Open-Source Architectures for AI-Assisted Music Composition and Production¶

Variant A: DAW‑Native Multi-Plugin Suite (Real-Time, In-DAW Pipeline)¶

Executive Summary: Variant A is a plugin-based architecture fully integrated into a Digital Audio Workstation (DAW). It consists of a suite of VST3/CLAP/AU plugins – each handling a specific stage (structure, composition, vocals, mixing, mastering) – that work together on separate DAW tracks. This design emphasizes real-time editability and seamless fit into existing workflows. Pros: Tight DAW integration allows using familiar interfaces, timeline, and third-party instruments; each plugin can be adjusted in real-time (e.g. regenerate drums on Track 5 without stopping playback). Cons: High complexity in synchronization and resource management (multiple heavy AI models must run concurrently without glitches); more difficult cross-plugin communication; dependent on DAW plugin API constraints. Riskiest Assumptions: 1) That multiple generative models can run in a DAW’s real-time context without breaking audio stream timing – initial validation needed for GPU processing latency and thread safety. 2) That DAW vendors’ SDKs and licenses (e.g. Steinberg VST3) permit distributing an open-source multi-plugin suite – careful review needed to ensure legally sound redistribution of any SDK code or to favor fully open standards like CLAP【10†L27-L33】. Overall, Variant A’s feasibility hinges on managing latency (possibly by pre-rendering sections) and ensuring each plugin remains interactive under heavy ML workloads.

Architecture Overview¶

Variant A implements the music generation pipeline as a chain of specialized plugins loaded on DAW tracks. Each plugin corresponds to a stage of production, enabling “human-in-the-loop” adjustments at every step. The data/condition flow is as follows:

1. Structure Plugin (Arranger): On a control track, an “Arranger” plugin analyzes the user’s prompt/genre tags/guide audio and generates a global song structure: sections (intro, verse, chorus, etc.) and an energy envelope over time. This may use rule-based templates or a trained structure model. The plugin can insert section markers or an automation curve in the DAW representing the intended energy contour for each segment. For example, a high-level transformer or LLM could map a textual prompt to a sequence of sections (e.g. “Start softly, build to an energetic chorus”)【16†L49-L58】【16†L63-L72】. If a guide track is provided, it uses audio segmentation algorithms (e.g. Music Structure Analysis Framework (MSAF), MIT-licensed【12†L170-L177】) to detect existing section boundaries and overall intensity【11†L1-L8】. This gives a starting template that the user can tweak (e.g. move a chorus later). The structure plugin likely works offline via ARA (Audio Random Access) extension, allowing it to read the entire guide track file from the DAW for analysis【10†L5-L13】. (Celemony’s ARA2 SDK is Apache-2.0【10†L13-L18】【26†L293-L301】, ensuring we can integrate this without license conflict.) Risk: No widely-used open model predicts section labels purely from text; we might combine simple AI (e.g. a GPT-4 prompt-to-structure heuristic in early version, later replaced by fine-tuned open LLM on song descriptions) or rely on predefined genre templates.

2. Composition Plugins (Instrument Tracks): For each instrument or stem (drums, bass, harmony, lead, etc.), a dedicated “Composer” plugin generates musical content (MIDI or audio) for its track, respecting the global structure from the Arranger. For example, a Drum plugin generates a drum pattern for verses vs. a more intense pattern for chorus, aligning with downbeats and fills at transitions. Internally, these plugins use symbolic generation models or libraries: e.g. a drum pattern model (like Groove MIDI VAE from Magenta, Apache-2.0【19†L5-L8】, trained on an open drum MIDI dataset) to produce realistic human-like rhythms, and a melody/harmony model for instruments like bass or piano. We will leverage open symbolic models where available – e.g. Pop Music Transformer (Transformer-XL on REMI representation【13†L5-L13】) for melody/harmony generation, retrained on a permissively licensed MIDI corpus. Pre-trained example: Magenta’s Music Transformer (released under Apache-2.0) showed strong long-term MIDI coherence【13†L23-L31】; we can adapt its open implementation to support conditional generation per section (conditioning on “chorus” vs “verse” tokens to vary intensity). Each plugin outputs MIDI notes to the DAW track (or internally to a synthesizer module), rather than raw audio, whenever possible – this allows the user to swap instrument sounds freely and edit notes. For rendering sound, we pair each Composer plugin with either a built-in open-source synthesizer (e.g. Surge synth, GPL-3.0, or SFZ sample player) or simply require the user to insert their preferred VST instrument after the plugin. This design choice keeps our code legally clean (we avoid bundling large sample libraries) and leverages the vast array of existing instrument plugins. For instance, an AI Bass plugin generates a bassline MIDI, and the user can route it to an open soundfont (like a CC0 bass guitar SF2). In cases where audio generation is needed (e.g. a specific drum timbre), the plugin can call an open model (like an audio diffusion model for drum loops) and output audio. We will favor symbolic MIDI output for core parts to maximize editability.

