{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":70203,"databundleVersionId":8068726,"sourceType":"competition"}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport librosa\nimport numpy as np\nimport pandas as pd\n\n# Set seed\nnp.random.seed(42)\n\n# Class labels from train audio\nclass_labels = sorted(os.listdir('/kaggle/input/birdclef-2024/train_audio/'))\n\n# List of test soundscapes (only visible during submission)\ntest_soundscape_path = '/kaggle/input/birdclef-2024/test_soundscapes/'\ntest_soundscapes = [os.path.join(test_soundscape_path, afile) for afile in sorted(os.listdir(test_soundscape_path)) if afile.endswith('.ogg')]\n\n# Open each soundscape and make predictions for 5-second segments\n# Use pandas df with 'row_id' plus class labels as columns\npredictions = pd.DataFrame(columns=['row_id'] + class_labels)\nfor soundscape in test_soundscapes:\n\n    # Load audio\n    sig, rate = librosa.load(path=soundscape, sr=None)\n\n    # Split into 5-second chunks\n    chunks = []\n    for i in range(0, len(sig), rate*5):\n        chunk = sig[i:i+rate*5]\n        chunks.append(chunk)\n        \n    # Make predictions for each chunk\n    for i, chunk in enumerate(chunks):\n        \n        # Get row id  (soundscape id + end time of 5s chunk)      \n        row_id = os.path.basename(soundscape).split('.')[0] + f'_{i * 5 + 5}'\n        \n        # Make prediction (let's use random scores for now)\n        # scores = model.predict...\n        scores = np.random.rand(len(class_labels))\n        \n        # Append to predictions as new row\n        new_row = pd.DataFrame([[row_id] + list(scores)], columns=['row_id'] + class_labels)\n        predictions = pd.concat([predictions, new_row], axis=0, ignore_index=True)\n        \n# Save prediction as csv\npredictions.to_csv('submission.csv', index=False)\npredictions.head()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null}]}