How to use crowsetta with your own annotation format

This section shows you how to use crowsetta for working with your own annotation format for vocalizations (or some other format not currently built into the library).

You can get the Jupyter notebook for this section by going to and clicking on the big green “Clone or Download” button on the right side of the screen. You can then find this notebook and others in the crowsetta/notebooks/ directory.

Steps to using crowsetta with your own annotation format

Below we’ll walk through a case study for using crowsetta with your annotation format. Here’s an outline of the steps we’ll go through:

  1. get your annotations into some variables in Python (maybe you already wrote code to do this)

  2. use one of the Sequence “factory functions” (we’ll explain what that means) to conveniently turn your annotations into Sequences

  3. turn the code you just wrote into a function that takes annotation files as an argument, and returns Sequences

  4. make a Transcriber that knows to use this function when you tell it you want to turn your annotation files into Sequences

Case Study: the BatLAB format

Let’s say you work in the Schumacher lab, studying bat vocalizations. The lab research specialist, Alfred, has spent years writing an application in Labview to capture bat calls, called SoNAR (“Sound and Neural Activity Recorder”). Alfred has also written a GUI in MATLAB called BatLAB that lets you interactively annotate audio files containing the bats’ calls, and saves the annotations in .mat (MATLAB data) files.

You’ve started to work with Python to analyze your data, because you like the data science and machine learning libraries. However, you find yourself writing the same code over and over again to unpack the annotations from the .mat files made by BatLAB. Every time you use the code for a new analysis, you have to modify it slightly. The code has some weird, hard-to-read lines to deal with the complicated MATLAB structs created by BatLAB and how they load into Python. The code also has several repetitive steps to deal with the idiosyncracies of how SoNAR and BatLAB save data: unit conversion, data types, etcetera. You can’t change BatLAB or SoNAR though, because that’s Alfred’s job, and everyone else’s code that was written ten years ago (and still works!) expects those idiosyncracies.

You know that it’s a good idea to turn the code you wrote into a function (because you took part in a Software Carpentry workshop and then you read this paper.) You figured out which bits of the code will be common to all your projects and you make that into a function, called parse_batlab_mat. At first you just copy and paste it into all your projects. Then you decide you also want to save everyone else in your lab the effort of writing the same code, so you put the script on your lab’s Github page. This is a step in the right direction, although parse_batlab_mat gives you back a Python list of dicts, and you end up typing a lot of things like:

labels = annot_list[0]['seg_type']
onsets = annot_list[0]['seg_onsets']
offsets = annot_list[0]['seg_offsets']

Typing all those very similar ['keys'] in particular gets kind of annoying and makes you wonder if you should spend your vacation learning how to use one of those hacker text editors like vim.

But before you can worry about that, you get back reviews of your paper in PLOS Comp. Bio. called “Pidgeon Bat: Emergence of Dialects in Colonies of Multiple Bat Species”. Reviewer #3 doesn’t buy your conclusions (and you are pretty sure from the way they write that it is Oswald Cobblepot, professor emeritus of ethology at Metropolitan University of Fruitville, Florida, and author of the seminal review from 1982, “Bat Calls: A Completely Innate Behavior Encoded Genetically”). You want to share your data with the world, mainly to mollify reviewer #3. The problem is that this reviewer (if he is who you think he is) only knows how to write Fortran code and is definitely not going to figure out how to copy and use your function parse_batlab_mat so he can run your analysis scripts and reproduce your figures for himself.

What you really want is to share your data and write your code in a way that doesn’t depend on anyone knowing anything about BatLAB orSoNAR and how those programs save data and annotations. This is where crowsetta comes to your rescue.

Okay, now that we’ve set up some background for our case study, let’s go through the steps we outlined above.

1. get your annotation into some variables in Python

Let’s look at this complicated data structure that we have our annotation in. The BatLAB GUI saves annotation into annotation.mat files with two variables:
- filenames: a vector where each element is the name of an audio file - annotations: a struct that has a record for each element in filenames, and that record is the annotation corresponding to the audio file with the same index in filenames
from import loadmat
bat1_annotation = loadmat('bat1_annotation.mat')
print('variables in .mat file:',
      [var for var in list(bat1_annotation.keys())
       if not var.startswith('__')]
variables in .mat file: ['filenames', 'annotations']

Below is the code you wrote to unpack the .mat files. Like we said above, the code has some weird, hard-to-read lines to deal with the way that the complicated MATLAB structs created by BatLAB load into Python, such as calling tolist() just to unpack an array, and some logic to make sure the labels get loaded correctly into a numpy array. And the code has several repetitive steps to deal with the idiosyncracies of SoNAR and BatLAB, like converting the start and stop times of the calls from seconds back to Hertz so you can find those times in the raw audio files.

