, word_pronunciation_scores, base_script_len): 
-    
-    avg_pronunciation_score = calculate_expected_value(word_pronunciation_scores)
-    if (total_words / base_script_len) < 0.15 or avg_pronunciation_score < 1.5:
-        return 10
-    audio, sr = librosa.load(audio_path)
-    non_silent_intervals = librosa.effects.split(audio, top_db=22)
-    non_silent_duration = sum([intv[1] - intv[0] for intv in non_silent_intervals]) / sr
-    
-    total_duration = len(audio) / sr
-    
-    non_silent_duration = non_silent_duration
-    ideal_min_rate, ideal_max_rate = 120 / 60, 140 / 60
-    actual_speech_rate = (total_words / (non_silent_duration + 1e-7)) * (total_words / base_script_len)
-    speaking_ratio = non_silent_duration / total_duration
-    # Existing speech rate score calculation
- 
-    # Determine if speech rate is within the ideal range
-    if actual_speech_rate <= ideal_max_rate:
-        # Within the ideal range or speaking slow
-        max_ratio = actual_speech_rate / ideal_max_rate
-        min_ratio = (actual_speech_rate / ideal_min_rate)
-        speech_rate_score = np.mean([max_ratio, min_ratio]) - 0.167 
-        # for normal speaking speech_rate_score between (0.708, 1) and for slow speaking speech_rate_score (0.707, 0)
-    else:
-        # Too fast 
-        # for fast speaking speech_rate_score (0.707, 0)
-        max_ratio = actual_speech_rate / ideal_max_rate
-        speech_rate_score = 0.7 / max_ratio
-    
-    # If speaking ratio is significantly less than the gold standard, reduce the fluency score
-    gold_standard_ratio = 0.9  # Assuming 90% speaking time is gold standard for natural speech
-    speaking_ratio_score = min(speaking_ratio / gold_standard_ratio, 1)
-    
-
-    # Pronunciation score calculation
-    avg_pronunciation_score = (avg_pronunciation_score - 1) / 2
-
-    # pronunciation_variance = np.var(word_pronunciation_scores, ddof=1,)
-
-    # Weighted combination of scores
-    # Adjust weights as needed
-    weight_speech_rate = 0.30
-    weight_speaking_ratio = 0.20
-    weight_pronunciation = 0.50
-    # weight_pronunciation_variance = 0.10
-
-    combined_score = speech_rate_score * weight_speech_rate + speaking_ratio_score * weight_speaking_ratio + avg_pronunciation_score * weight_pronunciation 
-    
-    # Scale the combined score to be between 10% and 100%
-    scaled_fluency_score = 10 + combined_score * 80
-
-    return scaled_fluency_score
-
-def calculate_pronunciation_accuracy(word_pronunciation_scores, fluency_score, base_script_len, total_words):
-    # if total_words / base_script_len < 0.25:
-    #     return 10
-    # Calculate average word pronunciation score
-    avg_pronunciation_score = calculate_expected_value(word_pronunciation_scores)
-
-    fluency_score = fluency_score / 100
-
-    avg_pronunciation_score = (avg_pronunciation_score - 1) / 2
-    avg_weight = 0.75
-    flu_weight = 0.25
-    combined_score = avg_weight * avg_pronunciation_score + flu_weight * fluency_score
-    # Scale to 10% - 90%
-    final_score = 10 + combined_score * 80  
-
-    return final_score
-
-def calculate_fluency_and_pronunciation(audio_path, total_words, word_pronunciation_scores, base_script_len):
-    
-    fluency_score = calculate_fluency_score(audio_path, total_words, word_pronunciation_scores, base_script_len)
-    
-    pronunciation_accuracy = calculate_pronunciation_accuracy(word_pronunciation_scores, fluency_score, base_script_len, total_words)
-    
-    return {'fluency_score': fluency_score, 'pronunciation_accuracy': pronunciation_accuracy}
-    
-
-if __name__ == '__main__':
-    pass