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Datasets:
M64
/
sid-music

Tasks:
Text Generation
Modalities:
Text
Audio
Formats:
text
Languages:
English
Size:
10M - 100M
Tags:
music
audio
commodore-64
sid
chiptune
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metadata 
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - music
  - audio
  - commodore-64
  - sid
  - chiptune
size_categories:
  - 100M<n<1B

SID Music Dataset

Register dumps from 2,418 Commodore 64 SID files for training music generation models. 9000 frames for each file, corresponding to 3 minutes of the sid file.

Dataset Description

  • Source: HVSC (High Voltage SID Collection)
  • Size: 1GB of register dump sequences
  • Format: Hex-encoded SID register states at 50Hz
  • Songs: 2,410 files from 15 legendary composers

Composers Included

Composer Songs
DRAX (Thomas Mogensen) 1042
Laxity (Thomas E. Petersen) 274
Jeroen Tel 163
Thomas Detert 162
Reyn Ouwehand 124
David Whittaker 98
Ben Daglish 86
Johannes Bjerregaard 84
Rob Hubbard 78
Jonathan Dunn 67
Matt Gray 47
Charles Deenen 46
Chris Hülsbeck 42
Mark Cooksey 39
Martin Galway 34
Total 2,418

Data Format

Each frame is 25 SID registers encoded as 50 hex characters:

B0080005410A306011C0064108200016800D41082000B4031F
B0084005410A30601100074108200016C00D41082000B4031F
B0088005410A30601140074108200016000E41082000B4031F
...
<end>
  • 50 hex characters = 25 bytes (SID registers $D400-$D418)
  • <end> marks song boundaries
  • 50 frames = 1 second of audio

Register Layout 

Bytes 0-6:   Voice 1 (freq, pulse width, control, envelope)
Bytes 7-13:  Voice 2
Bytes 14-20: Voice 3
Bytes 21-24: Filter + Volume

Usage

Quick Start with SidGPT 

# Clone SidGPT
git clone https://github.com/M64GitHub/SidGPT
cd SidGPT
pip install torch numpy tqdm

# Download this dataset
wget https://huggingface.co/datasets/M64/sid-music/resolve/main/training.txt.gz
gunzip training.txt.gz
mv training.txt training/data/sid/input.txt

# Tokenize & Train
cd training/data/sid && python prepare.py && cd ../..
python train.py config/train_sid.py

Or Use Pre-trained Model

Skip training entirely:

Manual / Custom Training

If using your own training setup:

  1. Download: training.txt.gz (~100MB compressed, ~1GB uncompressed)
  2. Format: Character-level, 22-token vocabulary
  3. Tokenize: Map characters to indices:
   vocab = ['\n', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 
            'A', 'B', 'C', 'D', 'E', 'F', '<', '>', 'd', 'e', 'n']
   char_to_idx = {c: i for i, c in enumerate(vocab)}
  1. Train: Any GPT/transformer architecture works. Recommended:
    • Block size: 1020+ tokens (20+ frames context)
    • Character-level prediction (no BPE)

Pre-trained Model

Skip training and use the pre-trained model directly:

Statistics

  • Total characters: ~1,000,000,000
  • Vocabulary: 22 tokens (0-9, A-F, <, >, d, e, n, \n)
  • Average song length: 9000 frames (~ 3 minutes)

License

MIT License.

Original SID files from HVSC are © their respective composers. This dataset contains derived register dumps for research purposes.

Citation 

@misc{sidmusicdataset2026,
  author = {Mario Schallner},
  title = {SID Music Dataset: C64 Register Dumps for ML},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/M64/sid-music}
}

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