Update README.md

README.md

@@ -12,38 +12,7 @@ tags:
 - **Paper:** [GADME](https://arxiv.org/abs/2403.10380)
 - **Point of Contact:** [Lukas Rauch](mailto:lukas.rauch@uni-kassel.de)
 
-### Quick Use
-- We recommend to use our [intro notebook](https://github.com/DBD-research-group/BirdSet/blob/main/notebooks/tutorials/birdset-pipeline_tutorial.ipynb) in our code repository
-- The BirdSet Code package simplfies the data processing steps 
-- For multi-label evaluation with a segment-based evaluation use the test_5s column for testing.
-
-We provide a very short example where no additional code is required. We load the first 5 seconds to quickly create an examplary training dataset.
-We recommend to start with HSN. It is a medium size dataset with a low number of overlaps within a segment.
-
-```python
-from datasets import Audio
-
-dataset = load_dataset("DBD-research-group/BirdSet", "HSN")
-
-# slice example
-dataset["train"] = dataset["train"].select(range(500))
 
-# the dataset comes without an automatic Audio casting, this has to be enabled via huggingface
-# this means that each time a sample is called, it is decoded (which may take a while if done for the complete dataset)
-# in BirdSet, this is all done on-the-fly during training and testing (since the dataset size would be too big if mapping and saving it only once)
-dataset = dataset.cast_column("audio", Audio(sampling_rate=32_000))
-
-# extract the first five seconds of each sample in training (not utilizing event detection)
-# this is not very efficient since each complete audio file must be decoded this way.
-# a custom decoding with soundfile, stating start and end would be more efficient (see BirdSet Code)
-def map_first_five(sample):
-    max_length = 160_000 # 32_000hz*5sec
-    sample["audio"]["array"] =  sample["audio"]["array"][:max_length]
-    return example
-
-# train is now available as an array that can be transformed into a spectrogram for example 
-train = train.map(map_first_five, batch_size=1000, num_proc=2)
-```
 ### Datasets
 We present the BirdSet benchmark that covers a comprehensive range of (multi-label and multi-class) classification datasets in avian bioacoustics. 
 We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies.
@@ -103,6 +72,38 @@ and a complementary paper (work in progress): https://arxiv.org/abs/2403.10380
 - We provide the full recording with the complete label set and specified bounding boxes.
 - This dataset excludes recordings that do not contain bird calls ("no_call").
 
+### How to
+- We recommend to use our [intro notebook](https://github.com/DBD-research-group/BirdSet/blob/main/notebooks/tutorials/birdset-pipeline_tutorial.ipynb) in our code repository
+- The BirdSet Code package simplfies the data processing steps 
+- For multi-label evaluation with a segment-based evaluation use the test_5s column for testing.
+
+We provide a very short example where no additional code is required. We load the first 5 seconds to quickly create an examplary training dataset.
+We recommend to start with HSN. It is a medium size dataset with a low number of overlaps within a segment.
+
+```python
+from datasets import Audio
+
+dataset = load_dataset("DBD-research-group/BirdSet", "HSN")
+
+# slice example
+dataset["train"] = dataset["train"].select(range(500))
+
+# the dataset comes without an automatic Audio casting, this has to be enabled via huggingface
+# this means that each time a sample is called, it is decoded (which may take a while if done for the complete dataset)
+# in BirdSet, this is all done on-the-fly during training and testing (since the dataset size would be too big if mapping and saving it only once)
+dataset = dataset.cast_column("audio", Audio(sampling_rate=32_000))
+
+# extract the first five seconds of each sample in training (not utilizing event detection)
+# this is not very efficient since each complete audio file must be decoded this way.
+# a custom decoding with soundfile, stating start and end would be more efficient (see BirdSet Code)
+def map_first_five(sample):
+    max_length = 160_000 # 32_000hz*5sec
+    sample["audio"]["array"] =  sample["audio"]["array"][:max_length]
+    return example
+
+# train is now available as an array that can be transformed into a spectrogram for example 
+train = train.map(map_first_five, batch_size=1000, num_proc=2)
+```
 
 ### Metadata