The dream is captivating: type a description — “a melancholic piano piece for a rainy day,” “an upbeat 8-bit adventure theme,” “a driving techno track with evolving synth lines” — and have an AI compose a complete, compelling piece of music. While Large Language Models (LLMs) like Google’s Gemini have shown remarkable prowess in generating creative text, code, and even short musical snippets, coaxing them into crafting longer, structured, and musically coherent compositions remains a significant hurdle.

Often, AI-generated music can feel repetitive, lack direction, or devolve into pleasant but aimless noodling, especially when tasked with creating something beyond a few bars. The AI might capture the initial mood but lose the plot halfway through. How do you maintain a sense of development, introduce contrasting sections, and build towards a satisfying conclusion?

This is the challenge we set out to tackle with MidiMaker.pro. It’s not just about generating notes; it’s about generating music with intention, structure, and a sense of progression. Our approach hinges on a core idea: divide and conquer. Instead of asking the LLM to compose an entire symphony in one go, we guide it through the process section by section, building the piece incrementally, much like a human composer might.

The Core Conundrum: AI, Music, and the Long Game

LLMs are fantastic at pattern recognition and text generation. Give them a prompt, and they can produce remarkably relevant output based on the vast amounts of data they’ve been trained on. However, musical composition involves more than just stringing notes together. It requires:

1. High-Level Planning: Understanding musical forms (verse-chorus, AABA), harmonic progressions, and overall emotional arcs.
2. Contextual Awareness: Remembering what happened musically in previous bars or sections and building upon it logically (or deviating purposefully).
3. Precise Timing and Notation: Representing complex rhythms, simultaneous events across multiple instruments, and changes in tempo or key accurately.

Directly asking an LLM for a 3-minute piece often strains its ability to maintain all these elements consistently. That’s where MidiMaker.pro’s strategy comes in.

The MidiMaker.pro Blueprint: Structure, Symbols, and Synthesis

Our solution involves a pipeline designed to guide the LLM while leveraging its creative capabilities. Think of it as providing a structured outline and a specialized language for the AI to work with.

(Conceptual Diagram: Imagine a flow from ‘Idea’ -> ‘Enriched Plan’ -> ‘Sectional Symbolic Generation (Loop)’ -> ‘Concatenated Symbolic Music’ -> ‘Parsed Structured Data’ -> ‘Final MIDI File’.)

Here’s the breakdown:

1. Translating Ideas into a Plan: We start with a simple text description (“a fast, optimistic electronic piece”). We first use the LLM itself to enrich this idea. We ask it to suggest a potential key, tempo, time signature, primary instruments, and even a possible structure (like Intro-Verse-Chorus-Verse-Bridge-Chorus-Outro). This creates a high-level roadmap for the entire composition.
2. A Language for Music: The Compact Symbolic Format: This is crucial. LLMs work best with text. We needed a way to represent musical events — notes, chords, rests, tempo changes, instrument selections, bar lines — as simple text commands. We designed a custom format that is:

• Compact: Easy for the LLM to generate without excessive verbosity.
• Unambiguous: Clear enough for a computer program (our parser) to understand precisely.
• Expressive Enough: Captures the essential musical information needed to create a MIDI file.

This format looks something like this:

• BAR:1 // Start of Bar 1 T:120 // Set Tempo to 120 BPM TS:4/4 // Set Time Signature to 4/4 K:Cmaj // Set Key to C Major INST:Pno // Select the Piano instrument N:Melody:C4:Q:90 // Note: Track 'Melody', Pitch C4, Duration Quarter, Velocity 90 N:Melody:E4:Q:90 INST:Bass // Select the Bass instrument N:Bassline:C2:H:80 // Note: Track 'Bassline', Pitch C2, Duration Half, Velocity 80 BAR:2 // Start of Bar 2 INST:Pno // Switch back to Piano for the next notes C:Chords:[G3,B3,D4]:H:70 // Chord: Track 'Chords', Pitches G3, B3, D4, Duration Half, Velocity 70 R:Melody:H // Rest: Track 'Melody', Duration Half

3. Section by Section Generation: This is the heart of MidiMaker.pro. Instead of one giant prompt, we break the composition down based on the enriched plan (e.g., Intro, Verse 1, Chorus 1). For each section, we send a dedicated prompt to the LLM. This prompt includes:

• The overall plan (mood, key, tempo, etc.).
• Specific goals for this section (e.g., “Generate an 8-bar intro, establishing the main groove,” “Create a 16-bar verse, introducing the main melody,” “Build energy for an 8-bar chorus”).
• The starting bar number for the section.
• Crucially, a summary or context from the previous section (to help maintain flow).
• The strict definition of our Compact Symbolic Format, telling the LLM exactly how to “write” the music.

