PianoVAM/PianoVAM_v1.0

PianoVAM: A Multimodal Piano Performance Dataset

Summary

This repository contains the PianoVAM (Video, Audio, Midi and Metadata) dataset, a multi-modal collection of piano performances designed for research in Music Information Retrieval (MIR).

The dataset features synchronized recordings of various piano pieces, providing rich data across several modalities. Our goal is to provide a comprehensive resource for developing and evaluating models that can understand the complex relationship between the visual, auditory, and symbolic aspects of music performance.

How to Cite

If you use the PianoVAM dataset in your research, please cite it as follows:

@inproceedings{kim2025pianovam,
  title={PianoVAM: A Multimodal Piano Performance Dataset},
  author={Kim, Yonghyun and Park, Junhyung and Bae, Joonhyung and Kim, Kirak and Kwon, Taegyun and Lerch, Alexander and Nam, Juhan},
  booktitle={Proceedings of the 26th International Society for Music Information Retrieval Conference (ISMIR)},
  year={2025}
}

Usage Guide

0. How to Download the Entire Dataset

To download the entire dataset, please follow the steps below.

1. Save the Script: Right-click on this Python script and select "Save Link As...". Save the file as download_pianovam.py in the folder where you want the dataset to be downloaded.

2. Run the Script: Open your terminal (Command Prompt or PowerShell on Windows, Terminal on macOS), navigate to the folder where you saved the script, and then run the following command:

   python download_pianovam.py
   

1. Load and Prepare the Dataset

This initial script loads the dataset, constructs the necessary file URLs, and prepares the audio for direct access.

from datasets import load_dataset, Audio
import requests
import os
import numpy as np
from scipy.io.wavfile import write

# Load dataset from the Hub
dataset = load_dataset("PianoVAM/PianoVAM_v1.0")

# Construct full URLs for all media files
def create_full_media_urls(example):
    base_url = "https://huggingface.co/datasets/PianoVAM/PianoVAM_v1.0/resolve/main/"
    example["audio_url"] = base_url + example["audio_path"]
    example["video_url"] = base_url + example["video_path"]
    example["midi_url"] = base_url + example["midi_path"]
    return example

dataset = dataset.map(create_full_media_urls)

# Cast the audio_url column for automatic audio decoding
dataset = dataset.cast_column("audio_url", Audio())

# Prepare a sample example from the training set
example = dataset["train"][0]

2. Access Decoded Data

After preparation, you can directly access metadata and the decoded audio array.

# Access metadata
print(f"Piece: {example['piece']} by {example['composer']}")
print(f"Performer: {example['P1_name']}")

# Access the decoded audio data object
audio_data = example["audio_url"]
print(f"Audio sampling rate: {audio_data['sampling_rate']}")
print(f"Audio array shape: {audio_data['array'].shape}")

Output:

Piece: Piano Concerto by E. Grieg
Performer: Yonghyun
Audio sampling rate: 44100
Audio array shape: (32876256,)

3. Download Source Files

Use the following methods to download the original source files to your local machine.

# --- Download Audio File (.wav) ---
audio_array = example["audio_url"]['array']
sampling_rate = example["audio_url"]['sampling_rate']
local_filename = os.path.basename(example['audio_path'])
write(local_filename, sampling_rate, audio_array.astype(np.float32))
print(f"Audio saved as '{local_filename}'")

# --- Download Video File (.mp4) ---
video_url = example['video_url']
local_filename = os.path.basename(video_url)
response = requests.get(video_url)
response.raise_for_status()
with open(local_filename, 'wb') as f:
    f.write(response.content)
print(f"Video saved as '{local_filename}'")

# --- Download MIDI File (.mid) ---
midi_url = example['midi_url']
local_filename = os.path.basename(midi_url)
response = requests.get(midi_url)
response.raise_for_status()
with open(local_filename, 'wb') as f:
    f.write(response.content)
print(f"MIDI saved as '{local_filename}'")

Dataset Description

The dataset consists of various piano pieces performed by multiple pianists. The data was captured simultaneously from a digital piano and high-resolution cameras to ensure precise synchronization between the audio, video, and MIDI streams. The collection is designed to cover a range of musical complexities and styles.

Note on Video Data

Please be aware that all video performances by the pianist named "jiwoo" have had a blur effect applied to the performer's upper body. This was done at the request of the performer to protect their privacy. The keyboard and hands remain fully visible and unaffected.

Directory Structure

The dataset repository is organized into the following directories:

PianoVAM_v1.0/
├── Audio/
├── Handskeleton/
├── MIDI/
├── TSV/
├── Video/
├── metadata.json
├── README.md
└── Fingering/     (Coming Soon)

Folder Contents

• Audio/: Contains the raw audio recordings of the piano performances.

  • Format: Uncompressed WAV (.wav).
  • Sample Rate: 44100 Hz.

• Video/: Contains the video recordings of the piano performances.

  • Format: MP4 (.mp4).
  • Resolution: 1920x1080 pixels.
  • Frame Rate: 60 fps.
  • Video Codec: H.264 (AVC).
  • Audio Codec: AAC.

• Handskeleton/: Contains the 3D hand landmark data for each performance.

  • Format: JSON (.json) files.
  • Details: Each file contains frame-by-frame coordinates for the 21 keypoints of both the left and right hands, as captured by MediaPipe Hands.

• MIDI/: Contains the ground truth performance data recorded directly from a digital piano.

  • Format: Standard MIDI Files (.mid).
  • Details: These files provide the precise timing (onset, offset), pitch, and velocity for every note played.

• metadata.json: A JSON file that maps each recording to its corresponding data split (train, valid, test, special) and provides other relevant information.

• TSV/: Contains pre-processed label data derived from the MIDI files for easier parsing. Each file is a tab-separated value file with 5 columns.

  • Format: TSV (.tsv).
  • Header: onset, key_offset, frame_offset, note, velocity
  • Column Descriptions:
    • onset: The start time of the note in seconds.
    • key_offset: The time when the finger is physically released from the key, in seconds. This is useful for video-based research such as fingering analysis.
    • frame_offset: The time when the sound completely ends, considering pedal usage. This is analogous to the 'offset' used in traditional audio-only transcription.
    • note: The MIDI note number (pitch).
    • velocity: The MIDI velocity (how hard the key was struck).

Coming Soon

• Fingering/: (Coming Soon) This directory will contain detailed frame-by-frame fingering annotations for each note played.

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). You are free to share and adapt the material for non-commercial purposes, provided you give appropriate credit and distribute your contributions under the same license.

Contact

For any questions about the dataset, please open an issue in the Community tab of this repository or contact [Yonghyun Kim/[email protected]].