Update README.md

README.md

@@ -34,11 +34,28 @@ Along with the full dataset, we provide several subsets which may be appropriate
 
 ## Dataset
 
-As detailed in our [paper](https://openreview.net/pdf?id=X5hrhgndxW), Aria-MIDI was created by transcribing publicly available solo-piano audio recordings into MIDI files. To accomplish this, we developed various tools which we integrated into our data pipeline, which we also release under the (Apache-2.0)[https://www.apache.org/licenses/LICENSE-2.0] license. In particular: 
+As detailed in our [paper](https://openreview.net/pdf?id=X5hrhgndxW), Aria-MIDI was created by transcribing publicly available solo-piano audio recordings into MIDI files. To accomplish this, we developed various tools which we integrated into our data pipeline, which we also release under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) license. In particular: 
 
 [Aria-AMT](https://github.com/EleutherAI/aria-amt). A seq-to-seq piano transcription model which accurately transcribes notes from solo-piano recordings to MIDI. We designed this model to be robust, aiming to maintain note-identification accuracy when transcribing realistic audio from a wide range of recording environments. We also provide an inference engine for Linux/CUDA, supporting batched processing as well as multi-gpu setups.
 
-[Aria-CL](https://github.com/loubbrad/aria-cl). A solo-piano audio-classification model capable of detecting and isolating (i.e., segmenting) solo-piano content in arbitrary audio files. Our approach to classification was designed to identify the audio-quality of the underlying piano recording, specifically targeting audio artifacts that may cause problems for transcription models, providing flexibility during inference.
+[Aria-CL](https://github.com/loubbrad/aria-cl). A solo-piano audio-classification model capable of detecting and isolating (i.e., segmenting) solo-piano content from arbitrary audio files. We designed our approach to additionally exclude solo-piano audio which may cause problems for transcription models, e.g., recordings with significant audio artifacts.
+
+We provide the dataset as a tarball. Filenames follow the format `<file_id>_<segment_number>.mid`, where each segment number corresponds to a distinct (i.e., non-overlapping), contiguous portion of solo-piano content from each audio file, identified and transcribed independently. Metadata is provided in the file metadata.json, structured as: 
+
+```
+<file_id>: {
+  "metadata": {
+    <metadata_category>: <metadata>,
+    ...,
+  },
+  "audio_scores": {
+    <segment_number>: <score>,
+    ...,
+  }
+}
+```
+
+Metadata was extracted from textual information associated with each original audio file and may vary in accuracy. Audio scores are calculated as the average score assigned by our classifier across segments, designed to correlate with the audio quality of the underlying solo-piano recording.
 
 ## Citation/License
 
@@ -53,3 +70,4 @@ Aria-MIDI is distributed with the [CC-BY-NC-SA 4.0](https://creativecommons.org/
   url={https://openreview.net/forum?id=X5hrhgndxW}, 
 }
 ```
+