# %load -r 7-8,14-46
mat = loadmat(mat_file, squeeze_me=True)
annot_list = []
for filename, annotation in zip(mat['filenames'], mat['annotations']):
    # below, .tolist() does not actually create a list,
    # instead gets ndarray out of a zero-length ndarray of dtype=object.
    # This is just weirdness that results from loading complicated data
    # structure in .mat file.
    seg_start_times = annotation['segFileStartTimes'].tolist()
    seg_end_times = annotation['segFileEndTimes'].tolist()
    seg_types = annotation['segType'].tolist()
    if type(seg_types) == int:
        # this happens when there's only one syllable in the file
        # with only one corresponding label
        seg_types = np.asarray([seg_types])  # so make it a one-element list
    elif type(seg_types) == np.ndarray:
        # this should happen whenever there's more than one label
        # something unexpected happened
        raise ValueError("Unable to load labels from {}, because "
                         "the segType parsed as type {} which is "
                         "not recognized.".format(filename,
    samp_freq = annotation['fs'].tolist()
    seg_start_times_Hz = np.round(seg_start_times * samp_freq).astype(int)
    seg_end_times_Hz = np.round(seg_end_times * samp_freq).astype(int)
    annot_dict = {
        'audio_file': filename,
        'seg_types': seg_types,
        'seg_start_times': seg_start_times,
        'seg_end_times': seg_end_times,
        'seg_start_times_Hz': seg_start_times_Hz,
        'seg_end_times_Hz': seg_end_times_Hz,

When it runs on a file, you end up with an annot_list where each item in the list is an annot_dict that contains the annotations for a file, like this:

annot_dict = {
    'seg_types': array([1, 1, 5, 2, ...]),
    'seq_start_times': array([0.00297619, 0.279125, 0.55564729,... ]),
    ... # end times, start and end times in Hertz

Again, as we said above, you turned your code into a function to make it easier to use across projects:

import numpy as np
from import loadmat

def parse_batlab_mat(mat_file):
    """parse batlab annotation.mat file"""
    # code from above
    return annot_list

As we’ll see in a moment, all you need to do is take this code you already wrote, and instead of returning your list of dicts, you return a list of Sequences.

2. use one of the Sequence “factory functions” to conveniently turn annotations in your format into Sequences

First, to get the Sequence, we’ll use a “factory function”, which just means it’s a function built into the Sequence class that gives us back an instance of a Sequence. One such factory function is Sequence.from_keyword. Here’s an example of using it:

from parsebat import parse_batlab_mat
from crowsetta.sequence import Sequence

# you, using the function you already wrote
annot_list = parse_batlab_mat(mat_file='bat1_annotation.mat')

# you have annotation from one file in an "annot_dict"
annot_dict = annot_list[0]

a_sequence = Sequence.from_keyword(labels=annot_dict['seg_types'],
print("a_sequence:\n", a_sequence)
 <Sequence with 15 segments>

3. turn the code we just wrote into a function that takes annotation files as an argument, and returns Sequences

Again, you pretty much already wrote this. Just take your parse_batlab_mat function from above and change a couple lines. First, you’re going to return a list of sequences instead of your annot_list from before. You probably want to make that explicit in your function.

# %load -r 4-7,24-25
from crowsetta.sequence import Sequence

def batlab2seq(mat_file):
    mat = loadmat(mat_file, squeeze_me=True)
    seq_list = []

Then at the end of your main loop, instead of making your annot_dict, you’ll make a new Sequence from each file using the from_keyword factory function, append the new Sequence to your seq_list, and then finally return that list of Sequences.

 # %load -r 56-63
 seq = Sequence.from_keyword(file=filename,
     return seq_list

If this still feels too wordy and repetitive for you, you can put
``segFileStartTimes``, ``segFileEndTimes``, et al., into a Python
``dict`` with ``keys`` corresponding to the parameters for
annot_dict = {
    'file': filename,
    'onsets_s': annotation['segFileStartTimes'].tolist(),
    'offsets_s': annotation['segFileEndTimes'].tolist()
    'labels': seg_types

Note here that you only have to specify the onsets an offsets of
segments *either* in seconds or in Hertz (but you can define

and then use another factory function, Sequence.from_dict, to create the Sequence.


