MORTM

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")