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eval.py

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+import gc
+import logging
+import argparse
+from datetime import datetime
+from collections import OrderedDict
+# Disable TF warnings about speed up and future warnings
+#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+# Disable warnings from h5py
+import warnings
+warnings.filterwarnings("ignore", category = FutureWarning)
+import math
+# Audio processing and DL frameworks 
+import h5py
+import librosa
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+import keras
+from keras import backend as K
+from keras.models import load_model
+
+genres = {0: 'metal', 1 : 'disco', 2: 'classical', 3 :'hiphop', 4:'jazz', 
+          5 :'country', 6:'pop', 7:'blues', 8:'reggae', 9:'rock'}
+
+song_samples = 660000
+
+def load_song(filepath):
+    y, sr = librosa.load(filepath)
+    y = y[:song_samples]
+    return y, sr
+
+def splitsongs(X, window = 0.1, overlap = 0.5):
+    # Empty lists to hold our results
+    temp_X = []
+    # Get the input song array size
+    xshape = X.shape[0]
+    chunk = int(xshape*window)
+    offset = int(chunk*(1.-overlap))
+
+    # Split the song and create new ones on windows
+    spsong = [X[i:i+chunk] for i in range(0, xshape - chunk + offset, offset)]
+    for s in spsong:
+        temp_X.append(s)
+
+    return np.array(temp_X)
+#print(splitsongs(y))
+
+def to_melspec(signals):
+    #mel_spec = [librosa.feature.melspectrogram(i) for i in signals]
+    melspec = lambda x : librosa.feature.melspectrogram(x, sr=22050, n_fft=1024, hop_length=512)[:, :, np.newaxis]
+    spec_array = map(melspec, signals)
+    return np.array(list(spec_array))
+
+
+def evaluate(filepath):
+    y = load_song(filepath)[0]
+    predictions = []
+    spectro = []
+    signals = splitsongs(y)
+    spec_array = to_melspec(signals)
+    spectro.extend(spec_array)
+    spectro = np.array(spectro)
+    spectro = np.squeeze(np.stack((spectro,)*3,-1)) #In case you're using VGG16
+    print(spectro.shape)
+    model = keras.models.load_model("Enter Model Path Here")
+    #print(model.summary())
+    #for i in range(len(spec_array)):
+    predictions = np.array(model.predict(spectro))
+    preds = np.argmax(predictions, axis=1)
+    print(genres[np.bincount(preds).argmax()])
+
+
+print(evaluate(input("Enter File Path :")))        
+
+