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| 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 = 0.8 | |
| flu_weight = 0.2 | |
| combined_score = avg_weight * avg_pronunciation_score + flu_weight * fluency_score | |
| # Scale to 10% - 90% | |
| final_score = 10 + combined_score * 90 | |
| 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 |