README.md

---
language:
- en
metrics:
- PER
- WER
- SongEval
- Audio Aesthetics
- MuQ
- FAD
pipeline_tag: text-to-audio
library_name: diffusers
tags:
- music
- art
---
<div align="center">
  <img src="https://declare-lab.github.io/jamify-logo-new.png" width="200"/ >
 <br/>
  <h1>JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment</h1>
  <br/>
 
  [![arXiv](https://img.shields.io/badge/Read_the_paper-blue?style=flat&logoColor=blue&link=https%3A%2F%2Farxiv.org%2Fabs%2F)](https://arxiv.org/abs/2507.) [![Static Badge](https://img.shields.io/badge/JAM-Huggingface-violet?style=flat&logo=huggingface&logoColor=blue&link=https%3A%2F%2Fhuggingface.co%2Fdeclare-lab%2FJAM-0.5)](https://huggingface.co/declare-lab/JAM-0.5) [![Static Badge](https://img.shields.io/badge/Project-Jamify-pink?style=flat&logo=homepage&link=https%3A%2F%2Fdeclare-lab.github.io%2Fjamify)](https://declare-lab.github.io/jamify)
 
</div>

JAM is a rectified flow-based model for lyrics-to-song generation that addresses the lack of fine-grained word-level controllability in existing lyrics-to-song models. Built on a compact 530M-parameter architecture with 16 LLaMA-style Transformer layers as the Diffusion Transformer (DiT) backbone, JAM enables precise vocal control that musicians desire in their workflows. Unlike previous models, JAM provides word and phoneme-level timing control, allowing musicians to specify the exact placement of each vocal sound for improved rhythmic flexibility and expressive timing.

## Features

- **Fine-grained Word and Phoneme-level Timing Control**: The first model to provide word-level timing and duration control in song generation, enabling precise prosody control for musicians
- **Compact 530M Parameter Architecture**: Less than half the size of existing models, enabling faster inference with reduced resource requirements
- **Enhanced Lyric Fidelity**: Achieves over 3× reduction in Word Error Rate (WER) and Phoneme Error Rate (PER) compared to prior work through precise phoneme boundary attention
- **Global Duration Control**: Controllable duration up to 3 minutes and 50 seconds.
- **Aesthetic Alignment through Direct Preference Optimization**: Iterative refinement using synthetic preference datasets to better align with human aesthetic preferences, eliminating manual annotation requirements

## JAM Samples

Check out the example generated music in the `generated_examples/` folder to hear what JAM can produce:

- **`Hybrid Minds, Brodie - Heroin.mp3`** - Electronic music with synthesized beats and electronic elements
- **`Jade Bird - Avalanche.mp3`** - Country music with acoustic guitar and folk influences  
- **`Rizzle Kicks, Rachel Chinouriri - Follow Excitement!.mp3`** - Rap music with rhythmic beats and hip-hop style

These samples demonstrate JAM's ability to generate high-quality music across different genres while maintaining vocal intelligence, style consistency and musical coherence.

## Requirements

- Python 3.10 or higher
- CUDA-compatible GPU with sufficient VRAM (8GB+ recommended)


## Installation

### 1. Clone the Repository

```bash
git clone https://github.com/declare-lab/jamify
cd jam
```

### 2. Run Installation Script

The project includes an automated installation script, run it in your own virtual environment:

```bash
bash install.sh
```

This script will:
- Initialize and update git submodules (DeepPhonemizer)
- Install Python dependencies from `requirements.txt`
- Install the JAM package in editable mode
- Install the DeepPhonemizer external dependency

### 3. Manual Installation (Alternative)

If you prefer manual installation:

```bash
# Initialize submodules
git submodule update --init --recursive

# Install dependencies
pip install -r requirements.txt

# Install JAM package
pip install -e .

# Install DeepPhonemizer
pip install -e externals/DeepPhonemizer
```

## Quick Start

### Simple Inference

The easiest way to run inference is using the provided `inference.py` script:

```python
python inference.py
```

This script will:
1. Download the pre-trained JAM-0.5 model from Hugging Face
2. Run inference with default settings
3. Save generated audio to the `outputs` directory

### Input Format

Create an input file at `inputs/input.json` with your songs:

```json
[
  {
    "id": "my_song",
    "audio_path": "inputs/reference_audio.mp3",
    "lrc_path": "inputs/lyrics.json", 
    "duration": 180.0,
    "prompt_path": "inputs/style_prompt.txt"
  }
]
```

Required files:
- **Audio file**: Reference audio for style extraction
- **Lyrics file**: JSON with timestamped lyrics
- **Prompt file**: Text description of desired style/genre. Text prompt is not used in the default setting where the audio reference is utilized.

