Note: Formerly known as
amt-augpy. Starting with v1.0.9, the package isamt-augmentor.
A Python toolkit for augmenting Automatic Music Transcription (AMT) datasets through various audio transformations while maintaining synchronization between audio and MIDI files. The dataset follows the same format as MAESTRO v3.0.0, which is commonly used for Automatic Music Transcription (AMT) tasks.
The toolkit expects a folder containing paired audio and MIDI files with matching names. The audio file and MIDI file must be ground truth data, as this toolkit is only for augmenting existing datasets - a common technique in Machine Learning.
dataset/
├── song1.wav # Audio file
├── song1.mid # Ground truth annotated midi file
- Time Stretching: Tempo modification while maintaining pitch
- Pitch Shifting: Transposition while preserving timing
- Reverb & Filtering: Room acoustics and frequency filtering effects
- Gain & Chorus: Depth and richness enhancement
- Noise Augmentation: Controlled noise addition for robustness training
- Pause Manipulation: Detection and modification of musical pauses
- Audio Merging: Combine multiple audio files into one for complex training scenarios
- Audio Standardization: Conversion to 44.1kHz WAV format
- Parallel Processing: Multi-core processing for faster augmentation
- Configuration System: YAML-based parameter customization
- Dataset Validation: Automatic validation of train/test/validation splits
- Dataset Modification: Built-in tools to modify existing dataset splits
- MAESTRO Compatibility: Dataset format compatible with MAESTRO v3.0.0
Built for AMT, not just audio. Unlike general audio augmenters, AMT-Augmentor keeps paired audio+MIDI aligned by applying transform-consistent updates to MIDI (transpose for pitch shift, time-scale for stretch) and ships MAESTRO-style dataset tools (CSV builder + split validation) to avoid leakage. It also supports semitone/time-aware transforms and reproducible runs via --seed.
- Python 3.9, 3.10, 3.11, 3.12, or 3.13
- System dependencies:
libsndfileandffmpeg(for audio processing)
You can install AMT-Augmentor either via pip or by cloning the repository:
pip install amt-augmentorgit clone https://github.com/LarsMonstad/amt-augmentor.git
cd amt-augmentor
pip install -e .amt-augmentor /path/to/dataset/directory
# Or running directly
python -m amt_augmentor.main /path/to/dataset/directoryThis will process all compatible audio files in the directory and their corresponding MIDI files. The script automatically selects random parameters within predefined ranges for each augmentation type.
# Use a custom configuration file
amt-augmentor /path/to/dataset/directory --config my_config.yaml
# Set random seed for reproducible augmentation
amt-augmentor /path/to/dataset/directory --seed 42
# Specify an output directory
amt-augmentor /path/to/dataset/directory --output-directory /path/to/output
# Generate a default configuration file
amt-augmentor --generate-config my_config.yaml
# Disable specific effects
amt-augmentor /path/to/dataset/directory --disable-effect timestretch --disable-effect chorus
# Control merge behavior (default merges 1 random file with each file)
amt-augmentor /path/to/dataset/directory --merge-num 2 # Merge 2 files with each file
# Modify existing dataset CSV files
amt-augmentor --modify-csv dataset.csv --list-split all # List all songs
amt-augmentor --modify-csv dataset.csv --move-to-split test --song-patterns "Mozart" # Move songs
amt-augmentor --modify-csv dataset.csv --remove-songs --song-patterns "BadRecording" # Remove songs
# Parallel processing with 8 workers
amt-augmentor /path/to/dataset/directory --num-workers 8
# Custom train/test/validation split
amt-augmentor /path/to/dataset/directory --train-ratio 0.8 --test-ratio 0.1 --validation-ratio 0.1
# Force specific songs to test set (prevents augmentation)
amt-augmentor /path/to/dataset/directory --custom-test-songs "song1,song3,song5"
# Dry run to preview what will be processed
amt-augmentor /path/to/dataset/directory --dry-run
# Verbose output for debugging
amt-augmentor /path/to/dataset/directory --verbose
# Check for valid MIDI-WAV pairs before processing
amt-augmentor /path/to/dataset/directory --check-pairs
# List available effects
amt-augmentor --list-effects
# Check version
amt-augmentor --versionamt-augmentor --helpAll augmentation parameters can be customized using a YAML configuration file. See config.sample.yaml for a complete example with documentation.
