Fretting-Transformer: MIDI to Guitar Tablature

• Fretting-Transformer is an encoder–decoder framework built on the T5 architecture that converts MIDI sequences into musically and ergonomically viable guitar tablature.
• It addresses string–fret ambiguity by learning context-sensitive mappings that optimize finger positioning and prevent abrupt, non-ergonomic transitions.
• Empirical evaluations show that its novel accuracy and playability metrics outperform traditional algorithms and commercial systems in automated transcription.

The Fretting-Transformer model is an encoder–decoder framework targeting automated translation of MIDI sequences into guitar tablature, employing the T5 transformer architecture. Developed to address the inherent limitations of symbolic music representations—particularly for stringed instruments where MIDI events lack crucial playability information—the model constructs both musically correct and physically executable transcriptions. Leveraging diverse datasets with domain-specific pre-processing, it introduces novel accuracy and playability metrics and offers context-sensitive output surpassing both traditional algorithms and commercial systems in empirical evaluations.

1. Model Architecture and Foundations

The Fretting-Transformer is founded on the T5 transformer architecture, characterized by a multi-layered encoder–decoder structure incorporating multi-head self-attention and cross-attention mechanisms. The encoder ingests tokenized MIDI sequences encoding musical events, while the decoder emits sequences of tokens corresponding to guitar tablature, including explicit string and fret assignments.

A central operation in the model is multi-head self-attention:

$$A = \text{softmax} \left( \frac{QK^{\mathsf{T}}}{\sqrt{d_k}} \right) \cdot V$$

where $Q$, $K$, and $V$ are the query, key, and value matrices, and $d_k$ is the key dimension. Multiple layers of such attention capture bounded temporal dependencies, polyphonic structure, and phrase-level context.

Critically, the Fretting-Transformer augments the base T5 encoder–decoder by conditioning the decoder’s generation on guitar-specific ergonomic principles. This enables direct modeling of physical constraints required for human playability, ensuring that transcriptions are not only musically intelligible but also viable for performance.

2. MIDI-to-Tablature Transcription and String–Fret Disambiguation

Automated MIDI-to-tablature transcription for guitar presents key challenges:

• String–Fret Ambiguity: A given pitch can be realized on numerous string–fret combinations; naive assignment ignores ergonomic and musical context. The Fretting-Transformer resolves ambiguity by learning context-dependent mappings, favoring efficient finger positions and coherent musical phrasing.
• Physical Playability: Beyond pitch sequencing, the model generates tablature accounting for hand positioning, minimizing abrupt shifts or stretches that violate human motor constraints.

During generation, each output token encodes both the intended pitch event and a suggested string–fret location. Conditioning incorporates phrase context, chordal structure, and prior finger positions to optimize transitions and minimize physical effort. The decoder thus produces sequences structured for ergonomic realizability as well as musico-symbolic correctness.

3. Data Sources and Tokenization Strategies

The model’s training corpus integrates multiple datasets emphasizing annotated playability and stylistic diversity:

Dataset	Source	Features
DadaGP	GuitarPro Files (tokenized)	Technique & style diversity
GuitarToday	Fingerstyle tabs, tutorials	Expert-validated fingering
Leduc	François Leduc Online Library	Classical/varied tuning schemes

Pre-processing transforms raw symbolic data (MIDI and human-generated tabs) into token sequences appropriate for transformer ingestion. Tokens encode not only note parameters (pitch, timing, velocity) but also instrumental configuration (tuning, capo position) and positional information required for string–fret assignment.

The advanced tokenization scheme segments events to allow joint modeling of musical and ergonomic features. This grants the architecture access to guitar-specific representational detail omitted by standard symbolic approaches.

4. Evaluation Metrics: Tablature Accuracy and Playability

Model outputs are assessed using two principal metric families:

• Tablature Accuracy: Quantifies the fidelity of transcription, verifying that the generated tablature reflects the target pitch sequence and selects appropriate string–fret locations. This metric discriminates errors in symbolic translation, including incorrect note or position assignment.
• Playability Metrics: Evaluate ergonomic feasibility, measuring factors such as abrupt positional jumps, non-ergonomic finger stretches, and chord transitions. Metrics derive from analysis of finger placement and the relative ease of executing the sequence on a physical instrument.

The use of playability metrics differentiates Fretting-Transformer from previous approaches, emphasizing that optimal transcription must balance musical correctness with performative realism. Experimental results demonstrate lower error rates and improved ergonomic outcomes compared to baseline rule-based or search-based algorithms.

5. Comparative Performance and Baseline Methods

The Fretting-Transformer is evaluated against traditional approaches such as the A* search algorithm, which computes optimal fingerings through combinatorial search for minimal cost paths but lacks context-sensitive adaptation. Commercial applications like Guitar Pro require manual corrections or post-processing to optimize for playability and accuracy.

Empirical findings show that Fretting-Transformer surpasses baselines in both note transcription accuracy and ergonomic rating. Unlike algorithms relying on isolated cost models or manual rule sets, the transformer learns context-conditioned mappings directly from diverse, annotated data.

Method	Symbolic Accuracy	Playability (Ergonomics)	Context Sensitivity
A* Search	Moderate	Limited	None
Guitar Pro	Varies	Post-hoc, manual	Partial
Fretting-Transformer	High	Superior	Full

A plausible implication is that context-sensitive learning architectures, as instantiated here, offer a scalable solution to domain-specific symbolic-to-physical translation tasks in music information retrieval.

6. Advanced Features and Future Prospects

The current implementation incorporates several advanced mechanisms:

• Context-Sensitive Processing: Dynamic adjustment of fingerings throughout musical phrases enhances harmonic coherence and realism.
• Tuning and Capo Conditioning: Enables flexible adaptation to various instrument setups, supporting custom tunings and physical modifications such as capo placement.

Anticipated future directions include:

• Integration of real-time user feedback for interactive transcription refinement.
• Extension to multi-instrument arrangements, encompassing bass and rhythm accompaniments.
• Enhancement of playability metrics through biomechanical modeling and empirical performance data.

This suggests that the Fretting-Transformer framework, with its domain-specialized adaptations and transformer-based core, lays the groundwork for increasingly sophisticated automated transcription systems across a range of musical contexts.

7. Significance and Impact

By directly addressing string–fret ambiguity and physical playability within a deep learning paradigm, the Fretting-Transformer advances the state of the art in automated guitar transcription. The model’s architecture, tokenization, and metric development provide a unified framework for bridging symbolic music representations and practical performance requirements. Its empirical superiority over baseline methods points to the efficacy of contextualized, data-driven approaches, with substantial implications for music information retrieval, digital instrument pedagogy, and algorithmic arrangement generation. This integration marks a substantive step toward fully automated, context-aware transcription pipelines capable of producing musically and physically executable output for human performers (Hamberger et al., 17 Jun 2025).