semvec/pypi-packages huggingface dataset:
prowav 0.6
The package for preprocessing wave data
License: MIT
Keywords: wave mfcc fft

# 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)
```