The iNaturalist Sounds Dataset

We present the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists worldwide. The dataset encompasses sounds from birds, mammals, insects, reptiles, and amphibians, with audio and species labels derived from observations submitted to iNaturalist, a global citizen science platform. Each recording in the dataset varies in length and includes a single species annotation. We benchmark multiple backbone architectures, comparing multiclass classification objectives with multilabel objectives. Despite weak labeling, we demonstrate that iNatSounds serves as a robust pretraining resource, achieving high performance relative to alternatives on strongly labeled downstream evaluation datasets. The dataset is available as a single, freely accessible archive, promoting accessibility and research in this important domain. We envision models trained on this data powering next-generation public engagement applications, and assisting biologists, ecologists, and land use managers in processing large audio collections, thereby contributing to the understanding of species compositions in diverse soundscapes.

Details

Super Category	Train	Train	Val/Test	Val	Test
	Species	Audio	Species	Audio	Audio
Aves	3,846	111,029	939	37,597	41,036
Insecta	745	10,080	111	3,065	3,305
Amphibia	650	13,183	118	4,004	4,081
Mammalia	296	2,566	41	983	1,073
Reptilia	32	154	3	49	32
Total	5,569	137,012	1,212	45,698	49,527

Evaluation

We evaluate at the "file-level", i.e. a single prediction and label is expected from an entire audio recording. We compute the following metrics

1. Class Averaged Top-1: Top-1 accuracy is computed for each class separately and then averaged
2. Class Averaged Top-5: Top-5 accuracy is computed for each class separately and then averaged
3. mAP: AP is computed for each class separately and then averaged
4. mF1: F1-score is computed for each class by choosing the threshold maximising F1 (there may be different thresholds for different classes). These F1 scores are averaged

Annotation Format

{
  "info" : info,
  "audio" : [audio],
  "categories" : [category],
  "annotations" : [annotation],
  "licenses" : [license]
}

info{
  "year" : int,
  "version" : str,
  "description" : str,
  "contributor" : str,
  "url" : str,
  "date_created" : str,
}

audio{
  "id" : int,
  "duration" : float,
  "file_name" : str,
  "license" : int,
  "rights_holder" : str,
  "date": str,
  "latitude": float,
  "longitude": float,
  "location_uncertainty": int,
}

category{
  "id" : int,
  "name" : str,
  "common_name" : str,
  "supercategory" : str,
  "kingdom" : str,
  "phylum" : str,
  "class" : str,
  "order" : str,
  "family" : str,
  "genus" : str,
  "specific_epithet" : str,
  "audio_dir_name" : str,
  "evaluation": 0 or 1
}

annotation{
  "id" : int,
  "audio_id" : int,
  "category_id" : int
}

license{
  "id" : int,
  "name" : str,
  "url" : str
}

Annotation Format Notes:

• It is possible for the location_uncertainty field to be null or nan when it was missing from the respective iNaturalist observation.

Terms of Use

By downloading this dataset you agree to the following terms:

1. You will abide by the iNaturalist Terms of Service.
2. You will use the data only for non-commercial research and educational purposes.
3. You will NOT distribute the dataset recordings.
4. UMass Amherst makes no representations or warranties regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose.
5. You accept full responsibility for your use of the data and shall defend and indemnify UMass Amherst, including its employees, officers and agents, against any and all claims arising from your use of the data, including but not limited to your use of any copies of copyrighted images that you may create from the data.

Data

The dataset files are available through the AWS Open Data Program:

• Train Recordings [81GB]
  • s3://ml-inat-competition-datasets/sounds/2024/train.tar.gz
  • Running md5sum train.tar.gz should produce 181c5419220c8a9468a12b8c938469ec
  • Recordings have a sample rate of 22.05kHz and have been converted to WAV format
  • Untaring the recordings creates a directory structure like train/category/recording.wav. This may take a while.
• Train Annotations [14MB]
  • s3://ml-inat-competition-datasets/sounds/2024/train.json.tar.gz
  • Running md5sum train.json.tar.gz should produce d0ae17ef52078194d450a83b5efe68a4
• Validation Recordings [25GB]
  • s3://ml-inat-competition-datasets/sounds/2024/val.tar.gz
  • Running md5sum val.tar.gz should produce 828e4cf96e818228cb3536469cb3fb1c
  • Recordings have a sample rate of 22.05kHz and have been converted to WAV format
  • Untaring the recordings creates a directory structure like val/category/recording.wav. This may take a while.
• Validation Annotations [3.7MB]
  • s3://ml-inat-competition-datasets/sounds/2024/val.json.tar.gz
  • Running md5sum val.json.tar.gz should produce 8e6e4b9628d43e36f1fea4621945403a
• Test Recordings [27GB]
  • s3://ml-inat-competition-datasets/sounds/2024/test.tar.gz
  • Running md5sum test.tar.gz should produce 082a2e96ca34f6c9f61767e33cbf3626
  • Recordings have a sample rate of 22.05kHz and have been converted to WAV format
  • Untaring the recordings creates a directory structure like test/category/recording.wav. This may take a while.
• Test Annotations [4.0MB]
  • s3://ml-inat-competition-datasets/sounds/2024/test.json.tar.gz
  • Running md5sum test.json.tar.gz should produce 5482f112f05db4ba8576ba425d95d6a7