:bell: Pydiogment

Pydiogment aims to simplify audio augmentation. It generates multiple audio files based on a starting mono audio file. The library can generates files with higher speed, slower, and different tones etc.

:inbox_tray: Installation

Dependencies

Pydiogment requires:

• Python (>= 3.5)

• NumPy (>= 1.17.2) pip install numpy

• SciPy (>= 1.3.1) pip install scipy

• FFmpeg sudo apt install ffmpeg

Installation

If you already have a working installation of NumPy and SciPy , you can simply install Pydiogment using pip:

pip install pydiogment

To update an existing version of Pydiogment, use:

pip install -U pydiogment

:bulb: How to use

• Amplitude related augmentation

  • Apply a fade in and fade out effect

    from pydiogment.auga import fade_in_and_out
    
    test_file = "path/test.wav"
    fade_in_and_out(test_file)
    

  • Apply gain to file

    from pydiogment.auga import apply_gain
    
    test_file = "path/test.wav"
    apply_gain(test_file, -100)
    apply_gain(test_file, -50)
    

  • Add Random Gaussian Noise based on SNR to file

    from pydiogment.auga import add_noise
    
    test_file = "path/test.wav"
    add_noise(test_file, 10)
    

• Frequency related augmentation

  • Change file tone

    from pydiogment.augf import change_tone
    
    test_file = "path/test.wav"
    change_tone(test_file, 0.9)
    change_tone(test_file, 1.1)
    

• Time related augmentation

  • Slow-down/ speed-up file

    from pydiogment.augt import slowdown, speed
    
    test_file = "path/test.wav"
    slowdown(test_file, 0.8)
    speed(test_file, 1.2)
    

  • Apply random cropping to the file

    from pydiogment.augt import random_cropping
    
    test_file = "path/test.wav"
    random_cropping(test_file, 1)
    

  • Change shift data on the time axis in a certain direction

    from pydiogment.augt import shift_time
    
    test_file = "path/test.wav"
    shift_time(test_file, 1, "right")
    shift_time(test_file, 1, "left")
    

:bookmark_tabs: Documentation

A thorough documentation of the library is available under pydiogment.readthedocs.io.

:construction_worker: Contributing

Contributions are welcome and encouraged. To learn more about how to contribute to Pydiogment please refer to the Contributing guidelines

:tada: Acknowledgment and credits

• The test file used in the pytests is OSR_us_000_0060_8k.wav from the Open Speech Repository.

:bell: Pydiogment

Pydiogment aims to simplify audio augmentation. It generates multiple audio files based on a starting mono audio file. The library can generates files with higher speed, slower, and different tones etc.

:inbox_tray: Installation

Dependencies

Pydiogment requires:

• Python (>= 3.5)

• NumPy (>= 1.17.2) pip install numpy

• SciPy (>= 1.3.1) pip install scipy

• FFmpeg sudo apt install ffmpeg

Installation

If you already have a working installation of NumPy and SciPy , you can simply install Pydiogment using pip:

pip install pydiogment

To update an existing version of Pydiogment, use:

pip install -U pydiogment

:bulb: How to use

• Amplitude related augmentation

  • Apply a fade in and fade out effect

    from pydiogment.auga import fade_in_and_out
    
    test_file = "path/test.wav"
    fade_in_and_out(test_file)
    

  • Apply gain to file

    from pydiogment.auga import apply_gain
    
    test_file = "path/test.wav"
    apply_gain(test_file, -100)
    apply_gain(test_file, -50)
    

  • Add Random Gaussian Noise based on SNR to file

    from pydiogment.auga import add_noise
    
    test_file = "path/test.wav"
    add_noise(test_file, 10)
    

• Frequency related augmentation

  • Change file tone

    from pydiogment.augf import change_tone
    
    test_file = "path/test.wav"
    change_tone(test_file, 0.9)
    change_tone(test_file, 1.1)
    

• Time related augmentation

  • Slow-down/ speed-up file

    from pydiogment.augt import slowdown, speed
    
    test_file = "path/test.wav"
    slowdown(test_file, 0.8)
    speed(test_file, 1.2)
    

  • Apply random cropping to the file

    from pydiogment.augt import random_cropping
    
    test_file = "path/test.wav"
    random_cropping(test_file, 1)
    

  • Change shift data on the time axis in a certain direction

    from pydiogment.augt import shift_time
    
    test_file = "path/test.wav"
    shift_time(test_file, 1, "right")
    shift_time(test_file, 1, "left")
    

:bookmark_tabs: Documentation

A thorough documentation of the library is available under pydiogment.readthedocs.io.

