HeartMuLa: A Family of Open Sourced Music Foundation Models
Abstract
We present a family of open-source Music Foundation Models designed to advance large-scale music understanding and generation across diverse tasks and modalities. Our framework consists of four major components: (1) HeartCLAP, an audio-text alignment model; (2) HeartTranscriptor, a robust lyric recognition model optimized for real-world music scenarios; and (3) HeartCodec, a low-frame-rate (12.5 Hz) yet high-fidelity music codec tokenizer that captures long-range musical structure while preserving fine-grained acoustic details and enabling efficient autoregressive modeling; (4) HeartMuLa, an LLM-based song generation model capable of synthesizing high-fidelity music under rich, user-controllable conditions (e.g., textual style descriptions, lyrics, and reference audio). In addition, it provides two specialized modes: (i) fine-grained musical attribute control, which allows users to specify the style of different song sections (e.g., intro, verse, chorus) using natural language prompts; and (ii) short, engaging music generation, which is suitable as background music for short videos. Lastly, HeartMuLa improves significantly when scaled to 7B parameters. For the first time, we show that a Suno-level, commercial-grade system can be reproduced using academic-scale data and GPU resources. We expect these foundation models to serve as strong baselines for future research and to facilitate practical applications in multimodal content production.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a family of open-source music foundation models that unify various aspects of music understanding and generation, providing a robust framework for future research and practical applications in the creative industry. The integration of advanced methodologies and the potential for user-controlled generation mark a significant advancement in the field of machine learning for audio.
Methodology
The paper introduces a comprehensive framework for music generation and understanding, comprising four distinct models that address various aspects of music processing. HeartCLAP focuses on audio-text alignment, HeartTranscriptor enhances lyric recognition, HeartCodec serves as a high-fidelity music tokenizer, and HeartMuLa is an LLM-based model for music generation. The methodology is well-structured, leveraging existing advancements in audio processing while innovating through the integration of low-frame-rate tokenization and user-controllable generation, which is a significant step forward in the field.
Results & evaluation
The authors claim extensive experiments demonstrating improvements over existing baselines in terms of reconstruction quality and generation performance. However, specific details regarding datasets, experimental setups, and quantitative results are not provided in the abstract or methodology sections, which limits the ability to fully assess the robustness of their claims. The scaling to 7B parameters is a notable highlight, suggesting that the model can handle larger contexts effectively.
Reproducibility
The paper mentions that the models are open-sourced, which is a positive aspect for reproducibility. However, without a dedicated URL for the code or datasets, it is challenging to evaluate the ease of reproducing the results. Clear documentation and access to the models would be necessary to facilitate further research.
Limitations
The paper does not address potential limitations or challenges encountered during the model development or the implications of using large-scale models in music generation. Additionally, while the ethical considerations are mentioned, the practical challenges of implementing watermarking and ensuring accountability in AI-generated music are not thoroughly discussed.
Broader impact
The HeartMuLa framework has the potential to significantly impact the music industry by providing tools for content creators to generate music efficiently and creatively. The open-source nature of the project could foster collaboration and innovation in the field of AI music generation, although ethical implications and copyright issues remain critical considerations. The main contribution of this paper is the introduction of a family of open-source music foundation models that unify various aspects of music understanding and generation, providing a robust framework for future research and practical applications in the creative industry. The integration of advanced methodologies and the potential for user-controlled generation mark a significant advancement in the field of machine learning for audio.
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