MIDI-LLM: Text-to-MIDI Music Generation

• MIDI-LLM is a large language model that maps free-form text to symbolic MIDI music using a joint text-MIDI tokenization scheme.
• It employs a unified transformer architecture augmented with 55,000 MIDI-specific tokens to efficiently support text-to-MIDI generation and analysis.
• The model achieves state-of-the-art metrics like FAD and CLAP, enabling real-time, controllable, and high-fidelity symbolic music production.

MIDI-LLM refers to the class of LLMs and associated methods designed to generate, analyze, or interact with symbolic music in MIDI (Musical Instrument Digital Interface) format, often from free-form text prompts. These systems operate at the intersection of symbolic music modeling, natural language processing, and multimodal artificial intelligence, leveraging the architecture and training paradigms of LLMs to enable novel forms of text-to-MIDI generation, retrieval, and music understanding. The MIDI-LLM paradigm has catalyzed new datasets, model adaptations, quality benchmarks, and deployment strategies that support efficient and controllable symbolic music generation at scale.

1. Definition and Scope

A MIDI-LLM is a LLM, typically with a Transformer-based architecture, that is trained, adapted, or fine-tuned to process both natural language and MIDI musical information. The core capability is the direct mapping between free-form text (captions, prompts, or descriptions) and symbolic MIDI output, enabling tasks such as text-conditional music generation, music captioning, retrieval, and cross-modal understanding. MIDI-LLMs may also encompass models that reason over, infill, or harmonize symbolic music using LLM techniques, but the defining characteristic is the joint handling of rich textual and symbolic (MIDI) representations within a unified machine learning framework (Wu et al., 6 Nov 2025).

2. Architectural Innovations and Tokenization

Most MIDI-LLMs are derived from advances in LLM architectures such as Llama, T5, or GPT-2. MIDI-LLMs expand conventional text LLMs by augmenting the vocabulary to include special MIDI tokens, often through custom tokenization schemes. For example, MIDI-LLM extends the Llama 3.2 model’s vocabulary with over 55,000 MIDI-specific tokens, representing onset time, note duration, and instrument-pitch combinations—using the Anticipatory Music Transformer (AMT) triplet grammar as a compact and efficient tokenization format. The expanded embedding matrix thus incorporates both standard text and symbolic music tokens, allowing the model to process native MIDI instructions with minimal architectural alterations:

$$\mathbf{E}_{\text{Midi-LLM}} := \begin{bmatrix} \mathbf{E}_{\text{LLM}} \ \mathbf{E}_{\text{AMT}} \end{bmatrix} \in \mathbb{R}^{(|\mathcal{V}_{\text{LLM}}| + |\mathcal{V}_{\text{AMT}}|) \times D}$$

where $\mathbf{E}_{\text{LLM}}$ and $\mathbf{E}_{\text{AMT}}$ are the embedding tables for regular language and MIDI tokens, respectively, and $D$ is the hidden size. The remainder of the transformer stack (self-attention, position encoding, output heads) is left intact, preserving compatibility with standard LLM toolchains and highly optimized inference libraries (Wu et al., 6 Nov 2025).

Most previous approaches—such as text2midi and Composer's Assistant—instead used encoder-decoder architectures, freezing the encoder LLM for captions, and training a specialized transformer decoder for symbolic music tokens (REMI, custom event grammars) (Bhandari et al., 21 Dec 2024, Malandro, 2023). By contrast, modern MIDI-LLMs are typically single-stack models with unified vocabularies, supporting both mixed-modal training and inference without additional cross-attention connections or bespoke decoders.

3. Training Paradigms and Data Resources

Two-Stage Training Recipe

MIDI-LLMs employ a two-stage training strategy:

1. Continued Pretraining: The base LLM is pretrained further on a curated blend of music-relevant text corpus (e.g., MusicPile, containing musical Q&A, ABC notation, synthetic music knowledge) and standalone MIDI sequences—tokenized in the model's MIDI grammar. No explicit text-MIDI pairing is required in this stage; the objective is next-token prediction, thereby acclimating the model to the distribution of both modalities.
2. Supervised Finetuning: The model is then fine-tuned on text–MIDI pairs, most commonly using high-quality, human-generated datasets such as MidiCaps, where each MIDI file is aligned with a detailed, multi-sentence textual description capturing objective (key, tempo, chord progression, instruments) and subjective (mood, genre, style) attributes (Melechovsky et al., 4 Jun 2024). Data augmentation techniques (e.g., Qwen2.5-Omni generated paraphrases, infilling samples) further expand the supervised domain and model robustness (Wu et al., 6 Nov 2025).

