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nagoshidayo
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MORTM

music-generation
transformer
MoE
ALiBi
FlashAttention
melody-generation
rhythmic-modeling
MIDI or Chord to MIDI or Chord
Model card Files Community
YAML Metadata Warning: The pipeline tag "MIDI or Chord to MIDI or Chord" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other

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]

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")
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