Demo | Paper | Code | Space Demo
This repository is the official weight repository for LeVo: High-Quality Song Generation with Multi-Preference Alignment. In this repository, we provide the SongGeneration model, inference scripts, and the checkpoint that has been trained on the Million Song Dataset.
| Model | Max Length | Language | GPU Memory | RTF(H20) | Download Link |
|---|---|---|---|---|---|
| SongGeneration-base | 2m30s | zh | 10G/16G | 0.67 | Huggingface |
| SongGeneration-base-new | 2m30s | zh, en | 10G/16G | 0.67 | Huggingface |
| SongGeneration-base-full | 4m30s | zh, en | 12G/18G | 0.69 | Huggingface |
| SongGeneration-large | 4m30s | zh, en | 22G/28G | 0.82 | Huggingface |
| SongGeneration-v2-large | 4m30s | zh, en, es, ja, etc. | 22G/28G | 0.82 | Huggingface |
| SongGeneration-v2-medium | 4m30s | zh, en, es, ja, etc. | 12G/18G | 0.69 | Coming soon |
| SongGeneration-v2-fast | 4m30s | zh, en, es, ja, etc. | - | - | Coming soon |
🚀 We introduce LeVo 2 (SongGeneration 2), an open-source music foundation model designed to shatter the ceiling of open-source AI music by achieving true commercial-grade generation.
Through a large-scale, rigorous expert evaluation (20 industry professionals, 6 core dimensions, 100 songs per model), LeVo 2 has proven its superiority:
- 🏆 Commercial-Grade Musicality: Comprehensively outperforms all open-source baselines across Overall Quality, Melody, Arrangement, Sound Quality, and Structure. Its subjective generation quality successfully rivals top-tier closed-source commercial systems (e.g., MiniMax 2.5).
- 🎯 Precise Lyric Accuracy: Achieves an outstanding Phoneme Error Rate (PER) of 8.55%, effectively solving the lyrical hallucination problem. This remarkable accuracy significantly outperforms top commercial models like Suno v5 (12.4%) and Mureka v8 (9.96%).
- 🎛️ Exceptional Controllability: Highly responsive to multi-modal instructions, including text descriptions and audio prompts, allowing for precise control over the generated music.
📊 For detailed experimental setups and comprehensive metrics, please refer to the Evaluation Performance section below or our upcoming technical report.
The code and weights in this repository is released in the LICENSE file.
