Data formats

Format of exported annotations and audio

Produced by the GUI via File/Save annotations and File/Export for DeepSS.

Annotations are exported to csv (comma-separated values) and audio data to npz (zip compressed numpy files) or wav (wave audio file):

  • .npz consist of two variables:

    • data: [samples, channels] array with the audio data

    • samplerate: [1,] array with the sample rate in Hz

  • _annotations.csv contains three columns:

    • name - the name of the song or syllable type

    • start_seconds - the start time of the syllable.

    • stop_seconds - the stop of the syllable. Start and stop are identical for song types of type event, like the pulses of fly song.

    • Each row in the file contains to a single annotation with name, start_seconds and stop_seconds. Special rows a reserved for song types without any annotations: For syllables or other segment types, the consist of the name, start_seconds is np.nan and an arbitrary stop_seconds. For event-like types (song pulses), both start_seconds and stop_seconds are np.nan.

Data structure used for training

For training, DeepSS expects a simple dictionary-like data structure, sth like:

  ├── ['train']
  │      ├── ['x']         (the audio data - samples x channels)
  │      ├── ['y']        (annotations - samples x song types, first one is noise, needs to add to )
  │      ├── ['y_suffix1'] (optional, multiple allowed)
  ├── ['val']
  │      ├── ['x']
  │      ├── ['y']
  │      ├── ['y_suffix1']
  ├── ['test']
  │      ├── ['x']
  │      ├── ['y']
  │      ├── ['y_suffix1']
  └── attrs
         └── ['samplerate'] (of x and y in Hz)
             ['class_types'] (event or segment)
             ['class_types_suffix1'] (event or segment)

Top-level keys train, val, and test correspond to the data splits for training, validation, and test. Training and validation data are required. Validation is used during training to monitor progress and adjust the learning rate or stop training early. Test data is optional - it is used after training to assess the performance of the model.

Each a top-level key contains a dict with the following keys:

  • Inputs x: [samples, channels]

    • First dim is always samples, last is typically audio channels (>=1).

    • For spectrogram representations, could also be frequency channels [samples, freqs] for single-channel recordings. For multi-channel data time, the frequency channels from the spectrum of each audio channels can be stacked to [time, channels*freqs]

  • Targets y for each sample in ‘x’: [samples, nb_classes]

    • Binary (0/1) or a probability but should sum to 1.0 across classes for each sample

    • Multiple targets can be specified by adding target variables with a suffix y_somesuffix. That allows you to train the a network with the same x but different y’s (e.g. pulse or sine-only). Must named like y_somesuffix, where somesuffix can be an arbitrary string. The target can be specified during training with the y_suffix=somesuffix argument, which defaults to ‘’ (the standard target w/o suffix y). Training y-suffixes also requires suffix-specific attributes specifying the name and type of the target - classnames_somesuffix and classtype_somesuffix.

  • Metadata in dict data.attrs:

    • samplerate_x_Hz, samplerate_y_Hz - should be equal

    • samplerate_song_Hz (optional) - of song, the original recording in case x is the result of a wavelet or spectrogram transform

    • class name and type information for each target:

      • classnames: [nb_classes: str] - one for each 2nd dim in y

      • classtype: [nb_classes: str] (optional for training) - event (e.g. pulse) or segment (sine or syllable)

      • for each target specified via y_somesuffix, create a classnames_suffix and classnames_somesuffix attribute

Data is accessed via data['train']['x'] and the metadata via data.attrs. attrs is a standard attribute for storing metadata in hdf5 and zarr files and can be easily attached to a standard dictionary: a = dict(); a.attrs = {'samplerate_x_Hz': 10_000}.

This structure can be implemented via python’s builtin dictionary, hdf5, xarray, zarr, or anything else that implements a key-value interface (called a Mapping in python).

We provide a alternative storage backend, npy_dir, that mirrors the data structure in directory hierarchy with numpy’s npy files (inspired by Cyrille Rossant’s series of blog posts (1, 2), jbof and exdir). Keys map to directories; values and attrs map to npy files (see dss.npy_dir). For instance, data['train']['x'] is stored in dirname.npy/train/x.npy. attrs is stored in the top-level directory. Storing data as npy files provides a fast memory-mapping mechanism for out-of-memory access if your data set does not fit in memory. While zarr, h5py, and xarray provide mechanisms for out-of-memory access, they tend to be slower in our experience or require fine tuning to reach the performance reached with memmapped npy files.

During training, data is loaded (see with the correct backend given by the extension of the file or directory. Currently supported files types are:

  • zarr storages (files or directories ending in .zarr),

  • hdf5 files (files ending in .h5),

  • directories ending in .npy with numpy files.

zarr is great for assembling and storing datasets, in particular for datasets with unknown final size since it allows appending to arrays, and is used as an intermediate storage during dataset assembly. For training, npy is the fastest, in particular for random access of datasets that are too large to fit in memory, thanks to highly efficient memory mapping.