Usage

Supported Soundfonts

000_Florestan_Piano_sf2
Arachno_SoundFont_-_Version_1.0_sf2
GeneralUser_GS_MuseScore_v1.442_sf2
GeneralUser_GS_v1.471_sf2
grand_piano_sf2
J800_Piano_sf2
JV1080_Nice_Piano_m_sf2
Riky-Kraunkofer_Soundfont_sf2
Roland_XP-50_sf2
Roland_XP-80_sf2
SF2.Piano.DGX.Chung.Song.130.564KB_sf2
Splendid.136_sf2
Velocity_Grand_Piano_sf2
Yamaha-C5-Salamander-JNv5.1_sf2
YDP-GrandPiano-20160804_sf2
MS_Basic_sf3

Example

import torch
from torchvision.transforms import *
from torch.utils.data import DataLoader
from datasets import load_dataset, concatenate_datasets


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

compose = Compose([
    Resize(300),
    CenterCrop(300),
    RandomAffine(5),
    ToTensor(),
    Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])


def transform(example_batch):
    inputs = [compose(x.convert("RGB")) for x in example_batch["image"]]
    example_batch["image"] = inputs
    return example_batch


cols = ["image", "label"]
ds_1 = load_dataset("george-chou/emopia_mel",
                    "000_Florestan_Piano").with_transform(transform, columns=cols)
ds_2 = load_dataset("george-chou/emopia_mel",
                    "J800_Piano").with_transform(transform, columns=cols)

trainset = concatenate_datasets([ds_1["train"], ds_2["train"]])
validset = concatenate_datasets([ds_1["validation"], ds_2["validation"]])
testset = concatenate_datasets([ds_1["test"], ds_2["test"]])

traLoader = DataLoader(trainset, batch_size=4, shuffle=True)
valLoader = DataLoader(validset, batch_size=4, shuffle=True)
tesLoader = DataLoader(testset, batch_size=4, shuffle=True)

for i, data in enumerate(traLoader, 0):
    inputs, labels = data["image"].to(device), data["label"].to(device)
    print("inputs: ", inputs)
    print("labels: ", labels)

for i, data in enumerate(valLoader, 0):
    inputs, labels = data["image"].to(device), data["label"].to(device)
    print("inputs: ", inputs)
    print("labels: ", labels)

for i, data in enumerate(tesLoader, 0):
    inputs, labels = data["image"].to(device), data["label"].to(device)
    print("inputs: ", inputs)
    print("labels: ", labels)

Skip Large Files

Open Git bash here and input following command: ''' GIT_LFS_SKIP_SMUDGE=1 '''

Maintenance

git clone git@hf.co:datasets/george-chou/emopia_mel

Cite

@article{Hung2021EMOPIAAM,
  title={EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation},
  author={Hsiao-Tzu Hung and Joann Ching and Seungheon Doh and Nabin Kim and Juhan Nam and Yi-Hsuan Yang},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.01374}
}