Update README.md

Initial README.md

README.md

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----
-license: mit
-dataset_info:
-  features:
-  - name: track_name
-    dtype: string
-  - name: start_time
-    dtype: int64
-  - name: embedding
-    dtype:
-      array2_d:
-        shape:
-        - 240
-        - 4800
-        dtype: float32
-  splits:
-  - name: train
-    num_bytes: 4166537771
-    num_examples: 904
-  download_size: 4171864391
-  dataset_size: 4166537771
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: mit
+dataset_info:
+  features:
+  - name: track_name
+    dtype: string
+  - name: start_time
+    dtype: int64
+  - name: embedding
+    dtype:
+      array2_d:
+        shape:
+        - 240
+        - 4800
+        dtype: float32
+  splits:
+  - name: train
+    num_bytes: 4166537771
+    num_examples: 904
+  download_size: 4171864391
+  dataset_size: 4166537771
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+
+# Jukebox Embeddings for the URMP Dataset
+
+## Overview
+
+This dataset extends the University of Rochester Multi-Modal Music Performance (URMP) Dataset by providing embeddings for each audio file.
+
+## Original URMP Dataset
+
+[Link to official site](https://labsites.rochester.edu/air/projects/URMP.html)
+
+The URMP dataset was created to facilitate audio-visual analysis of musical performances. It comprises multiple simple multi-instrument musical pieces assembled from coordinated but separately recorded performances of individual tracks.
+
+## Jukebox Embeddings
+
+Embeddings are derived from [OpenAI's Jukebox model](https://openai.com/index/jukebox/), following the approach described in [Castellon et al. (2021)](https://arxiv.org/abs/2107.05677) with some modifications followed in [Spotify's Llark paper](https://arxiv.org/pdf/2310.07160):
+
+- Source: Output of the 36th layer of the Jukebox encoder
+- Original Jukebox encoding: 4800-dimensional vectors at 345Hz
+- Audio/embeddings are chunked into 25 seconds clips as that is the max Jukebox can take in as input, any clips shorter than 25 seconds are padded before passed through Jukebox
+- Approach: Mean-pooling within 100ms frames, resulting in:
+    - Downsampled frequency: 10Hz
+    - Embedding size: 1.2 × 10^6 for a 25s audio clip
+    - Embedding shape: [240, 4800]
+- This method retains temporal information while reducing the embedding size
+
+### Why Jukebox? Are these embeddings state-of-the-art as of September 2024?
+
+Determining the optimal location to extract embeddings from large models typically requires extensive probing. This involves testing various activations or extracted layers of the model on different classification tasks through a process of trial and error. Additional fine-tuning is often done to optimise embeddings across these tasks.
+
+The two largest publicly available music generation and music continuation (i.e.: able to take in audio as input) models are Jukebox and MusicGen. According to [this paper on probing MusicGen](https://www.merl.com/publications/docs/TR2024-032.pdf), embeddings extracted from Jukebox appears to outperform MusicGen on average in their classification tasks.
+
+## Dataset Features
+
+This extension to the URMP dataset includes:
+
+1. File name of each WAV file in the URMP dataset
+2. Start time of the audio
+3. Jukebox embedding for each audio file
+
+## Applications
+
+This extended dataset can be used for various tasks, including but not limited to:
+
+- Music source separation
+- Transcription
+- Performance analysis
+- Multi-modal information retrieval
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("jonflynn/urmp_jukebox_embeddings")
+
+# There's only one split, that is train
+train_dataset = dataset['train']
+```
+
+## Citation
+
+If you use this dataset in your research, please cite the original URMP paper and this extension:
+
+```bibtex
+@article{li2018creating,
+  title={Creating a multi-track classical music performance dataset for multi-modal music analysis: Challenges, insights, and applications},
+  author={Li, Bochen and Liu, Xinzhao and Dinesh, Karthik and Duan, Zhiyao and Sharma, Gaurav},
+  journal={IEEE Transactions on Multimedia},
+  year={2018},
+  publisher={IEEE}
+}
+
+@dataset{flynn2024urmpjukebox,
+  author       = {Jon Flynn},
+  title        = {Jukebox Embeddings for the URMP Dataset},
+  year         = {2024},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/jonflynn/urmp_jukebox_embeddings}},
+}
+```