Update README.md

README.md

@@ -22,7 +22,44 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- audio-classification
+pretty_name: VocalSimilarity
+size_categories:
+- 100K<n<1M
 ---
-# Dataset Card for "vocsim"
+### Dataset Description
+
+"Benchmarking embeddings for retrieval and discrimination of vocalizations in humans and songbirds". 
+This bechmarking aggregated dataset consists of a collection of vocalization samples from humans and songbirds.
+
+### Data Fields
+
+1. **Subset**: Specifies the subset/category of the dataset. It can indicate whether the sample is from humans or songbirds, and possibly more detailed categorization.
+
+2. **Audio**: Contains the audio sample.
+
+3. **Label**: Represents the label or class of the audio clip, indicating the type of vocalization or sound.
+
+4. **Speaker**: Identifies the speaker or source of the vocalization in the case of human datasets, or the individual bird in the case of songbird datasets.
+
+### Human Datasets
+
+1. **AMI**: The AMI Meeting Corpus comprises 100 hours of multi-modal meeting recordings, including audio data for utterances, words, and vocal sounds, alongside detailed speaker metadata.
+
+2. **TIMIT**: The TIMIT dataset contains manual phonetic transcriptions of utterances read by 630 English speakers with various dialects.
+
+3. **VocImSet**: The Vocal Imitation Set features recordings of 236 unique sound sources being imitated by 248 speakers.
+
+
+### Songbird Datasets
+
+1. **Tomka**: The Gold-Standard Zebrafinch dataset contains 48,059 vocalizations of 36 vocalization types from 4 zebra finches.
+
+2. **Nicholson**: The Bengalese finch song repository includes songs of four Bengalese finches recorded in the Sober lab at Emory University and manually clustered by two authors.
+
+3. **DAS**: The Deep Audio Segmenter Dataset features single male Bengalese finch songs, including 473 vocalizations of 6 vocalization types.
+
+4. **Elie**: Vocal repertoires from zebra finches, collected between 2011 and 2014 at the University of California Berkeley by Julie E Elie. This dataset contains 3,500 vocalizations from 50 individuals and 65 vocalization types.
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)