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Datasets:
EwanB
/
satb-choral-dataset

Tasks:
Audio Classification
Audio-to-Audio
Modalities:
Audio
Size:
1K<n<10K
Tags:
audio
music
choral
satb
source-separation
pytorch
License:
Dataset card Files
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  1. README.md +91 -0
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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - audio-classification
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+ - audio-to-audio
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+ tags:
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+ - audio
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+ - music
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+ - choral
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+ - satb
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+ - source-separation
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+ - pytorch
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+ pretty_name: SATB Choral Source Separation Dataset (Compressed)
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+ size_categories:
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+ - 1K<n<10K
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+ ---
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+
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+ # SATB Choral Source Separation Dataset (Compressed)
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+
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+ This dataset contains preprocessed 4-second audio chunks from the Choral Singing Dataset (CSD) formatted for SATB (Soprano, Alto, Tenor, Bass) voice source separation tasks.
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+
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+ **This is the compressed version with 8kHz sample rate and int8 precision for smaller file sizes.**
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+
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+ ## Dataset Description
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+
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+ - **Total chunks**: 26,883
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+ - **Dataset size**: ~4155.3 MB
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+ - **Audio format**: 8 kHz mono, 4-second chunks
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+ - **Precision**: int8 for memory efficiency
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+ - **Sources**: 4 individual voice parts (SATB)
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+ - **Mixed audio**: Sum of all 4 voices
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+
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+ ## Data Structure
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+
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+ ### Folder Structure
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+ ```
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+ ├── chunks/ # All individual chunk .pt files
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+ ├── quality_samples/ # Sample WAV files for quality checking
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+ ├── train_split.pt # List of training chunk filenames
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+ ├── val_split.pt # List of validation chunk filenames
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+ └── test_split.pt # List of test chunk filenames
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+ ```
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+
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+ ### Chunk Files
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+ Each `.pt` file in `chunks/` contains a dictionary with:
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+ - `mixed`: Mixed audio tensor (4 seconds, 32k samples at 8kHz) - int8
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+ - `sources`: Individual voice tensors [4, 32k] (soprano, alto, tenor, bass) - int8
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+ - `song`: Song identifier ("ER", "LI", or "ND")
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+ - `singer_combo`: List of singer IDs used [soprano_id, alto_id, tenor_id, bass_id]
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+ - `chunk_id`: Chunk number within the combination
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+
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+ # Load data splits
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+ train_files = torch.load('train_split.pt')
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+ val_files = torch.load('val_split.pt')
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+ test_files = torch.load('test_split.pt')
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+
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+ # Load a single chunk
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+ chunk = torch.load(f'chunks/{train_files[0]}')
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+ mixed_audio = chunk['mixed'].float() / 127.0 # Convert back to float
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+ separated_voices = chunk['sources'].float() / 127.0
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+
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+ # mixed_audio shape: [32000] (4 seconds at 8kHz)
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+ # separated_voices shape: [4, 32000] (SATB voices)
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+ ```
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+
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+ ## Compression Details
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+
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+ - **Sample rate**: Reduced from 16kHz to 8kHz (2x reduction)
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+ - **Precision**: int8 instead of float16 (2x reduction)
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+ - **Total compression**: ~4x smaller than original
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+ - **Quality samples**: Available in `quality_samples/` for evaluation
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+
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+ ## Original Dataset
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+
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+ Based on the Choral Singing Dataset (CSD):
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+ - **Songs**: "Erbarme dich" (ER), "Locus iste" (LI), "Nuper rosarum flores" (ND)
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+ - **Singers**: 4 different singers per voice part
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+ - **Combinations**: All possible singer combinations (4^4 = 256 per song)
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+
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+ ## Citation
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+
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+ If you use this dataset, please cite the original CSD paper and this preprocessing work.
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+
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+ ## License
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+
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+ CC-BY-4.0 - Same as the original Choral Singing Dataset
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