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  We present the BirdSet benchmark that covers a comprehensive range of classification datasets in avian bioacoustics.
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  We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies.
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  | | train | test | test_5s | size (GB) | #classes |
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  |--------------------------------|--------:|-----------:|--------:|-----------:|-------------:|
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  | [PER][1] (Amazon Basin) | 16,802 | 14,798 | 15,120 | 10.5 | 132 |
@@ -73,13 +75,13 @@ We offer a static set of evaluation datasets and a varied collection of training
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  #### Metadata
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- | | format datasets. | description |
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  |------------------------|-------------------------------------------------------:|-------------------------:|
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- | audio | Audio(sampling_rate=32_000, mono=True, decode=True) | |
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- | filepath | Value("string") | |
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- | start_time | Value("float64") | |
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- | end_time | Value("float64") | |
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- | low_freq | Value("int64") | |
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  | high_freq | Value("int64") | |
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  | ebird_code | ClassLabel(names=class_list) | |
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  | ebird_code_multilabel | Sequence(datasets.ClassLabel(names=class_list)) | |
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  #### Example Metadata Train
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  ```python
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- EXAMPLE TRAIN
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  {'audio': {'path': '.ogg',
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  'array': array([ 0.0008485 , 0.00128899, -0.00317163, ..., 0.00228528,
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  0.00270796, -0.00120562]),
 
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  We present the BirdSet benchmark that covers a comprehensive range of classification datasets in avian bioacoustics.
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  We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies.
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+ We have a complementary code base: https://github.com/DBD-research-group/BirdSet
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+
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  | | train | test | test_5s | size (GB) | #classes |
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  |--------------------------------|--------:|-----------:|--------:|-----------:|-------------:|
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  | [PER][1] (Amazon Basin) | 16,802 | 14,798 | 15,120 | 10.5 | 132 |
 
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  #### Metadata
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+ | | format | description |
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  |------------------------|-------------------------------------------------------:|-------------------------:|
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+ | audio | Audio(sampling_rate=32_000, mono=True, decode=True) | audio object from hf |
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+ | filepath | Value("string") | path where the recording is saved |
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+ | start_time | Value("float64") | only testdata:start time of a vocalization if the ground truth label is given |
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+ | end_time | Value("float64") | only testdata: end time of a vocalzation if the ground truth label is given |
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+ | low_freq | Value("int64") | |
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  | high_freq | Value("int64") | |
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  | ebird_code | ClassLabel(names=class_list) | |
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  | ebird_code_multilabel | Sequence(datasets.ClassLabel(names=class_list)) | |
 
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  #### Example Metadata Train
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  ```python
 
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  {'audio': {'path': '.ogg',
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  'array': array([ 0.0008485 , 0.00128899, -0.00317163, ..., 0.00228528,
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  0.00270796, -0.00120562]),