{"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":"df_train= pd.read_csv(\"/kaggle/input/birdsong-recognition/train.csv\")\ndf_test= pd.read_csv(\"/kaggle/input/birdsong-recognition/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"f=\"../input/birdsong-recognition/train_audio/\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path_train=[]\ny_train=[]\nfor i in range(0,len(df_train)):\n    f=\"../input/birdsong-recognition/train_audio/\"\n    f=f+df_train.iloc[i,2]+'/'+df_train.iloc[i,7]\n    path_train.append(f)\n    y_train.append(df_train.iloc[i,2])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(path_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from pydub import AudioSegment\n\nsound = AudioSegment.from_mp3(path_train[i])\n\nraw_data = sound.raw_data\ndata = np.fromstring(raw_data, dtype=np.int16)\nsample_rate = sound.frame_rate\nsample_size = sound.sample_width\nchannels = sound.channels","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train=[]\nfrom pydub import AudioSegment\n\nfor i in range(0, len(path_train)):\n    sound = AudioSegment.from_mp3(path_train[i])\n    raw_data = sound.raw_data\n    data = np.fromstring(raw_data, dtype=np.int16)\n    x_train.append(data)\n#sample_rate = sound.frame_rate","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize=(10, 5))\nplt.plot(data)\nplt.savefig(\"./my_img.png\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import IPython\nimport librosa\nx, sr = librosa.load(path=f, mono=True)\nIPython.display.Audio(data=data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.shape","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}