- Input: WAV, FLAC, MP3, M4A, AIFF
- Output: WAV (44.1kHz)
- MIDI (.mid)
For each input file pair (audio + MIDI), the toolkit generates multiple augmented versions with the following naming convention:
original_name_augmented_effect_parameter_randomsuffix.extension
The _augmented_ identifier ensures all augmented files are properly recognized and handled during dataset creation.
Example:
piano_augmented_timestretch_1.2_abc123.wav
piano_augmented_timestretch_1.2_abc123.mid
piano_augmented_noise_1.5_def456.wav
piano_augmented_noise_1.5_def456.mid
piano_augmented_merge_piano2_ghi789.wav
piano_augmented_merge_piano2_ghi789.mid
The dataset follows the same format as MAESTRO v3.0.0. Songs assigned to test or validation splits will have their augmented versions excluded to prevent data leakage.
# Create dataset with default split ratios (70% train, 15% test, 15% validation)
amt-augmentor /path/to/directory
# Create dataset with custom split ratios
amt-augmentor /path/to/directory --train-ratio 0.8 --test-ratio 0.1 --validation-ratio 0.1
# Force specific songs to test set (they won't be augmented)
amt-augmentor /path/to/directory --custom-test-songs "song1,song3,song5"Dataset split validation is automatically performed after CSV creation to ensure:
- Augmented songs are not included in test/validation splits
- No cross-split contamination occurs
- Original and augmented songs are properly distributed
The generated CSV follows the MAESTRO format with the following columns:
- canonical_composer
- canonical_title
- split
- year
- midi_filename
- audio_filename
- duration
After creating a dataset CSV, you can easily modify it to adjust train/test/validation splits:
# List all songs and their distribution
amt-augmentor --modify-csv dataset.csv --list-split all
# List only test songs
amt-augmentor --modify-csv dataset.csv --list-split test
# List all songs with detailed view
amt-augmentor --modify-csv dataset.csv --list-split all --verbose
# Move songs to a different split (substring matching)
amt-augmentor --modify-csv dataset.csv --move-to-split test --song-patterns "Mozart,Chopin"
# Remove songs from dataset
amt-augmentor --modify-csv dataset.csv --remove-songs --song-patterns "BadRecording1,BadRecording2"
# Create backup before modifications (off by default)
amt-augmentor --modify-csv dataset.csv --move-to-split validation --song-patterns "Bach" --backupFeatures:
- Substring matching: Patterns like "Mozart" match any song containing that substring
- Smart augmented handling: Augmented versions automatically stay in train split only
- Backup option: Use
--backupto create a backup before modifications
Contributions are welcome! Please feel free to submit a Pull Request.
For development:
- Install development dependencies:
pip install -e ".[dev]" - Run tests:
pytest tests/ - Check typing:
mypy amt_augmentor - Format code:
black amt_augmentor
- Lars Monstad (@LarsMonstad) – Original author and maintainer
- @monoamine11231 – Noise augmentation, custom test songs feature, and various improvements
For questions or collaboration:
- Email: lars@botsformusic.com
- Organization: https://botsformusic.com
- GitHub: https://github.com/LarsMonstad/amt-augmentor
MIT License - see LICENSE file for details.
If you use this toolkit in your research, please cite:
@software{amt_augmentor,
author = {Lars Monstad and contributors},
title = {AMT-Augmentor: Audio + MIDI augmentation toolkit for AMT datasets},
version = {1.1.2},
year = {2025},
publisher = {Bots for Music},
url = {https://github.com/LarsMonstad/amt-augmentor}
}