Metadata-Version: 2.1
Name: amt-augmentor
Version: 1.1.2
Summary: Audio + MIDI paired augmentation toolkit for Automatic Music Transcription (AMT)
Author-email: Lars Monstad <lars.monstad@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Lars Løberg Monstad
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/LarsMonstad/amt-augmentor
Project-URL: Issues, https://github.com/LarsMonstad/amt-augmentor/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: librosa<0.12.0,>=0.9.1
Requires-Dist: numpy<2.0.0,>=1.23.5
Requires-Dist: soundfile<0.14.0,>=0.12.1
Requires-Dist: pedalboard<1.0.0,>=0.7.3
Requires-Dist: pretty_midi<0.3.0,>=0.2.10
Requires-Dist: tqdm<5.0.0,>=4.64.1
Requires-Dist: numba<0.63.0,>=0.56.4
Requires-Dist: llvmlite<0.46.0,>=0.39.1
Requires-Dist: pyyaml<7.0.0,>=5.1.0
Requires-Dist: tabulate<1.0.0,>=0.9.0
Requires-Dist: setuptools>=42; python_version >= "3.12"
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: mypy>=1.0.0; extra == "dev"
Requires-Dist: black>=23.0.0; extra == "dev"
Requires-Dist: pylint>=2.15.0; extra == "dev"
Requires-Dist: sphinx>=6.0.0; extra == "dev"
Requires-Dist: types-PyYAML>=5.4.0; extra == "dev"
Requires-Dist: types-tqdm>=4.64.0; extra == "dev"

<div align="center">
  <img src="https://raw.githubusercontent.com/LarsMonstad/amt-augmentor/refs/heads/main/images/BotsForMusic_Logo_Black_2.png" alt="Bots for Music Logo" width="300">

  # AMT-Augmentor

  ## Python Data Augmentation Toolkit for Automatic Music Transcription (AMT)

  **Developed by [Bots for Music](https://botsformusic.com), maintained by Lars Monstad**

  [![PyPI version](https://badge.fury.io/py/amt-augmentor.svg)](https://badge.fury.io/py/amt-augmentor)
  [![Python](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://www.python.org)
  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
  [![CI](https://github.com/LarsMonstad/amt-augmentor/actions/workflows/ci.yml/badge.svg)](https://github.com/LarsMonstad/amt-augmentor/actions/workflows/ci.yml)
  [![Downloads](https://pepy.tech/badge/amt-augmentor)](https://pepy.tech/project/amt-augmentor)
</div>

> **📦 [View on PyPI](https://pypi.org/project/amt-augmentor/)** 
>
> **Note:** Formerly known as `amt-augpy`. Starting with v1.0.9, the package is **`amt-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](https://magenta.tensorflow.org/datasets/maestro), 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
```

## Features

### Audio Transformations
- **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

### Processing & Dataset Handling
- **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

## Why AMT-Augmentor?

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.

## Installation

You can install AMT-Augmentor either via pip or by cloning the repository:

### Using pip

```bash
pip install amt-augmentor
```

### From source

```bash
git clone https://github.com/LarsMonstad/amt-augmentor.git
cd amt-augmentor
pip install -e .
```


## Usage

### Basic Usage

```bash
amt-augmentor /path/to/dataset/directory
# Or running directly
python -m amt_augmentor.main /path/to/dataset/directory
```



This 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.

### Advanced Usage

```bash
# 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 --version
```

### Help and options

```bash
amt-augmentor --help
```

## Configuration

All augmentation parameters can be customized using a YAML configuration file. See `config.sample.yaml` for a complete example with documentation.


## File Format Support

### Audio
- Input: WAV, FLAC, MP3, M4A, AIFF 
- Output: WAV (44.1kHz)

### Annotations
- MIDI (.mid)

## Output Structure

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

## Dataset Creation & Validation

The dataset follows the same format as [MAESTRO v3.0.0](https://magenta.tensorflow.org/datasets/maestro). Songs assigned to test or validation splits will have their augmented versions excluded to prevent data leakage.

### Creating the Dataset CSV

```bash
# 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"
```

### Validating the Dataset Split

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

### CSV Format

The generated CSV follows the MAESTRO format with the following columns:
- canonical_composer
- canonical_title
- split
- year
- midi_filename
- audio_filename
- duration

### Modifying Existing Datasets

After creating a dataset CSV, you can easily modify it to adjust train/test/validation splits:

```bash
# 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" --backup
```

**Features:**
- **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 `--backup` to create a backup before modifications

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

For development:
1. Install development dependencies: `pip install -e ".[dev]"`
2. Run tests: `pytest tests/`
3. Check typing: `mypy amt_augmentor`
4. Format code: `black amt_augmentor`

## Contributors

- **Lars Monstad (@LarsMonstad)** – Original author and maintainer
- **@monoamine11231** – Noise augmentation, custom test songs feature, and various improvements

## Contact

For questions or collaboration:
- Email: lars@botsformusic.com
- Organization: https://botsformusic.com
- GitHub: https://github.com/LarsMonstad/amt-augmentor

## License

MIT License - see LICENSE file for details.

## Citation

If you use this toolkit in your research, please cite:

```bibtex
@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}
}
```