Data Resources

• MidiCaps (Melechovsky et al., 4 Jun 2024): The principal open dataset for text-to-MIDI training, comprising 168,407 curated MIDI files and LLM-generated captions. Captions are constructed via a feature extraction pipeline (Music21, Mido, Essentia, Chordino), and synthesized by SOTA LLMs (Claude 3 Opus) using in-context learning, ensuring grammaticality, feature fidelity, and multiperspective music description. The dataset yields multi-genre, multi-instrument, and multi-mood coverage.
• GigaMIDI (Lee et al., 24 Feb 2025): For general pretraining, models may exploit GigaMIDI—1.4M+ MIDI files, with track-level expressivity heuristics (DNVR, DNODR, NOMML) to curate subsets of expressively performed music. This large corpus enables scaling and diverse musical exposure, especially for pure symbolic pretraining.
• Auxiliary resources: Lakh MIDI, SymphonyNet for domain transfer, additional datasets for special tasks (e.g., performance modeling, sentiment/emotion detection, music information retrieval).

4. Evaluation and Benchmarking

Quality and Controllability

MIDI-LLMs are evaluated on multiple axes:

• Fréchet Audio Distance (FAD): Captures the statistical similarity between the feature distributions of synthesized and real audio rendered from generated MIDI, lower is better.
• CLAP Score: Cosine similarity between CLAP model embeddings of the prompt and the synthesized audio (using contrastive language-audio pretraining), higher is better. Reflects text-music alignment.
• Key/Tempo/Chord Matching: Proportion of generations matching user-specified musical features.
• Compression Ratio: Measures structural repetition and motif development in generated outputs.
• Inference Speed and Efficiency: Real-time factor (RTF), generation time for long outputs, critical for live or interactive applications.

In direct comparison, MIDI-LLM achieves FAD = 0.173 and CLAP = 22.1, outperforming Text2midi (FAD = 0.818, CLAP = 18.7) (Wu et al., 6 Nov 2025). MIDI-LLM also offers generation at 3–14× real-time speed, with batch-4 parallelization and support for hardware quantization (FP8), yielding performance improvements over prior encoder-decoder pipelines.

Human Evaluation

Listening studies with both general audiences and music experts (see MidiCaps) show that LLM-generated captions and, by extension, LLM-conditional generations are judged as human-like and accurate, rivaling or exceeding expert annotator ratings in certain low-level tasks (key/tempo). This suggests that, when trained on detailed captioned datasets, MIDI-LLMs can produce contextually faithful musical continuations from varied natural language prompts.

5. Symbolic Reasoning, Limitations, and Comparative Insights

MIDI-LLMs display exceptional proficiency in symbolic score reasoning: when provided MIDI input or operating in text-to-MIDI mode, state-of-the-art models achieve near-ceiling performance on core structural music perception/analysis tasks—syncopation scoring, transposition detection, chord quality identification. Crucially, this "reasoning" is limited to the symbolic domain. As demonstrated in recent evaluations (Carone et al., 25 Oct 2025), performance drops significantly when input is audio, as perceptual extraction of symbolic features becomes the bottleneck.

Modality	Task	SOTA LLM (Gemini Pro) Accuracy
MIDI	Syncopation	95–100%
MIDI	Chord ID (LogicLM)	100%
Audio	Syncopation	25–65%
Audio	Chord ID	30–50%

This establishes that MIDI-based "musical understanding" in LLMs reflects strong symbolic computation but does not entail true audio-level perception. Such findings have methodological implications: future benchmarks must disentangle symbolic reasoning from auditory transcription, and models designed for music listening must cultivate robust audio-to-symbolic pipelines before integrating LLM reasoning layers (Carone et al., 25 Oct 2025).

In mapping and generation directions, text2midi and MIDI-LLM demonstrate the value of end-to-end architectures, with MIDI-LLM's preservation of vanilla LLM structure enabling seamless use of vLLM and related infrastructure for practical application, covering both batch and interactive/real-time music generation (Wu et al., 6 Nov 2025, Bhandari et al., 21 Dec 2024).

