Update README.md

README.md

@@ -3,6 +3,18 @@ license: cc-by-4.0
 size_categories:
 - 100K<n<1M
 pretty_name: VocSim
+tags:
+- audio
+- audio-similarity
+- zero-shot-learning
+- representation-learning
+- embedding-evaluation
+- unsupervised-learning
+- speech
+- environmental-sounds
+- animal-vocalizations
+- benchmark
+paperswithcode_id: audiosim
 dataset_info:
   features:
   - name: audio
@@ -17,88 +29,71 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 5452179735.8840885
+    num_bytes: 5452179735 # ~5.45 GB
     num_examples: 114641
-  download_size: 5500616162
-  dataset_size: 5452179735.8840885
+  download_size: 5500616162 # ~5.5 GB
+  dataset_size: 5452179735 # ~5.45 GB
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
-tags:
-- audio-retrieval
-- zero-shot
-- unsupervised-clustering-generalization
 ---
-# VocSim: A Benchmark for Zero-Shot Audio Similarity
 
+# VocSim: Zero-Shot Audio Similarity Benchmark
 [![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
-[![Dataset Size](https://img.shields.io/badge/Dataset%20Size-5.45%20GB-orange)](https://huggingface.co/datasets/anonymous-submission000/vocsim)
-[![Number of Examples](https://img.shields.io/badge/Examples-114k-brightgreen)](https://huggingface.co/datasets/anonymous-submission000/vocsim)
-
-**VocSim** is a large-scale benchmark dataset meticulously crafted to evaluate the generalization capabilities of neural audio embeddings for **zero-shot audio similarity tasks**. It challenges models to recognize fine-grained acoustic similarity between sounds they haven't been explicitly trained to classify together.
+[![Size](https://img.shields.io/badge/Size-5.45%20GB-orange)](https://huggingface.co/datasets/anonymous-submission000/vocsim) 
+[![Examples](https://img.shields.io/badge/Examples-114k-brightgreen)](https://huggingface.co/datasets/anonymous-submission000/vocsim) 
 
-**[VocSim Leaderboard 🏆](https://paperswithcode.com/dataset/audiosim)**
+**VocSim** evaluates how well neural audio embeddings generalize for **zero-shot audio similarity**. It tests recognizing fine-grained acoustic similarity without specific similarity training.
 
-**Repository:** [GitHub Link - Add Upon DOI]
+**[Leaderboard](https://paperswithcode.com/dataset/audiosim)**
 
-**Paper:** [VocSim: Zero-Shot Audio Similarity Benchmark for Neural Embeddings - Link Upon DOI]()
+**Paper:** [Link Upon DOI]()
 
-**Point of Contact:** Anonymous Authors (initially)
+**Repository:** [Link Upon DOI]()
 
 ---
 
-## 🎯 Dataset Goal: Evaluating Generalization
-
-The core purpose of VocSim is to provide a standardized testbed for assessing how well learned audio representations can capture the notion of "sameness" between sounds based purely on their acoustic properties, without relying on supervised training signals for the specific similarity task being evaluated.
-
-## 🧩 Dataset Structure: Diverse Acoustic Challenges
-
-VocSim achieves its goal by aggregating **19 distinct subsets** (15 publicly available, 4 reserved as blind test sets) that introduce significant variability across key acoustic and structural dimensions:
-
-*   🎤 **Sound Source:** Spans human speech (phonemes, words, utterances), intricate birdsong (calls, syllables from multiple species), otter calls, and common environmental events.
-*   ⏱️ **Duration:** Ranges from fleeting sub-100ms syllables to multi-second recordings.
-*   📊 **Class Structure:** Includes subsets with a few well-populated classes (e.g., environmental sounds) alongside subsets with thousands of classes having only a handful of examples each (e.g., rare words or syllables).
-*   🔊 **Acoustic Conditions:** Features both clean, studio-quality recordings (like TIMIT portions) and challenging, noisy, naturalistic recordings (e.g., AMI meetings, field recordings of animals).
-*   🧬 **Intra-class Variation:** Naturally incorporates real-world differences in loudness, speaking/singing rate, speaker identities, animal individuals, and recording environments within the same semantic `label`.
-
-This diversity forces embedding models to learn robust representations that are invariant to nuisance factors while remaining sensitive to the core acoustic characteristics defining a sound class.
-
-### Key Metrics
-
-Performance is measured using metrics sensitive to retrieval quality and cluster coherence:
+## Key Features
 
-*   **RA@k (Retrieval Accuracy @k):** The fraction of the *k* retrieved nearest neighbors that share the same `label` as the query sample. *Higher is better.* (e.g., RA@1 checks if the single closest neighbor is correct).
-*   **CSCF (Cluster Separation Confusion Fraction):** Estimates the proportion of samples that are, on average, closer to embeddings from *other* classes than to embeddings from their *own* class. Reflects how well-separated the class clusters are in the embedding space. *Lower is better.*
+*   **Diverse Sources:** Human speech (phones, words, utterances), birdsong, otter calls, environmental sounds.
+*   **Varied Conditions:** Spans clean to noisy recordings, short (<100ms) to long durations, few to many classes per subset.
+*   **Standardized:** All audio is 16kHz mono.
 
-(See Section 5 of the paper for detailed definitions and analysis.)
-
-### Other Potential Uses
-
-Beyond zero-shot similarity, VocSim embeddings can be evaluated on:
-
-*   Unsupervised Clustering (e.g., using metrics like NMI or ARI).
-*   As input features for downstream supervised classification tasks.
+## Task & Evaluation
 
+*   **Primary Task:** Zero-Shot Audio Similarity Retrieval.
+*   **Metrics:**
+    *   **RA@k:** Retrieval Accuracy @k (*Higher is better*).
+    *   **CSCF:** Cluster Separation Confusion Fraction (*Lower is better*).
 
 ---
 
-## 💾 Data Format
-
-### Data Instances
-
-Each data point in the dataset includes the audio waveform and associated metadata. All audio is standardized to **16kHz mono**.
+## Data Format
 
 ```python
 {
-  'audio': {
-      'path': None, # Path may be None if loaded directly
-      'array': array([-0.00024, -0.00048, ..., 0.00012], dtype=float32), # Waveform
-      'sampling_rate': 16000 # Always 16kHz
-    },
-  'subset': 'HW1',             # Origin subset ID (e.g., Human Words 1)
-  'speaker': 'speaker_id_abc', # Identifier for speaker, animal, or session. 'N/A' if not applicable.
-  'label': 'hello'              # Ground truth class label (word, phone, syllable type, etc.)
+  'audio': {'array': array([...], dtype=float32), 'sampling_rate': 16000},
+  'subset': 'HW1',      # Origin identifier
+  'speaker': 'spk_id',  # Speaker/Animal/Source ID or 'N/A'
+  'label': 'hello'      # Ground truth class for similarity
 }
-```
+
+Train split: 114,641 public examples from 15 subsets for evaluation.
+
+Blind Test Sets: 4 additional subsets held out privately.
+
+## Citation
+```bib
+@inproceedings{vocsim_authors_2024,
+  title={VocSim: Zero-Shot Audio Similarity Benchmark for Neural Embeddings},
+  author={Anonymous Authors},
+  booktitle={Conference/Journal},
+  year={2025},
+  url={[Link to paper upon DOI]}
+}
+```
+## License
+
+CC BY 4.0 - Creative Commons Attribution 4.0 International.