{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"conda install -c conda-forge librosa","execution_count":null,"outputs":[]},{"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)\nimport librosa\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":"sample_audio = [\n    'aldfly/XC134874.mp3',\n    'amegfi/XC109299.mp3',\n    'brebla/XC104521.mp3',\n    'lewwoo/XC161334.mp3',\n    'macwar/XC125970.mp3',\n    'norwat/XC124175.mp3',\n    'pinjay/XC153392.mp3',\n    'rufhum/XC133552.mp3',\n    'weskin/XC124287.mp3',\n    'yetvir/XC120867.mp3'    \n]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"BASE_PATH = '../input/birdsong-recognition'\n\n# image and mask directories\ntrain_data_dir = f'{BASE_PATH}/train_audio'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for audio in sample_audio:\n    print(\"Audio sample of bird\", audio.split('/')[0])\n    audio_file = f\"{train_data_dir}/{audio}\"\n    display(ipd.Audio(audio_file))\n    \n    signal1, rate1 = librosa.load(audio_file, duration=5)   #default sampling rate is 22 HZ\n    dur=librosa.get_duration(signal1)\n    print(\"Duration in seconds. \",librosa.get_duration(signal1))\n    print(signal1.shape, rate1)","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}