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Dynibatch is a Python library dedicated to providing labeled mini-batches of audio data to machine learning algorithms.

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Dynibatch

Dynibatch is a Python library dedicated to providing mini-batches of audio data to machine learning algorithms.

It has been designed to deal with the following issues:

  • Reproducibility: given a dataset (data + train/valid/test split + labels) and some parameters (e.g. segment size and overlap, audio feature to be used...), an experiment should be easily reproducible. Dynibatch allows to keep all this information in dedicated objects and a configuration file.

  • Big data: because some datasets are huge, Dynibatch keeps a low memory footprint by generating mini-batches on the fly. To avoid recomputing at every epoch the data needed to generate the mini-batches, it can be cached in the disk.

  • Label management: labels are typically provided either per file (i.e. one label for the whole audio file) or per chunk (i.e. one label for an audio chunk, delimited by a start time and an end time). In either case, Dynibatch automatically maps the labels provided with the dataset to one label per segment, where a segment is one fixed-size observation (see the tutorial for more details). Labels are not mandatory, so that unsupervised algorithms can be run.

  • Usability: with a given config file, generating mini-batches is as easy as

      mb_gen = MiniBatchGen.from_config(config)
      mb_gen_e = mb_gen.execute(with_targets=True)
    
      # get the first mini-batch
      data, targets = next(mb_gen_e)
    

More details and examples can be found in the tutorial.

Install

The instructions below have been tested on Ubuntu 16.04 and Miniconda + Python 3.5.

Get Dynibatch source

Clone repository.

Create conda environment

$ cd dynibatch
$ conda env create -f dynibatch.yml

Activate conda environment

$ source activate dynibatch

Add dynibatch to your PYTHONPATH

$ export PYTHONPATH=$PYTHONPATH:<path to dynibatch>

Test

Make sure all the tests pass.

$ py.test tests

Examples

See tutorial.

Dependencies

The list of dependencies is provided as information only, since they should all be installed during the creation of the dynibatch conda environment.

Contact

Julien Ricard
Vincent Roger
Herv� Glotin
DYNI, LSIS, University of Toulon, France

dynicontactgmail.com

About

Dynibatch is a Python library dedicated to providing labeled mini-batches of audio data to machine learning algorithms.

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