6. Downstream Tasks and Application Domains

The MIDI-LLM paradigm supports a wide array of downstream research and creative tasks:

• Text-to-MIDI Generation: Free-form, prompt-driven multitrack music composition, encompassing genre, style, instrumentation, and subjective descriptors. Conditional and infilling capabilities allow refining or regenerating sections of a composition in context (Wu et al., 6 Nov 2025, Bhandari et al., 21 Dec 2024).
• Multi-modal Embedding and Retrieval: Cross-modal semantic embedding enables music search by text and vice versa, as in CLaMP 2, which leverages LLM-based captioning and contrastive learning over ABC–MIDI–text triplets for multilingual retrieval (Wu et al., 17 Oct 2024).
• Music Captioning and Analysis: Generation of human-readable, multi-sentence descriptions for MIDI segments, supporting musicology, music information retrieval, and generative model evaluation (Melechovsky et al., 4 Jun 2024).
• Interactive Composition Tools: Full integration into DAWs (Composer's Assistant), real-time error correction (RNN-based aids), local browser deployment (MIDInfinite), and web applications, extending symbolic music AI to both end-users and creative professionals (Malandro, 2023, Marinov, 2020, Zhou et al., 14 Nov 2024).
• Expressivity and Performance Modeling: GigaMIDI's detection heuristics for expressive MIDI permit training models that better capture human-like microtiming and dynamics (Lee et al., 24 Feb 2025).

7. Limitations and Future Directions

MIDI-LLMs, despite major advances, have several limitations:

• Surface-Level Audio Understanding: Performance saturates at the symbolic level; models do not yet "listen" as humans do, and are dependent on the quality and scope of symbolic input data (Carone et al., 25 Oct 2025).
• Prompt Conditioning and Coverage: Capturing rare or detailed attributes from under-specified or ambiguous prompts remains a challenge. Instrument coverage and chord/key fidelity may lag in excerpts versus full-length generation (Bhandari et al., 21 Dec 2024).
• Data Constraints: The expressiveness, coverage, and diversity of training datasets (MIDI and captions) directly bound generalization. Ongoing dataset enrichment, especially across genres, cultures, and expressive modalities, is required.
• Evaluation Metrics: No consensus exists on the optimal musical quality benchmarks; metrics such as CLAP, FAD, and structural analysis only partly capture human notions of musicality and appropriateness.

A plausible implication is that future MIDI-LLMs will need to integrate symbolic and audio perception modules, expand to richer multimodal annotation schemes, and adopt dynamic, user-adaptive conditioning mechanisms, while maintaining practical deployment profiles (local inference, DAW integration, real-time response).

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Table: MIDI-LLM Model Attributes and Evaluation (abbreviated)

Model	Architecture	Tokenization Schema	Data (SFT)	Inference Engine	FAD (↓)	CLAP (↑)	Real-Time Factor
MIDI-LLM	Llama3.2 w/ vocab expansion	AMT triplet + infill	MidiCaps + LMD	vLLM	0.173	22.1	3–14
text2midi	Encoder-decoder (T5+custom)	REMI+	MidiCaps + Synth.	Vanilla PyTorch	0.818	18.7	0.6–1

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• MIDI-LLM: "MIDI-LLM: Adapting LLMs for Text-to-MIDI Music Generation" (Wu et al., 6 Nov 2025)
• MidiCaps: "MidiCaps: A large-scale MIDI dataset with text captions" (Melechovsky et al., 4 Jun 2024)
• Text2midi: "Text2midi: Generating Symbolic Music from Captions" (Bhandari et al., 21 Dec 2024)
• Evaluating perception: "Evaluating Multimodal LLMs on Core Music Perception Tasks" (Carone et al., 25 Oct 2025)
• GigaMIDI: "The GigaMIDI Dataset with Features for Expressive Music Performance Detection" (Lee et al., 24 Feb 2025)
• Composer's Assistant: "Composer's Assistant: An Interactive Transformer for Multi-Track MIDI Infilling" (Malandro, 2023)
• CLaMP 2: "CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using LLMs" (Wu et al., 17 Oct 2024)
• MIDInfinite/AMT: "Local deployment of large-scale music AI models on commodity hardware" (Zhou et al., 14 Nov 2024)