4. The LLM then generates only the symbolic text for that specific section. We repeat this process for all defined sections.

5. Stitching it Together: The symbolic text blocks for each section are simply concatenated in order, creating one long text file representing the entire piece in our custom format.

6. From Symbols to Sound: Parsing and MIDI Generation: This is where our Python script (music.py) takes over.

• Parsing: The script reads the concatenated symbolic text line by line. It understands commands like INST, T, BAR, N, C, R. It meticulously tracks time, calculating the precise start and end times of each note and rest in seconds, based on the current tempo and time signature. It manages multiple instrument tracks simultaneously, ensuring everything lines up correctly at the bar lines. It maps instrument names (“Pno”, “Gtr”, “Drums”) to General MIDI program numbers and handles the special conventions for drum tracks.
• MIDI Synthesis: Using the excellent pretty_midi library, the parser’s structured output (a list of precisely timed notes, tempo changes, etc.) is converted into a standard MIDI file (.mid). This file contains all the necessary information — notes, timing, instruments, tempo, key, time signature changes — ready to be imported into a Digital Audio Workstation (DAW) like Logic Pro, Ableton Live, FL Studio, or played by any MIDI player.

Features That Make a Difference

This pipeline enables several key features:

• LLM Creativity, Guided Structure: Leverages the LLM’s ability to generate novel musical ideas within a controlled, section-based framework.
• Improved Coherence: Generating section by section significantly improves the chances of maintaining musical focus and logical development over longer durations.
• Flexibility: Easily configure the initial description, the desired section structure and goals, the LLM model (e.g., different Gemini versions), and default musical parameters.
• Standard Output: Creates industry-standard MIDI files, making the output immediately usable for musicians and producers.
• Instrument Handling: Supports a range of instruments and includes mappings for common drum kit sounds.

Navigating the Challenges: Prompt Whispering and Symbolic Strength

Building MidiMaker.pro wasn’t without its hurdles.

• Prompt Engineering is Key: Getting the LLM to consistently follow the symbolic format rules, adhere to the section’s goals, and smoothly transition from the previous section requires careful prompt design. It’s an ongoing process of refinement — learning how to “talk” to the AI effectively. Sometimes the LLM gets creative with the format, requiring robust parsing and error handling.
• Symbolic Format Limitations: Our current format captures the basics, but musical expression is rich and nuanced. We consciously kept it simple for LLM generation and reliable parsing. Adding support for articulations (staccato, legato), gradual dynamics (crescendos), pedal markings, or complex rhythms like tuplets requires careful consideration to avoid making the format too complex for the LLM to handle correctly.
• Ensuring Seamless Transitions: While passing summaries between sections helps, ensuring perfect musical flow isn’t guaranteed. Sometimes the harmonic or melodic jump between AI-generated sections can be abrupt. Improving this context-passing mechanism is a key area for future work.

The Road Ahead: Refining the Dialogue Between Human and AI

MidiMaker.pro is a step towards more sophisticated AI music generation. We’re excited about the potential and are already exploring enhancements:

• Richer Symbolic Language: Gradually adding more expressive elements to the format.
• Smarter Context Passing: Sending more specific musical information (like the last few bars of notation or harmonic context) between section prompts.
• Music Theory Awareness: Experimenting with prompts that incorporate basic music theory rules or constraints, or even adding post-processing steps to analyze and potentially refine the LLM’s output.
• User Interface: Developing a web-based interface (index.html is a placeholder) to make the tool more accessible beyond running a Python script.

Conclusion: A Structured Approach to AI Creativity

MidiMaker.pro demonstrates that by breaking down the complex task of music composition into manageable steps and creating a clear communication channel (our symbolic format), we can guide powerful LLMs to generate longer, more structured musical pieces. It’s not about replacing human composers but exploring new tools for creativity, offering starting points, or generating unique musical textures. The journey of teaching AI to understand not just notes, but the narrative arc of music, is just beginning, and we believe a structured, sectional approach is a promising path forward.