Now that you have a function that takes annotation files and return Sequences, call it something like batlab2seq and put it in a file that ends with .py, e.g. This is also known as a Python module (as you’ll need to know below). To see the entire example, check out the file in this folder (and compare it with

4. make a Transcriber that knows to use this function when you tell it you want to turn your annotation files into Sequences

If you have worked with Crowsetta already, or gone through the tutorial, you know that we can work with a Transcriber that does the work of making Sequences of Segments from annotation files for us. We create a new instance of a Transcriber by writing something like this:

scribe = Transcriber()

You will do the same thing here, but to tell the Transcriber how to work with your format, you will pass an argument for the user_config parameter when you create a new one:

scribe = Transcriber(user_config=your_config)

The argument you pass for user_config will be a Python dictionary with the following structure:

your_config = {
    'batlab': {
        'module': '',
        'to_seq': 'batlab2seq',
        'to_csv': 'None',
        'to_format': 'None',

Notice that this a dictionary of dictionaries, where each key in the top-level dict is the name of a user-defined format, here batlab. If you had multiple formats to use, you would add more dicts inside the top-level dict.

The value for each key is another Python dictionary that tells the Transcriber what functions to use from your module when you call one of its methods and specify this format. In the example above, you’re telling the Transcriber that when you say file_format='batlab', it should use functions from the module. More specifically, when you call scribe.to_seq(file='annotation.mat', file_format='batlab'), it should use the batlab2seq function to convert your annotation into Sequences. Notice also that you can specify 'None' for to_csv and to_format (which would be a function that converts Sequences back to the BatLAB format).

Here’s what it looks like to do all of that in a few lines of code:

from crowsetta import Transcriber

your_config = {
    'batlab': {
        'module': '',
        'to_seq': 'batlab2seq',

scribe = Transcriber(user_config=your_config)

seq_list = scribe.to_seq(file='bat1_annotation.mat', file_format='batlab')

And now, just like you do with the built-in formats, you get back a list of Sequences from your format:

print(f'First item in seq_list: {seq_list[0]}')
print(f'First segment in first sequence:\n{seq_list[0].segments[0]}')
First item in seq_list: <Sequence with 15 segments>
First segment in first sequence:
Segment(label='1', file='lbr3009_0005_2017_04_27_06_14_46.wav', onset_s=0.0029761904761904934, offset_s=0.14150432900432905, onset_Hz=143, offset_Hz=6792)

Notice that we also get a to_csv function for free:


import csv
with open('test.csv', 'r', newline='') as csv_file:
     reader = csv.reader(csv_file)
     for _ in range(4):
['label', 'onset_s', 'offset_s', 'onset_Hz', 'offset_Hz', 'file']
['1', '0.0029761904761904934', '0.14150432900432905', '143', '6792', 'lbr3009_0005_2017_04_27_06_14_46.wav']
['1', '0.279125', '0.504625', '13398', '24222', 'lbr3009_0005_2017_04_27_06_14_46.wav']
['5', '0.5556472915365209', '0.5962916666666667', '26671', '28622', 'lbr3009_0005_2017_04_27_06_14_46.wav']

How does that work? Well, as long as we can convert our annotation format to Sequences, then we can pass those Sequences to the crowsetta.csv2seq function, which will output them as a .csv file. The Transcriber does this by default. Under the hood, when you make a new Transcriber with your user_config, it wraps your format2seq function and the seq2csv function into one, using the function crowsetta.csv.toseq_func_to_csv.


Now you have seen in detail the process of working with your own annotation format in Crowsetta. Here’s a review of the steps, with some code snippets worked in to tie it all together:

  1. get your annotations into some variables in Python, perhaps using code you already wrote

  2. use one of the Sequence “factory functions” to conveniently turn your annotations into Sequences

  3. turn all that code into a function that takes annotation files as an argument, and returns Sequences

steps 1-3 will give you something like this in a file named something like

from Crowsetta import Sequence

def myformat2seq(my_format_files):
    seq_list = []
    for format_file in my_format_files:
    # load annotation into some Python variables, e.g. a dictionary
        annot_dict = magic_annotation_unpacking_function(format_file)
        seq = Sequence.from_dict(annot_dict)
    return seq_list
  1. make a Transcriber that knows to use this function when you tell it you want to turn your annotation files into Sequences, and/or csv files, or to convert back to your format from Sequences (assuming you wrote a function in your module that will do so).

from Crowsetta import Transcriber

my_config = {
    'my_format': {
        'module': '',
        'to_seq': 'myformat2seq',
        'to_csv': 'myformat2csv',
        'to_format': 'seq2myformat,
scribe = Transcriber(user_config=my_config)
seq_list = scribe.to_seq(file='my_annotations.txt', file_format='my_format')