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main_code.py

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+import numpy as np
+import tensorflow as tf
+from scipy.io.wavfile import write
+import keras.backend as K
+import librosa.display
+import cv2
+import librosa
+import matplotlib.pyplot as plt
+import librosa.display
+import numpy as np
+from keras.applications import VGG16
+import os
+import scipy
+
+# Define function to preprocess input audio
+#convert song to mel spectogram as siamese network doesn't work on sound directly
+def create_spectrogram(clip,sample_rate,save_path):
+    plt.interactive(False)
+    fig=plt.figure(figsize=[0.72,0.72])
+    S=librosa.feature.melspectrogram(y=clip,sr=sample_rate)
+    librosa.display.specshow(librosa.power_to_db(S,ref=np.max))
+    fig.savefig(save_path,dpi=400,bbox_inches='tight',pad_inches=0)
+    plt.close()
+    fig.clf()
+    plt.close(fig)
+    plt.close('all')
+    del save_path,clip,sample_rate,fig,S
+    
+def load_img(path):
+    img=cv2.imread(path)
+    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+    img=cv2.resize(img,(150,150))
+    return img
+
+import pickle
+
+def main_loop():
+
+    with open('dict.pickle', 'rb') as handle:
+        songspecdict = pickle.load(handle)
+
+    # Load the song to match
+    song, sr = librosa.load("my_audio.wav")
+    to_match = np.copy(song[0:220500])
+    print("Loaded data into librosa...")
+
+    # Create spectrogram image of the song to match
+    create_spectrogram(to_match, sr, 'test.png')
+    print("Created spectogram...")
+
+    # Load the spectrogram image of the song to match
+    to_match_img = load_img('test.png')
+    to_match_img = np.expand_dims(to_match_img, axis=0)
+    print("Loaded spectrum image...")
+
+    # Get the embedding of the song to match
+    # Load the tune recognition model
+    model = tf.keras.models.load_model('./embdmodel_1.hdf5')
+    embedding_model=model.layers[2]
+    to_match_emb = embedding_model.predict(to_match_img)
+    print("Get song embedding...")
+
+    # Calculate the distances between the song to match and the songs in the database
+    songsdistdict = {}
+    for key, values in songspecdict.items():
+        dist_array = []
+        for embd in values:
+            dist_array.append(np.linalg.norm(to_match_emb - embd))
+            
+        songsdistdict[key] = min(dist_array)
+    song_titles=list(songsdistdict.keys())
+    distances=list(songsdistdict.values())
+
+    # Get the title and artist of the recognized song
+    recognized_song_artist, recognized_song_title = song_titles[distances.index(min(distances))].split('-')
+    recognized_song_title = os.path.splitext(recognized_song_title)[0]
+    print(f'Artist: {recognized_song_artist}')
+    print(f'Title: {recognized_song_title}')
+
+    return recognized_song_title