Metadata-Version: 2.1
Name: prowav
Version: 0.6
Summary: The package for preprocessing wave data
Home-page: https://github.com/wildgeece96/prowav
Author: Soh
Author-email: wildgeece96@gmail.com
License: MIT
Description: # ProWav  
        You can use this for preprocessing wave files.  
        
        ## Usage
        Please install prowav by using pip.
        ```
        pip install prowav  
        ```
        
        
        ### Usage  
        
        ProWav can calculate mfcc spectrogram and pad for batch execution  
        ```python  
        from prowav import ProWav  
        
        prowav = ProWav(["path/to/wave/data_1.wav", "path/to/wave/data_2.wav"])  
        
        frame_width = 20 # the length of a frame (ms)
        stride_width = 20 # the frame interval (ms)
        n_mfcc = 26 # the number of features by mfcc features (If you want to use mfcc preprocessing, you should specify this value)  
        mode = 'MFCC'
        window_func = 'hamming' # the name for window function
        data = prowav.prepro(frame_width=frame_width,stride_width=stride_width,mode=mode,
                                               n_mfcc=n_mfcc, window_func=window_func)
        # >> (num_files, num_frames, n_mfcc)    
        ```
        If you want use fft spectrogram, please specify the mode, "fft".  
        ```python
        prowav = ProWav(["path/to/wave/data_1.wav", "path/to/wave/data_2.wav"])
        frame_width = 20
        stride_width = 20
        mode='fft'
        window_func='hamming'  
        data = prowav.prepro(frame_width=20,stride_width=20,\
              mode=mode, window_func=window_func)  
        # >> (num_files, num_frames, num_features)
        ```
        You can also use mel-spectrogram. Specify the mode, "mel_spec"
        ```python
        prowav = ProWav(["path/to/wave/data_1.wav", "path/to/wave/data_2.wav"])
        frame_width = 50 
        stride_width = 50 
        mode='MFCC'
        n_mfcc = 26
        window_func='hamming'
        data = prowav.prepro(frame_widh=frame_width, stride_width=stride_width,
                          mode=mode, window_func=window_func, n_mfcc=n_mfcc)
        ```
        
        You can use zero-padding or repeat-padding.
        ```python 
        prowav = ProWav(["path/to/wave/data_1.wav", "path/to/wave/data_2.wav"])
        frame_width = 50 
        stride_width = 50 
        mode='mel_spec'
        n_mels = 50
        window_func='hamming'
        data_zero = prowav.prepro(frame_widh=frame_width, stride_width=stride_width,
                          mode=mode, n_mels=n_mels,window_func=window_func, zero_padding=True) # zero padding 
        data_repeat = prowav.prepro(frame_widh=frame_width, stride_width=stride_width,
                          mode=mode, ne_mels=n_mels, window_func=window_func, repeat_padding=True) # repeat padding 
        ```
        
        Just loading wave data is possible.  
        
        ```python 
        prowav = ProWav(["path/to/wave/data_1.wav", "path/to/wave/data_2.wav"]) 
        
        prowav.load_wav() # loading wav file into this class.
        
        prowav.data # the list of ndarray. Raw data are listed.
        ```
        
        You can choose parallel option.
        
        ```python 
        prowav.load_wav(parallel=True)
        
        prowav.load_wav(parallel=True, verbose=5)
        ```
        
        
        
Keywords: wave mfcc fft
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
