https://github.com/bmcfee/pumpp

practically universal music pre-processor
https://github.com/bmcfee/pumpp

machine-learning music nyucds python

Last synced: about 1 year ago
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practically universal music pre-processor

• Host: GitHub
• URL: https://github.com/bmcfee/pumpp
• Owner: bmcfee
• License: isc
• Created: 2016-07-03T14:29:43.000Z (almost 10 years ago)
• Default Branch: main
• Last Pushed: 2022-04-19T21:06:45.000Z (about 4 years ago)
• Last Synced: 2025-04-14T07:08:10.732Z (about 1 year ago)
• Topics: machine-learning, music, nyucds, python
• Language: Python
• Size: 332 KB
• Stars: 61
• Watchers: 6
• Forks: 11
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md

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README

          
# pumpp

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[![Documentation Status](http://readthedocs.org/projects/pumpp/badge/?version=latest)](http://pumpp.readthedocs.io/en/latest/?badge=latest)

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

```python

>>> 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)

```