{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import librosa","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"def load_test_clip(path, start_time, duration=5):\n    return librosa.load(path, offset=start_time, duration=duration)[0]\nTEST_FOLDER = '../input/birdsong-recognition/test_audio/'\ntest_info = pd.read_csv('../input/birdsong-recognition/test.csv')\ntest_info.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"train = pd.read_csv('../input/birdsong-recognition/train.csv')\nbirds = train['ebird_code'].unique()\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_info","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def make_prediction(sound_clip, birds):\n    return np.random.choice(birds)\ntry:\n    preds = []\n    for index, row in test_info.iterrows():\n        # Get test row information\n        site = row['site']\n        start_time = row['seconds'] - 5\n        row_id = row['row_id']\n        audio_id = row['audio_id']\n\n        # Get the test sound clip\n        if site == 'site_1' or site == 'site_2':\n            sound_clip = load_test_clip(TEST_FOLDER + audio_id + '.mp3', start_time)\n        else:\n            sound_clip = load_test_clip(TEST_FOLDER + audio_id + '.mp3', 0, duration=None)\n\n        # Make the prediction\n        pred = make_prediction(sound_clip, birds)\n\n        # Store prediction\n        preds.append([row_id, pred])\n\n    preds = pd.DataFrame(preds, columns=['row_id', 'birds'])\nexcept:\n    preds = pd.read_csv('../input/birdsong-recognition/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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"}},"nbformat":4,"nbformat_minor":4}