Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription

2025/06/17 by Anna Hamberger, Sebastian Murgul, Hamberger, Anna +5 · 2 citations
Arts and Humanities · Computer Science · #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #Diverse Musicological Studies #FOS: Computer and information sciences #FOS: Electrical engineering #Multimedia (cs.MM) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.14223

openalex publication_date 2025/06/17 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

Abstract

Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription.

Citations

• MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling
• High Resolution Guitar Transcription via Domain Adaptation
• miditok: A Python package for MIDI file tokenization
• Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer
• DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
• Automatic Composition of Guitar Tabs by Transformers and Groove Modeling
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
• Automatic Music Transcription: An Overview
• Music Transformer
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Cited by

• Explicit Note-Event Tokenization and Pitch-Validity Constrained Decoding for MIDI-to-Tablature Transcription
• TART: A Comprehensive Tool for Technique-Aware Audio-to-Tab Guitar Transcription

Related

• MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling
• Explicit Note-Event Tokenization and Pitch-Validity Constrained Decoding for MIDI-to-Tablature Transcription
• TART: A Comprehensive Tool for Technique-Aware Audio-to-Tab Guitar Transcription
• A Machine Learning Approach for MIDI to Guitar Tablature Conversion
• SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription