practically universal music pre-processor
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.


GitHub license Build Status Coverage Status Dependency Status Documentation Status

practically universal music pre-processor

pumpp up the jams

The goal of this package is to make it easy to convert pairs of (audio, jams) into data that can be easily consumed by statistical algorithms. Some desired features:

  • Converting tags to sparse encoding vectors
  • Sampling (start, end, label) to frame-level annotations at a specific frame rate
  • Extracting input features (eg, Mel spectra or CQT) from audio
  • Converting between annotation spaces for a given task

Example usage

>>> import jams
>>> import pumpp

>>> audio_f = '/path/to/audio/myfile.ogg'
>>> jams_f = '/path/to/annotations/myfile.jamz'

>>> # Set up sampling and frame rate parameters
>>> sr, hop_length = 44100, 512

>>> # Create a feature extraction object
>>> p_cqt = pumpp.feature.CQT(name='cqt', sr=sr, hop_length=hop_length)

>>> # Create some annotation extractors
>>> p_beat = pumpp.task.BeatTransformer(sr=sr, hop_length=hop_length)
>>> p_chord = pumpp.task.SimpleChordTransformer(sr=sr, hop_length=hop_length)

>>> # Collect the operators in a pump
>>> pump = pumpp.Pump(p_cqt, p_beat, p_chord)

>>> # Apply the extractors to generate training data
>>> data = pump(audio_f=audio_f, jam=jams_fjams_f)

>>> # Or test data
>>> test_data = pump(audio_f='/my/test/audio.ogg')

>>> # Or in-memory
>>> y, sr = librosa.load(audio_f)
>>> test_data = pump(y=y, sr=sr)