HeartMuLa is the open-source library and reference implementation for the HeartMuLa family of music foundation models, designed to support both music generation and music-related understanding tasks in a cohesive stack. At the center is HeartMuLa, a music language model that generates music conditioned on inputs like lyrics and tags, with multilingual support that broadens the range of lyric-driven use cases. The project also includes HeartCodec, a music codec optimized for high reconstruction fidelity, enabling efficient tokenization and reconstruction workflows that are critical for training and generation pipelines. For text extraction from audio, it provides HeartTranscriptor, a Whisper-based model tuned specifically for lyrics transcription, which helps bridge generated or recorded audio back into structured text. It also introduces HeartCLAP, which aligns audio and text into a shared embedding space.
Features
- Music generation model conditioned on lyrics and descriptive tags
- Multilingual lyric support for broader creative workflows
- High-fidelity music codec for audio tokenization and reconstruction
- Lyrics transcription model tuned from a Whisper baseline
- Audio–text alignment embeddings for cross-modal retrieval
- Reference library with example workflows for inference and evaluation