3. Vocal Producer Plugin: On the vocal track, the “Vocal Producer” plugin handles lyrics-to-vocals. It takes the user-provided lyrics (and optionally a melody contour or guide vocal) and produces a sung vocal track. This plugin first generates a vocal melody line if none is given, using either rules (e.g. match melody to chord progressions from other tracks) or an open melody model. One approach is to utilize the lyrics-to-melody alignment capabilities of open singing models like NNSVS (Neural Network Singing Voice Synthesis toolkit, MIT)【9†L13-L20】. We can feed the lyrics and a MIDI melody into an NNSVS-trained model to synthesize the vocal audio. The voice font itself must be from a commercially reusable voice dataset – for example, a voice trained on the CMU Wilderness dataset (folk songs, if public domain) or a custom-recorded CC0 singing corpus. If available, we might use the pretrained voice model from research (e.g. some NNSVS community voicebank under CC-BY). The plugin performs phoneme conversion of lyrics (using an open phonemizer library, MIT) and timing alignment (predict phoneme durations based on the melody and tempo). The core synthesis uses a parametric vocoder like WORLD or MB-iSTFT (both open-source) to generate the sung audio. Post-generation, the Vocal Producer plugin applies vocal cleanup: open-source pitch correction (e.g. a PyTorch implementation of AutoTune or the open-source “Autotalent” algorithm, GPL) to tighten intonation, formant-preserving time stretch to align with the exact tempo (open phase vocoder from RubberBand library, GPL, could be called in offline mode), and adding natural breathing or doubling. If the user recorded a draft vocal, this plugin can instead operate in “assist mode”: it would pitch-correct and timing-correct the user’s vocal using open tools (e.g. pyWORLD for pitch extraction and resynthesis【18†L25-L28】, or CREPE (MIT) for pitch detection). All processing happens within the DAW via ARA or offline rendering inside the plugin, allowing the user to audition changes quickly. Risk: High-quality singing synthesis is still challenging with fully open data. If current open models (e.g. DiffSinger) have non-commercial weights, we plan a retraining on datasets like Opencpop (Chinese singing, CC-BY) and NUS-48E (English singing, likely research license) – careful vetting required. We will mark any voice model whose training data is unclear as “unsafe” and exclude until retrained with CC0/CC-BY recordings.

4. Arrangement & Orchestration Coordination: To ensure all these instrument plugins produce a coherent song, we introduce a shared context mechanism. One plugin (likely the Arranger on the master track) is designated as the Global Controller, broadcasting the structure and chord progression to others. We implement this via the host’s automation parameters or shared memory: e.g. the Arranger plugin can have VST3 parameters encoding “current chord” or “section label”, which other plugins on instrument tracks listen to. Alternatively, we use an OSC (Open Sound Control) bus on localhost to send messages (since OSC is open and many DAWs support plugin <-> external communication). Each Composer plugin receives these cues and generates music accordingly. For example, the Chords plugin generates chord pads following the progression broadcast by the Arranger (the progression itself could be generated via an open chord analysis model or by fitting chords to the melody using music theory rules). Because all components live in the DAW, real-time sync is handled by the host’s timeline – no drift as long as each plugin outputs MIDI/audio aligned to the DAW’s bar/beat grid.

5. Mix Assistant Plugin: Once the stems (instrument tracks and vocals) are generated, a “Mix Assistant” plugin helps with gain staging, panning, EQ, and effects. This plugin, likely on the master bus (or as a control panel affecting all tracks via sidechain inputs), analyzes the stems in real-time or offline. We incorporate known best practices and possibly a trained model: e.g. an open automatic mixing system to set relative volumes. One approach is rule-based: measure each stem’s loudness (RMS/LUFS using an open library like EBU R128 (public domain implementation) and adjust gains to target a balance (drums and vocals at ~ -6 dB, etc.). Another is to use a small neural network trained on a dataset of multitracks with “ideal” mix settings (though open multitrack data with mix annotations is scarce). For EQ, the plugin could use a library of DSP filters (JUCE’s DSP module, ISC/MIT, or DSPFilters library, BSD) to notch out frequency conflicts – for instance, automatically apply a high-pass filter on guitars to leave room for bass. We can integrate algorithms from research: e.g. a masking reduction system that detects when one track’s frequency content masks another’s and lowers the masker via dynamic EQ【22†359-L368】【22†393-L401】. For dynamics, we include an open compressor (like LA-2A clone from RTNeural, MIT) and set thresholds based on target crest factor. These operations can be non-ML (straightforward audio engineering heuristics). The Mix Assistant might also support a **reference track matching mode using Matchering 2.0 (GPL-3.0) – where the user provides a reference song and our plugin matches the overall EQ/loudness profile【22†359-L368】【22†364-L372】. (To avoid GPL contamination in our core, we could spawn Matchering as an external process to analyze and get target stats, since its Python library is GPL【23†1-L4】.)

