Delete audio.py

audio.py

@@ -1,105 +0,0 @@
-from transformers import AutoFeatureExtractor, WhisperForAudioClassification
-import torch
-# import librosa
-
-
-
-device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-# device = 'cpu'
-print('Run on:', device)
-
-SAMPLEING_RATE = 16000
-MAX_LENGTH = SAMPLEING_RATE * 10 # 10 seconds 
-
-
-fluency_model_name = "seba3y/whisper-tiny-fluency" #future use
-acc_model_name = 'seba3y/whisper-tiny-accuracy'
-
-fluency_feature = AutoFeatureExtractor.from_pretrained(fluency_model_name)
-fluency_model = WhisperForAudioClassification.from_pretrained(fluency_model_name).to(device)
-acc_feature = AutoFeatureExtractor.from_pretrained(acc_model_name)
-acc_model = WhisperForAudioClassification.from_pretrained(acc_model_name).to(device)
-
-
-def load_audio_from_path(audio, feature_extractor, max_length=MAX_LENGTH):
-    # audio, _ = librosa.load(file_path, sr=SAMPLEING_RATE)
-    _, audio = audio
-    audio_length = len(audio)
-    # Splitting the audio if it's longer than max_length
-    segments = []
-    for start in range(0, audio_length, max_length):
-        end = min(start + max_length, audio_length)
-        segment = audio[start:end]
-        inputs = feature_extractor(segment, sampling_rate=SAMPLEING_RATE, return_tensors="pt", max_length=max_length, padding="max_length", ).input_features
-        segments.append(inputs)
-    
-    return segments
-
-
-@torch.no_grad()
-def model_generate(inputs, model):
-    logits = model(inputs.to(device))[0]
-    return logits
-
-
-def postprocess(logits, model, noise=1):
-    logits = noise * (logits.cpu() + 0.9)
-    scores = logits.softmax(-1)[0]
-    print(scores)
-    ids = torch.argmax(scores, dim=-1).item()
-    scores = scores.tolist()
-    labels = model.config.id2label[ids]
-    return labels, round(scores[ids], 2)
-
-def predict(segments, model, noise):
-    
-
-    all_logits = []
-    
-    for segment in segments:
-        logits = model_generate(segment, model)
-        all_logits.append(logits)
-
-    # Aggregating the results (simple average)
-    avg_logits = torch.mean(torch.stack(all_logits), dim=0)
-    return postprocess(avg_logits, model, noise)
-
-def prdict_accuracy(file_path):
-    Anoise = torch.tensor([100.618, .0118, 10.945, 30.419])
-    result = predict(file_path, acc_model, Anoise)
-    return result
-
-def predict_fluency(file_path):
-    Fnoise = torch.tensor([5.618, 4.518, 2.145, 0.219])
-    result = predict(file_path, fluency_model, Fnoise)
-    return result
-
-def predict_all(file_path):
-    Anoise = torch.tensor([5.618, 1.518, 10.945, 100.419])
-    Fnoise = torch.tensor([3.618, 5.518, 3.045, 0.49])
-    segments = load_audio_from_path(file_path, acc_feature)
-    acc = predict(segments, acc_model, Anoise)
-    fle = predict(segments, fluency_model, Fnoise)
-    return acc, fle
-        
-if __name__ == '__main__':
-    file_path = r'uploads\audio.wav'
-    print('start')
-    result = predict_fluency(file_path)
-    print('done')
-    # print('Fluency of the speech:')
-    # print("="*25)
-    # print(result)
-    # # for key, value in result.items():
-    # #     print('Prediction:', key, "\nConfidinse:", round(value, 2) * 100, '%')
-    # # print() 
-    # # print("="*25) 
-    # # print()
-    # print('Pronunciation Accuracy of the speech:')
-    # print("="*25)  
-    # result = prdict_accuracy(file_path)
-    # print(result)
-    # for key, value in result.items():
-    #     print('Prediction:', key, "\nConfidinse:", round(value, 2) * 100, '%')
-    # print()
-    # print('='*25)