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        "author": "Soh",
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        "description": "# ProWav  \nYou can use this for preprocessing wave files.  \n\n## Usage\nPlease install prowav by using pip.\n```\npip install prowav  \n```\n\n\n### Usage  \n\nProWav can calculate mfcc spectrogram and pad for batch execution  \n```python  \nfrom prowav import ProWav  \n\nprowav = ProWav([\"path/to/wave/data_1.wav\", \"path/to/wave/data_2.wav\"])  \n\nframe_width = 20 # the length of a frame (ms)\nstride_width = 20 # the frame interval (ms)\nn_mfcc = 26 # the number of features by mfcc features (If you want to use mfcc preprocessing, you should specify this value)  \nmode = 'MFCC'\nwindow_func = 'hamming' # the name for window function\ndata = prowav.prepro(frame_width=frame_width,stride_width=stride_width,mode=mode,\n                                       n_mfcc=n_mfcc, window_func=window_func)\n# >> (num_files, num_frames, n_mfcc)    \n```\nIf you want use fft spectrogram, please specify the mode, \"fft\".  \n```python\nprowav = ProWav([\"path/to/wave/data_1.wav\", \"path/to/wave/data_2.wav\"])\nframe_width = 20\nstride_width = 20\nmode='fft'\nwindow_func='hamming'  \ndata = prowav.prepro(frame_width=20,stride_width=20,\\\n      mode=mode, window_func=window_func)  \n# >> (num_files, num_frames, num_features)\n```\nYou can also use mel-spectrogram. Specify the mode, \"mel_spec\"\n```python\nprowav = ProWav([\"path/to/wave/data_1.wav\", \"path/to/wave/data_2.wav\"])\nframe_width = 50 \nstride_width = 50 \nmode='MFCC'\nn_mfcc = 26\nwindow_func='hamming'\ndata = prowav.prepro(frame_widh=frame_width, stride_width=stride_width,\n                  mode=mode, window_func=window_func, n_mfcc=n_mfcc)\n```\n\nYou can use zero-padding or repeat-padding.\n```python \nprowav = ProWav([\"path/to/wave/data_1.wav\", \"path/to/wave/data_2.wav\"])\nframe_width = 50 \nstride_width = 50 \nmode='mel_spec'\nn_mels = 50\nwindow_func='hamming'\ndata_zero = prowav.prepro(frame_widh=frame_width, stride_width=stride_width,\n                  mode=mode, n_mels=n_mels,window_func=window_func, zero_padding=True) # zero padding \ndata_repeat = prowav.prepro(frame_widh=frame_width, stride_width=stride_width,\n                  mode=mode, ne_mels=n_mels, window_func=window_func, repeat_padding=True) # repeat padding \n```\n\nJust loading wave data is possible.  \n\n```python \nprowav = ProWav([\"path/to/wave/data_1.wav\", \"path/to/wave/data_2.wav\"]) \n\nprowav.load_wav() # loading wav file into this class.\n\nprowav.data # the list of ndarray. Raw data are listed.\n```\n\nYou can choose parallel option.\n\n```python \nprowav.load_wav(parallel=True)\n\nprowav.load_wav(parallel=True, verbose=5)\n```\n\n\n\n\n",
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        "home_page": "https://github.com/wildgeece96/prowav",
        "keywords": "wave mfcc fft",
        "license": "MIT",
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        "name": "prowav",
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