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| import numpy as np | |
| import tensorflow as tf | |
| import librosa | |
| import os | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| def load_model(): | |
| abs_path = os.getcwd() | |
| model = tf.keras.models.load_model(abs_path + "/saved_model/model_20230607_02") | |
| model.compile(optimizer='adam', | |
| loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), | |
| metrics=['accuracy']) | |
| return model | |
| def sample_preparer(location): | |
| sample_data = [] | |
| sample = np.zeros((128, 100, 3)) | |
| y, sr = librosa.load(location, sr=22050) | |
| y, _ = librosa.effects.trim(y, top_db=50) | |
| y = librosa.resample(y=y, orig_sr=sr, target_sr=22050) | |
| melspect = librosa.feature.melspectrogram(y=y) | |
| for i, _ in enumerate(melspect): # 128 | |
| for j, _ in enumerate(melspect[i]): # LENGTH | |
| sample[i][j] = melspect[i][j] | |
| sample_data = [sample] | |
| return sample_data | |