{"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 pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nimport sklearn\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n%matplotlib inline\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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_data = pd.read_csv(\"../input/birdsong-recognition/train.csv\")\ntrain_data = train_data.drop([\"author\", \"recordist\", \"license\", \"rating\", \"date\", \"url\", \"file_type\", \"background\", \"primary_label\", \"description\" ], axis=1)\n\ntrain_data = train_data[[\"ebird_code\", \"filename\"]]\n\ntrain_data.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"le = sklearn.preprocessing.LabelEncoder()\ntrain_data[\"ebird_code\"] = le.fit_transform(train_data[\"ebird_code\"])\ntrain_data.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\n\nfilepaths = []\n\ndirectories = sorted(os.listdir('/kaggle/input/birdsong-recognition/train_audio'))\n\nfor directory in directories:\n    filepath = '/kaggle/input/birdsong-recognition/train_audio/' + directory + \"/\"\n    for dirpath, dirnames, filenames in os.walk(filepath):\n        filenames = sorted(filenames)\n        for filename in filenames:\n            filepaths.append(os.path.join(dirpath, filename))\n    \n    \ntrain_data['filepath'] = pd.Series(filepaths)\n\nprint(\"complete\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.set_option('display.max_colwidth', None)\ntrain_data.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize = (10,8))\n\naudio, sr = librosa.load(train_data[\"filepath\"][0], sr=44100)\nX = librosa.stft(audio)\nXdb = librosa.amplitude_to_db(abs(X))\nlibrosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='log')\nplt.colorbar()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")\n\ncmap = plt.get_cmap('inferno')\nplt.figure(figsize = (8,8))\n\ndef mp3_to_img(filepath, num):\n    audio, sr = librosa.load(filepath, sr=44100)\n    plt.specgram(audio, NFFT=2048, Fs=2, Fc=0, noverlap=128, cmap=cmap, sides='default', mode='default', scale='dB');\n    plt.axis('off');\n    plt.savefig(f'img{num}.png')\n    plt.clf()\n\n\ni = 0\nfor file in train_data['filepath']:\n    mp3_to_img(file, i)\n    i+=1\n\n\nprint(\"complete\")","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}