Multimodal LLM Project
This project demonstrates a conceptual framework for a multimodal large language model (LLM) that can process and generate responses based on text, images, and audio input. The current implementation provides a basic structure and placeholder functions, illustrating the key components of such a system.
Functionality
The core functionality includes:
- Input Processing: Takes text, image, and/or audio as input.
- Multimodal Fusion (Placeholder): Currently a placeholder. In a complete system, this stage would involve encoding the different modalities into a shared embedding space, allowing the model to understand the relationships between them.
- Response Generation: Outputs text responses based on the input. The code includes examples of generating images and MIDI files, although these are currently placeholders.
Key Features (Planned)
- Image Generation: Generate images based on textual prompts (placeholder implementation currently uses dummy images).
- Music Generation: Generate MIDI files based on textual prompts (placeholder implementation currently produces a simple MIDI file with a C major scale).
- Text-to-Speech: Converts text prompts into audio output (using
espnet/kan-bayashi_ljspeech_vits).
- Multimodal Understanding: A robust model would be capable of understanding relationships between the different modalities (e.g., describing an image provided as input, generating music based on the descriptive text provided as input).
Getting Started
- Install Dependencies:
