pipeline_tag: MIDI or Chord to MIDI or Chord
tags:
- music-generation
- transformer
- MoE
- ALiBi
- FlashAttention
- melody-generation
- rhythmic-modeling
Model Card for MORTM (Metric-Oriented Rhythmic Transformer for Melodic generation)
MORTM is a Transformer-based model designed for melody generation, with a strong emphasis on metric (rhythmic) structure. It represents music as sequences of pitch, duration, and relative beat positions within a measure (normalized to 96 ticks), making it suitable for time-robust, rhythm-aware music generation tasks.
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
MORTM (Metric-Oriented Rhythmic Transformer for Melodic generation) is a decoder-only Transformer architecture optimized for music generation with rhythmic awareness. It generates melodies measure-by-measure in an autoregressive fashion. The model supports chord-conditional generation and is equipped with the following features:
Mixture of Experts (MoE) in the feedforward layers for capacity increase and compute efficiency.
ALiBi (Attention with Linear Biases) for relative positional biasing.
FlashAttention2 for fast and memory-efficient attention.
Relative tick-based tokenization (e.g., [Position, Duration, Pitch]) for metric robustness.
Developed by: Koue Okazaki & Takaki Nagoshi
Funded by [optional]: Nihon University, Graduate School of Integrated Basic Sciences
Shared by [optional]: ProjectMORTM
Model type: Transformer (decoder-only with MoE and ALiBi)
Language(s) (NLP): N/A (music domain)
License: MIT
Finetuned from model [optional]: Custom-built from scratch (not fine-tuned from a pretrained LM)
Model Sources [optional]
- Repository: https://github.com/Ayato964/MORTM (replace with actual link)
- Paper [optional]: In submission
- Demo [optional]: Coming soon
Uses
Direct Use
MORTM can generate melodies from scratch or conditionally based on chord progressions. It is ideal for:
- Melody composition in pop, jazz, and improvisational styles.
- Real-time melodic suggestion systems for human-AI co-creation.
- Music education and melody completion tools.
Downstream Use [optional]
- Style transfer with different chord inputs.
- Harmonization and rhythm-based accompaniment systems.
Out-of-Scope Use
- Audio-to-audio tasks (e.g., vocal separation).
- Raw audio synthesis (requires additional vocoder).
- Not suitable for genre classification or music recommendation.
Bias, Risks, and Limitations
As the training dataset is primarily composed of Western tonal music, the model may underperform on:
- Non-tonal, microtonal, or traditional music styles.
- Polyrhythmic or tempo-variable music.
- Genres not sufficiently represented in training data (e.g., Indian classical).
Recommendations
Generated melodies should be manually reviewed in professional music contexts. Users are encouraged to retrain or fine-tune on representative datasets when applying to culturally specific music.
How to Get Started with the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("nagoshidayo/mortm")
tokenizer = AutoTokenizer.from_pretrained("nagoshidayo/mortm")