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| import numpy as np | |
| import librosa | |
| import tensorflow as tf | |
| from sklearn.preprocessing import LabelEncoder | |
| import gradio as gr | |
| #TF_ENABLE_ONEDNN_OPTS=0 | |
| classes_path = r'./classes_new.npy' | |
| model_path = r'./Model_Audio_Classification_new.h5' | |
| #uddhav Gupta | |
| def audio_class(audio_path): | |
| labelencoder = LabelEncoder() | |
| labelencoder.classes_ = np.load(classes_path) | |
| model_h5 = tf.keras.models.load_model(model_path) | |
| audio, sample_rate = librosa.load(audio_path, res_type='soxr_vhq') | |
| mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40) | |
| mfccs_scaled_features = np.mean(mfccs_features.T, axis=0) | |
| mfccs_scaled_features = mfccs_scaled_features.reshape(1, -1) | |
| predict_x = model_h5.predict(mfccs_scaled_features) | |
| predicted_label = np.argmax(predict_x, axis=1) | |
| prediction_class = labelencoder.inverse_transform(predicted_label) | |
| result = str(prediction_class) | |
| result = result.replace("['", '') | |
| result = result.replace("']", '') | |
| #print(result) | |
| #print(f"Audio Class: {x}") | |
| return result | |
| des = '''Upload .wav / .mp3 file or record audio and wait until file upload or recording. | |
| Press submit button to classify among predefined audio classes or clear button to remove audio file in current directory. | |
| Audio classes for classification: air conditioner, car horn, children playing, dog bark, drilling, engine idling, gun shot, jackhammer, siren, and street music.''' | |
| x = gr.Interface( | |
| audio_class, | |
| gr.Audio(sources=["upload","microphone"],type="filepath"), | |
| "text", | |
| title="Audio classification Using Deep Learning", | |
| description=des) | |
| x.launch() |