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import librosa |
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import numpy as np |
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import os |
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import glob |
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audio_folder = 'images/audio' |
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file_paths = glob.glob(os.path.join(audio_folder, '*.wav')) |
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results = [] |
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for file_path in file_paths: |
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y, sr = librosa.load(file_path) |
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S = librosa.feature.melspectrogram(y=y, sr=44100, n_mels=256) |
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log_S = librosa.power_to_db(S, ref=np.max) |
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vector = log_S.mean(axis=0) |
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results.append([os.path.basename(file_path), vector]) |
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results_dict = {item[0]: item[1] for item in results} |
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np.save('vec.npy', results_dict) |
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print("Results saved to 'vec.npy'") |
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import json |
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with open('vectors.jsonl', 'w') as fout: |
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for filename, vec in results_dict.items(): |
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entry = {'filename': filename, 'audio_vector': vec.tolist()} |
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line = json.dumps(entry) + '\n' |
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fout.write(line) |
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print("Vectors saved to 'vectors.jsonl'") |