Metadata-Version: 2.1
Name: ProcessAudio
Version: 0.22.11
Summary: Procesamiento de audios
Home-page: https://github.com/wisrovi/ProcessAudio
Author: William Rodriguez
Author-email: wisrovi.rodriguez@gmail.com
License: GPLv3
Download-URL: https://github.com/wisrovi/ProcessAudio/releases/tag/V0.22.11
Description: # ProcessAudio
         Libreria python para hacer data augmentation en audios y/o extraer caracteristicas a audios
        
        ---
        
        # Installation
        
        ```bash
        pip install ProcessAudio
        ```
        ---
        
        # Description
        
        A `ProcessAudio` object should be created and use its attributes.
        
        This library have tree main functions:
        
        * `Features`: Extract features from audio
        * `AudioAugmentation`: Augment audio in different ways
        * `AllDataAugmentation`: Augment audio in different ways and extract features
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        ## Features methods
        
        * `set_data(data_audio:str="<path_audio_file>)`: Set data to extract features
        * `get_croma()`: Extract croma features
        * `get_mfcc()`: Extract mfcc features
        * `get_rmse()`: Extract rmse features
        * `get_centroide_espectral()`: Extract spectral centroid features
        * `get_rolloff()`: Extract spectral rolloff features
        * `get_cruce_por_cero()`: Extract zero crossing rate features
        * `get_ancho_banda_espectral()`: Extract spectral bandwidth features
        * `build_all()`: Extract all features in a list
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        ## AudioAugmentation methods
        
        * `loudness()`: Apply loudness to audio file creating a new data
        * `add_mask()`: Apply mask to audio file creating a new data
        * `pitch()`: Apply pitch to audio file creating a new data
        * `get_original()`: Get original audio file
        * `add_crop()`: Apply crop to audio file creating a new data
        * `add_noise()`: Apply noise to audio file creating a new data
        * `add_noise2()`: Apply noise to audio file creating a new data
        * `shift()`: Apply shift to audio file creating a new data
        * `stretch()`: Apply stretch to audio file creating a new data
        * `speed()`: Apply speed to audio file creating a new data
        * `normalizer()`: Apply normalizer to audio file creating a new data
        * `polarizer()`: Apply polarizer to audio file creating a new data
        * `write_audio_file()`: Write audio file
        * `plot_time_series()`: Plot time series
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        ## AllDataAugmentation methods
        
        * `build_all(extract_features: bool)`: Augment audio and extract features if extract_features is True
        
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        # Usage
        
        ## Example Features
        ```python
        import os
        from ProcessAudio.Features import Features
        
        filepath = os.path.dirname(os.path.abspath(__file__)) + os.sep
        path_file = filepath + "demo" + os.sep + "dat_92.wav"
        
        features = Features()
        features.set_data(path_file)
        DATA = features.build_all() # Extract all features
        print(DATA)
        print(len(DATA))  
        ```
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        ## Example AudioAugmentation
        ```python
        import os
        from ProcessAudio.AudioAugmentation import AudioAugmentation
        
        filepath = os.path.dirname(os.path.abspath(__file__)) + os.sep
        path_file = filepath + "demo" + os.sep + "dat_92.wav"
        folder_save = filepath + "new_audios" + os.sep
        
        aumentation = AudioAugmentation(audio_file=path_file, save=folder_save)
        audio_con_ruido = aumentation.add_noise(factor_ruido=0.05)
        audio_normalizer = aumentation.normalizer()
        audio_loudness = aumentation.loudness()
        ```
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        ## Example AllDataAugmentation
        ```python
        import os
        from ProcessAudio.AllDataAugmentation import AllDataAugmentation
        
        filepath = os.path.dirname(os.path.abspath(__file__)) + os.sep
        path_file = filepath + "demo" + os.sep + "dat_92.wav"
        folder_save = filepath + "new_audios" + os.sep
        
        aumentation = AllDataAugmentation(path_file, path_save=folder_save, label=["cero", "uno"])
        data, label = aumentation.build_all(extract_features=True)
        print(len(data), len(label))
        print(len(data[0]), label[0])
        ```
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        # Citing
        
        
        If you want to cite ProcessAudio in an academic paper, there are two ways to do it.
        
        - APA:
        
            WISROVI, W.S.R.V. (2022). Python library to augment audio data and/or extract audio features (Version 0.22.11) [Computer Software]. https://github.com/wisrovi/ProcessAudio
        
        - BibTex:
        
            @software{WISROVI_Instrument_Classifier_2022,
        author = {WISROVI, William Steve RodrÃ­guez Villamizar},
        month = {10},
        title = {{Python library to augment audio data and/or extract audio features}},
        URL = {https://github.com/wisrovi/ProcessAudio},
        version = {0.22.11},
        year = {2022}
        }
        
        
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        # License
        
        GPLv3 License
        
        ---
        
        [Back To Top â†¥](#ProcessAudio)
        
        # Support:
        <p>
            <a href="https://www.buymeacoffee.com/wisrovirod8"> 
                <img align="left" src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" height="50" width="210" alt="wisrovirod8" />
            </a>    
        </p>
        
        
Keywords: encoding,i18n,xml
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
