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# 🎵 Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music
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<p align="center">
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<a href="https://huggingface.co/longyu1315/Amadeus-S">
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<img src="https://img.shields.io/badge/🤗-Amadeus--S-yellow" alt="HuggingFace">
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</a>
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<a href="https://arxiv.org/abs/2508.20665">
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<img src="https://img.shields.io/badge/arXiv-2508.20665-blue" alt="arXiv">
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</a>
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</p>
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**Amadeus** is a novel **symbolic music (MIDI) generation framework**. We use **autoregressive modeling** for note sequences, **discrete diffusion models** for intra-note attributes, and **representation optimization** to enhance model performance. Compared to current mainstream autoregressive or hierarchical autoregressive models, Amadeus achieves significant improvements in **generation quality, speed, and controllability**. While significantly improving generation quality, we have achieved a speedup of at least **4x** compared to pure autoregressive models. We also support a training-free **fine-grained attribute control** mechanism, which endows Amadeus with maximum flexibility. We will continuously update the **code, models, and datasets**.
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***
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## 🏗️ Model Architecture
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<p align="center">
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<img src="assets/amadeus-framwork.drawio.png" alt="Amadeus architecture" width="600">
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</p>
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***
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## 📅 Changelog
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* 2025-08-28: Released inference code and the **Amadeus-S** model
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***
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## ⚙️ Installation and Usage
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Set up the environment (inference only):
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```bash
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conda create -n amadeus_slim python=3.10
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conda activate amadeus_slim
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pip install -r demo/requirements.txt
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```
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First run:
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```bash
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# Chinese interface
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python demo/Amadeus_app_CN.py
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# English interface
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python demo/Amadeus_app_EN.py
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```
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> Note:
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>
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> `Amadeus_app_CN.py`
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>
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> is for the Chinese interface, and
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>
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> `Amadeus_app_EN.py`
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>
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> is for the English interface.
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👉 The model will be automatically downloaded to the `models/` folder, which includes a usable **soundfont**. Please modify the path of `DEFAULT_SOUND_FONT` in `Amadeus/symbolic_encoding/``midi2audio.py`.
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Example of command-line generation:
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```
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python generate.py -wandb\_exp\_dir models/Amadeus-S -text\_encoder\_model google/flan-t5-base -temperature 2 -prompt "A lively and melodic pop rock song featuring piano, overdriven guitar, electric drum and electric bass, set in a fast 4/4 tempo and the key of C# minor, with a frequently recurring chord progression of D, A, C#m, and F# that evokes a mix of emotion and love."
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```
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***
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## 📂 Repository Structure
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```
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Amadeus/
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├── demo/ # Example scripts and interfaces (CN/EN)
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├── Amadeus/ # Core model and symbolic encoding
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├── assets/ # Architecture diagrams and sample audio files
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├── data\_representation # Data processing
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├── models/ # Downloaded or cached pre-trained models
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└── generate.py # Command-line generation entry point
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```
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***
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## 📊 Evaluation Results
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We evaluated **generation speed, text alignment, and note attribute control accuracy** on the **MidiCaps** dataset. The results are as follows:
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| Model | Speed (notes/s) | CLAP ↑ | TBT ↑ | CK ↑ | CTS ↑ | CI ↑ | CMtop3 ↑ |
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| -------------- | --------------- | -------- | --------- | --------- | --------- | --------- | --------- |
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| Text2Midi | 4.02 | 0.19 | 31.76 | 22.22 | 84.15 | 19.92 | 60.57 |
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| MuseCoco | 1.67 | 0.19 | 34.21 | 14.66 | 94.24 | 22.42 | 38.18 |
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| T2M-inferalign | 4.02 | 0.20 | 39.32 | 29.80 | 84.32 | 20.13 | 47.74 |
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| **Amadeus** | **16.23** | 0.20 | 73.93 | 39.31 | 96.98 | 26.01 | 65.52 |
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| **Amadeus-M** | 10.51 | **0.21** | **76.31** | **43.07** | **97.02** | **27.11** | **66.39** |
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***
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## 🤝 Acknowledgements and Contributions
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The development of Amadeus is inspired by the music and AI communities, with the goal of **serving music creators, not replacing them**.
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We welcome developers and researchers to contribute code or provide suggestions — please reach out to us via **Issues** or **Pull Requests**.
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Part of the design of this project references [JudeJiwoo/nmt](https://github.com/JudeJiwoo/nmt), and we would like to express our gratitude here 🙏.
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***
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## ⚠️ Notes
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The current model is relatively small and may not always generate MIDI that fully matches the description.
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You can try **slightly adjusting parameters such as temperature or top-p** to improve the results.
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We will continue to improve the model to provide more stable and higher-quality generation.
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***
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## 📚 Citation
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If you find Amadeus helpful for your research or create，please cite our paper:
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```bibtex
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@article{su2025amadeus,
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title = {Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music},
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author = {Su, Hongju and Li, Ke and Yang, Lan and Zhang, Honggang and Song, Yi-Zhe},
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journal = {arXiv preprint arXiv:2508.20665},
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year = {2025}
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}
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