:construction_worker: Contributing

Contributions are welcome and encouraged. To learn more about how to contribute to Pydiogment please refer to the Contributing guidelines

:tada: Acknowledgment and credits

• The test file used in the pytests is OSR_us_000_0060_8k.wav from the Open Speech Repository.

pydiogment

pydiogment aims to simplify audio augmentation. It generates multiple audio files based on a starting mono audio file. The library can generates files with higher speed, slower, and different tones etc.

Installation

Dependencies

pydiogment requires:

• Python (>= 3.5)

• NumPy (>= 1.17.2) pip install numpy

• SciPy (>= 1.3.1) pip install scipy

• FFmpeg sudo apt install ffmpeg

Installation

If you already have a working installation of numpy and scipy, you can simply install pydiogment using pip:

pip install -U pydiogment

How to use

• Amplitude related augmentation

  • Apply a fade in and fade out effect

    from pydiogment.auga import fade_in_and_out
    
    test_file = "path/test.wav"
    fade_in_and_out(test_file)
    

  • Apply gain to file

    from pydiogment.auga import apply_gain
    
    test_file = "path/test.wav"
    apply_gain(test_file, -100)
    apply_gain(test_file, -50)
    

  • Add Random Gaussian Noise based on SNR to file

    from pydiogment.auga import add_noise
    
    test_file = "path/test.wav"
    add_noise(test_file, 10)
    

• Frequency related augmentation

  • Change file tone

    from pydiogment.augf import change_tone
    
    test_file = "path/test.wav"
    change_tone(test_file, 0.9)
    change_tone(test_file, 1.1)
    

• Time related augmentation

  • Slow-down/ speed-up file

    from pydiogment.augt import slowdown, speed
    
    test_file = "path/test.wav"
    slowdown(test_file, coefficient=0.8)
    speed(test_file, coefficient=1.2)
    

  • Apply random cropping to the file

    from pydiogment.augt import random_cropping
    
    test_file = "path/test.wav"
    random_cropping(test_file, 1)
    

  • Change shift data on the time axis in a certain direction

    from pydiogment.augt import shift_time
    
    test_file = "path/test.wav"
    shift_time(test_file, 1,"right")
    shift_time(test_file, 1,"left")
    

Documentation

A thorough documentation of the library is available under pydiogment.readthedocs.io.

Contributing

Contributions are welcome and encouraged. To learn more about how to contribute to pydiogment please refer to the Contributing guidelines

Acknowledgment and credits

• The test file used in the pytests is OSR_us_000_0060_8k.wav from the Open Speech Repository.

pydiogment

pydiogment aims to simplify audio augmentation. It generates multiple audio files based on a starting mono audio file. The library can generates files with higher speed, slower, and different tones etc.

Installation

Dependencies

pydiogment requires:

• Python (>= 3.5)

• NumPy (>= 1.17.2) pip install numpy

• SciPy (>= 1.3.1) pip install scipy

• FFmpeg sudo apt install ffmpeg

Installation

If you already have a working installation of numpy and scipy, you can simply install pydiogment using pip:

pip install -U pydiogment

How to use

• Amplitude related augmentation

  • Apply a fade in and fade out effect

    from pydiogment.auga import fade_in_and_out
    
    test_file = "path/test.wav"
    fade_in_and_out(test_file)
    

  • Apply gain to file

    from pydiogment.auga import apply_gain
    
    test_file = "path/test.wav"
    apply_gain(test_file, -100)
    apply_gain(test_file, -50)
    

  • Add Random Gaussian Noise based on SNR to file

    from pydiogment.auga import add_noise
    
    test_file = "path/test.wav"
    add_noise(test_file, 10)
    

• Frequency related augmentation

  • Change file tone

    from pydiogment.augf import change_tone
    
    test_file = "path/test.wav"
    change_tone(test_file, 0.9)
    change_tone(test_file, 1.1)
    

• Time related augmentation

  • Slow-down/ speed-up file

    from pydiogment.augt import slowdown, speed
    
    test_file = "path/test.wav"
    slowdown(test_file, coefficient=0.8)
    speed(test_file, coefficient=1.2)
    

  • Apply random cropping to the file

    from pydiogment.augt import random_cropping
    
    test_file = "path/test.wav"
    random_cropping(test_file, 1)
    

  • Change shift data on the time axis in a certain direction

    from pydiogment.augt import shift_time
    
    test_file = "path/test.wav"
    shift_time(test_file, 1,"right")
    shift_time(test_file, 1,"left")
    

Documentation

A thorough documentation of the library is available under pydiogment.readthedocs.io.

Contributing

Contributions are welcome and encouraged. To learn more about how to contribute to pydiogment please refer to the Contributing guidelines

Acknowledgment and credits

• The test file used in the pytests is OSR_us_000_0060_8k.wav from the Open Speech Repository.