Update README.md

README.md

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----
-license: cc-by-4.0
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: start_time
-    dtype: int32
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 10915894
-    num_examples: 5873
-  download_size: 3025628
-  dataset_size: 10915894
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: cc-by-4.0
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: start_time
+    dtype: int32
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 10915894
+    num_examples: 5873
+  download_size: 3025628
+  dataset_size: 10915894
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+
+# Maestro ABC Notation 25s Dataset
+
+## Dataset Summary
+
+This is based on V3.0.0 of the Maestro dataset.
+
+The **Maestro ABC Notation 25s Dataset** is a curated collection of question-and-answer pairs derived from short audio clips within the [MAESTRO dataset](https://magenta.tensorflow.org/datasets/maestro). Each entry in the dataset includes:
+
+- An `id` corresponding to the original audio file.
+- A `start_time` marking where the 25-second audio clip begins within the full track.
+- A `question` designed to prompt music transcription in ABC notation.
+- An `answer` that provides the transcription in ABC notation format.
+
+This dataset is crafted for training multi-modal audio-language models (such as [Spotify Llark](https://research.atspotify.com/2023/10/llark-a-multimodal-foundation-model-for-music/) and [Qwen2-Audio](https://github.com/QwenLM/Qwen2-Audio)) with a focus on music transcription tasks. The MIDI-to-ABC conversion is achieved with a modified script based on [this code](https://github.com/jwdj/EasyABC/blob/master/midi2abc.py).
+
+### Why ABC Notation?
+
+ABC notation was chosen due to several advantages:
+
+- **Simplicity**: ABC is a concise, text-based music notation, which makes it easier for text-based models to parse.
+- **Compatibility**: It’s a widely recognized format that many language models have already been trained on.
+- **Flexibility**: ABC notation can be extended to include additional musical information, such as tempo changes, time signature adjustments, and specific playing styles.
+
+### Dataset Modifications for ABC Notation
+
+- Assigned default octave ranges based on the common play ranges of piano keys to streamline the notation and reduce redundancy.
+- Excluded pieces with significant time signature changes or tempo fluctuations (over 10 BPM) for consistency.
+- Removed sections with silence to ensure active musical content in each sample.
+
+## Licensing Information
+
+- **MAESTRO Dataset**: The audio files are sourced from the MAESTRO dataset, licensed under the Creative Commons Attribution Non-Commercial Share-Alike 4.0 license. Please refer to the [MAESTRO dataset page](https://magenta.tensorflow.org/datasets/maestro) for full licensing details.
+
+## Citation Information
+
+If you utilize this dataset, please cite it as follows:
+
+```bibtex
+@dataset{maestro_abc_notation_25s_2024,
+  title={MAESTRO ABC Notation Dataset},
+  author={Jon Flynn},
+  year={2024},
+  howpublished={\url{https://huggingface.co/datasets/jonflynn/maestro_abc_notation_25s}},
+  note={ABC notation for the MAESTRO dataset split into 25-second segments},
+}
+```
+
+For the original MAESTRO dataset, please cite the following:
+
+```bibtex
+@inproceedings{hawthorne2018enabling,
+  title={Enabling Factorized Piano Music Modeling and Generation with the {MAESTRO} Dataset},
+  author={Curtis Hawthorne and Andriy Stasyuk and Adam Roberts and Ian Simon and Cheng-Zhi Anna Huang and Sander Dieleman and Erich Elsen and Jesse Engel and Douglas Eck},
+  booktitle={International Conference on Learning Representations},
+  year={2019},
+  url={https://openreview.net/forum?id=r1lYRjC9F7},
+}
+```