## Advanced Usage

### Using `python -m jam.infer`

For more control over the generation process:

```bash
# Basic usage with custom checkpoint
python -m jam.infer evaluation.checkpoint_path=path/to/model.safetensors

# With custom output directory
python -m jam.infer evaluation.checkpoint_path=path/to/model.safetensors evaluation.output_dir=my_outputs

# With custom configuration file
python -m jam.infer config=configs/my_config.yaml evaluation.checkpoint_path=path/to/model.safetensors
```

### Multi-GPU Inference

Use Accelerate for distributed inference:

```bash
# Basic usage with custom checkpoint
accelerate launch --config_path path/to/accelerate/config.yaml jam.infer

# With custom configuration file
accelerate launch --config_path path/to/accelerate/config.yaml jam.infer config=path/to/inference/config.yaml
```

## Configuration Options

### Key Parameters

#### Evaluation Settings
- `evaluation.checkpoint_path`: Path to model checkpoint (required)
- `evaluation.output_dir`: Output directory (default: "outputs")
- `evaluation.test_set_path`: Input JSON file (default: "inputs/input.json")
- `evaluation.batch_size`: Batch size for inference (default: 1)
- `evaluation.num_samples`: Only generate first n samples in test_set_path (null = all)
- `evaluation.vae_type`: VAE model type ("diffrhythm" or "stable_audio")

#### Style Control
- `evaluation.ignore_style`: Ignore style prompts (default: false)
- `evaluation.use_prompt_style`: Use text prompts for style (default: false)
- `evaluation.num_style_secs`: Style audio duration in seconds (default: 30)
- `evaluation.random_crop_style`: Randomly crop style audio (default: false)

## Input File Formats

### Lyrics File (`*.json`)
```json
[
    {"start": 2.2, "end": 2.5, "word": "First word of lyrics"},
    {"start": 2.5, "end": 3.7, "word": "Second word of lyrics"},
    {"more lines ...."}
]
```

### Style Prompt File (`*.txt`)
```
Electronic dance music with heavy bass and synthesizers
```

### Input Manifest (`input.json`)
```json
[
  {
    "id": "unique_song_id",
    "audio_path": "path/to/reference.mp3",
    "lrc_path": "path/to/lyrics.json",
    "duration": 180.0,
    "prompt_path": "path/to/style.txt"
  }
]
```

## Output Structure

Generated files are saved to the output directory:

```
outputs/
├── generated/          # Final trimmed audio files
├── generated_orig/     # Original generated audio
├── cfm_latents/       # Intermediate latent representations
├── local_files/       # Process-specific metadata
└── generation_config.yaml  # Configuration used for generation
```

## Performance Tips

1. **GPU Memory**: Use `evaluation.batch_size=1` for large on limited VRAM
2. **Multi-GPU**: Use `accelerate launch` for faster processing of multiple samples
3. **Mixed Precision**: Add `--mixed_precision=fp16` to reduce memory usage

## Troubleshooting

### Common Issues

#### "Checkpoint path not found"
```bash
# Make sure to specify the checkpoint path
python -m jam.infer evaluation.checkpoint_path=path/to/your/model.safetensors
```

#### "CUDA out of memory"
```bash
# Reduce batch size or use mixed precision
accelerate launch --mixed_precision=fp16 -m jam.infer evaluation.checkpoint_path=model.safetensors
```

#### "Test set not found"
```bash
# Create input.json file in inputs/ directory or specify custom path
python -m jam.infer evaluation.test_set_path=path/to/your/input.json evaluation.checkpoint_path=model.safetensors
```

## Model Downloads

The `inference.py` script automatically downloads the JAM-0.5 model. For manual download:

```python
from huggingface_hub import snapshot_download
model_path = snapshot_download(repo_id="declare-lab/jam-0.5")
```

## Citation

If you use JAM in your research, please cite:

```bibtex
@misc{jam2024,
  title={JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment},
  author={Renhang Liu and Chia-Yu Hung and Navonil Majumder and Taylor Gautreaux and Amir Ali Bagherzadeh and Chuan Li and Dorien Herremans and Soujanya Poria},
  year={2025}
}
```

## License

**JAM** is the first open-sourced model released under **Project Jamify**, developed for facilitating academic research and creative exploration in AI-generated songs from lyrics. The model is subject to:

1. **Project Jamify License**: Intended **solely for non-commercial, academic, and entertainment purposes**
2. **Stability AI Community License Agreement**: Required due to use of Stability AI model components

### Key Restrictions:
- **No copyrighted material** was used in a way that would intentionally infringe on intellectual property rights
- **JAM is not designed** to reproduce or imitate any specific artist, label, or protected work
- Outputs generated by JAM must **not be used to create or disseminate content that violates copyright laws**
- **Commercial use of JAM or its outputs is strictly prohibited**
- **Attribution Required**: Must retain "This Stability AI Model is licensed under the Stability AI Community License, Copyright © Stability AI Ltd. All Rights Reserved."

### Responsibility
Responsibility for the use of the model and its outputs lies entirely with the end user, who must ensure all uses comply with applicable legal and ethical standards.

For complete license terms, see [LICENSE.md](LICENSE.md) and [STABILITY_AI_COMMUNITY_LICENSE.md](STABILITY_AI_COMMUNITY_LICENSE.md).

For questions, concerns, or collaboration inquiries, please contact the Project Jamify team via the official repository.

## Support

For issues and questions:
- Open an issue on GitHub
- Check the troubleshooting section above
- Review the configuration options for parameter tuning