Any2Music: Exploration of Music Generation with Multimodal Encoder-Decoder Architecture

This article introduces the Any2Music project developed by FelipeMarra, which is based on a multimodal encoder-decoder architecture and explores technical paths for generating music from multiple input modalities such as text, images, and audio, providing new implementation references for AI music creation. The core of the project lies in breaking the limitation of single modality and realizing the paradigm of "any input to music", which has important technical inspiration significance.

Project Basic Information:

• Original Author/Maintainer: FelipeMarra
• Source Platform: GitHub
• Original Link: https://github.com/FelipeMarra/any2music
• Release Date: 2026-06-16

Traditional music generation models are often limited to a single modality (e.g., text-to-music, melody continuation). As an art form integrating auditory perception, emotional expression, structural logic, and cultural context, a single modality can hardly fully capture creative needs. The Any2Music project attempts to break this limitation by applying multimodal AI technology to the field of music generation, representing a new direction in AI music creation.

The core of Any2Music is the multimodal encoder-decoder architecture:

• Encoder Part: Supports text, image, audio, and other inputs. The text encoder extracts style/emotion semantics; the image encoder analyzes color/atmosphere visual features; the audio encoder extracts style/rhythm features of reference music. All encoder outputs are projected into a shared embedding space to achieve cross-modal fusion.
• Decoder Part: Converts the fused representation into music output, supporting symbolic music (MIDI, generating note sequences via autoregressive/diffusion models) and raw audio (generating waveforms using vocoders or end-to-end synthesis techniques).

Multimodal Fusion Challenges: Need to solve modal alignment (e.g., associating "sad blue画面" with music features) and modal conflict (tone decision when input modal information is inconsistent), which may use attention mechanisms, gated fusion, or multimodal Transformers. Tech Stack Speculation: Encoders may be based on pre-trained models like CLIP (image-text) and Whisper (audio); decoders may use Music Transformer or diffusion models. Training and Evaluation: Training data requires paired (input modality, music) samples; evaluation needs to consider both music quality (harmonic complexity, melody variation) and cross-modal consistency (manual or similarity metrics).

Any2Music can be applied in various scenarios:

1. Video Soundtrack: Upload a video to automatically generate background music matching the emotion/rhythm;
2. Image-to-Music: Convert photos (e.g., sunset beach → soothing guitar music, city night view → electronic music) into music;
3. Text-to-Music: Generate desired music via natural language description (e.g., "energetic electronic music for morning runs");
4. Style Transfer: Reinterpret existing songs into other styles (e.g., pop to jazz).

Comparison with Existing Tools: Compared to Suno/Udio (text-to-music) and MusicLM (audio continuation), Any2Music's advantage lies in the flexibility of multi-modal input, but it also increases technical complexity and user threshold. Limitations: Scarce multi-modal training data, unstable generation quality due to cross-modal semantic gap, high computational resource requirements. Future Directions: Expand more modalities (tactile/motion data), improve music controllability (instruments/rhythm/structure), optimize user interaction interface.

The Any2Music project is an important attempt in the development of AI music generation towards the multi-modal direction, demonstrating the possibility of integrating visual, language, auditory, and other perceptual modalities, opening up new paths for AI-assisted artistic creation. Although in the early stage, its exploration direction is inspiring for the future development of AI music tools, and it is expected to promote more diverse, intuitive, and personalized music creation.