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| import torch | |
| import librosa | |
| import os | |
| from model import Wav2Vec2BertForSequenceClassification | |
| from transformers import AutoFeatureExtractor | |
| # from optimum.bettertransformer import BetterTransformer | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' | |
| # os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = '1' | |
| # os.environ['TRANSFORMERS_VERBOSITY'] = 'error' | |
| torch.random.manual_seed(0); | |
| # protobuf==3.20.0 | |
| model_name = "arslanarjumand/wav2vec-reptiles" | |
| processor = AutoFeatureExtractor.from_pretrained(model_name) | |
| model = Wav2Vec2BertForSequenceClassification.from_pretrained(model_name).to(device) | |
| # model = BetterTransformer.transform(model) | |
| def load_audio(audio_path, processor): | |
| audio, sr = librosa.load(audio_path, sr=16000) | |
| input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_features | |
| return input_values | |
| def get_emissions(input_values, model): | |
| results = model(input_values,).logits[0] | |
| return results | |
| def speaker_pronunciation_assesment(audio_path): | |
| input_values = load_audio(audio_path, processor) | |
| result_scores = get_emissions(input_values, model) | |
| pronunciation_score = round(result_scores[0].cpu().item()) | |
| fluency_score = round(result_scores[1].cpu().item()) | |
| total_score = round(result_scores[2].cpu().item()) | |
| content_scores = round(result_scores[3].cpu().item()) | |
| result = {'pronunciation_accuracy': pronunciation_score, | |
| 'content_scores': content_scores, | |
| 'total_score': total_score, | |
| 'fluency_score': fluency_score} | |
| return result | |
| if __name__ == '__main__': | |
| pass | |