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| 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) | |