6. Mastering Plugin: Finally, a “Mastering” plugin on the master bus performs final loudness normalization, multiband compression, stereo widening, and limiting. This ensures the track meets “release-ready” specs (e.g. -14 LUFS integrated loudness for streaming). We can incorporate open algorithms: e.g. the Hyrax brickwall limiter from Matchering (which was open-sourced)【22†401-L409】 to catch peaks, and a light exciter/saturator (DSP by waveshaping). The plugin will measure loudness using standard meters (ITU BS.1770 via libebur128, MIT) and apply gain to reach the target (with user override if needed). The mastering stage could be largely preset-based (a few styles like “transparent” vs “warm analog” chain). This plugin can run in near-real-time (with lookahead of a few ms for the limiter).

Real-Time vs Offline: Variant A strives for real-time operation where possible. MIDI generation can often be real-time (computing a few bars ahead). However, some processes like vocal synthesis or diffusion-based audio generation are too heavy for instant response. In those cases, the plugins will pre-render content when the transport is stopped or during a pre-playback stage. For example, the Vocal plugin might generate the entire vocal line offline (when the user clicks “Generate”), then simply play back the rendered audio during playback. The Arranger plugin could similarly compute structure and even the initial MIDI for all sections upfront when the user presses a “Compose” button, populating the DAW timeline with MIDI clips. The user can then hit play to listen and make edits. So while integrated in DAW, the workflow may involve offline generation steps for heavy tasks, followed by real-time auditioning and minor interactive tweaks (like transposing a melody or regenerating a single section’s drums).

DAW Session Integration: Because each plugin stores its settings (and any generated MIDI/audio data) in the DAW project file, the whole AI session is saved with the DAW session. For example, an Arranger plugin can save the generated structure, seeds, and model settings as plugin state. If using JUCE or Clap, we can implement chunk-based state saving. The user can reopen the project and all plugins recall their last outputs, ensuring reproducibility. We also include seed locking in each generation plugin so that a user can freeze a random seed to recall the same result later – critical for consistent re-renders.

ARA2 for Clip-Level Edits: A standout feature possible in Variant A is deep clip analysis via ARA2. For instance, the Vocal Producer plugin can use ARA2 on the vocal clip to allow manual corrections: the user could open a Melodyne-like editor (provided by our plugin UI) to nudge pitches or timing, and the plugin then re-synthesizes that segment. Since ARA is Apache-2.0 and supported in many DAWs【10†L13-L21】【26†L293-L301】, we can incorporate this for a truly integrated experience where the AI suggestions are editable as if they were recorded audio/MIDI.

Prototype & Feasibility: To validate Variant A, a minimal prototype would include a couple of plugins: e.g. a Drum Generator plugin that takes section labels from an Arranger and outputs a drum MIDI track, and a Mix plugin that auto-adjusts two tracks’ volumes. We would test these in a host like Reaper (which is scriptable and tolerant to experimental plugins). Key metrics to monitor are latency (the time between requesting a generation and hearing it – should be seconds offline or a few beats ahead real-time) and stability (no audio dropouts in DAW). We will likely set a requirement that generation only occurs when transport is stopped or in a background thread that fills a MIDI clip buffer for upcoming bars, to avoid blocking the audio thread.

Integration Notes: We plan to use the CLAP plugin format for its open licensing and modern features (e.g. better parameter and event handling) – CLAP is MIT licensed and suited for open-source development【10†L27-L33】. VST3 SDK (proprietary but gratis) could be used if needed for wider DAW support, wrapped in a way that doesn’t violate redistribution (or the user may have to install the SDK separately). By keeping our code GPL or MIT and dynamically linking to the VST3 SDK (allowed by Steinberg’s VST3 license as long as not static linking), we can remain license-compliant. We will isolate any GPL components (e.g. if we include GPL DSP code like ZynAddSubFX synth) in separate processes or behind clearly separated interfaces, to avoid full contamination of the suite – or simply release the entire plugin suite under GPLv3 to allow mixing GPL components freely (which is acceptable since our goal is open-source distribution).

Variant A Summary: This architecture offers maximal user control and leverages existing DAW capabilities. It is essentially an AI co-producer living inside the DAW. The trade-offs are complexity in development and ensuring that the sum of many modules still produces a high-quality, cohesive song. This approach shines for users who want to guide the AI’s hand at each step and integrate it with their own mixing and instrument choices. However, achieving “one-click” full song generation is less straightforward here; instead, the user is part of the process (which aligns with “human-in-the-loop”). In terms of quality, using symbolic MIDI and high-quality plugins can yield very polished results (since sound comes from pro-grade synths and samples). The structure and arrangement quality will depend on our models’ strength in understanding musical form – something we will continuously improve via training and user feedback (e.g. maybe fine-tune the structure model on a dataset of 10,000 songs annotated with sections and tags, using an open dataset like Harmonix Set if available, or one derived from public-domain songs).

Variant B: Standalone AI Orchestrator (External Engine with DAW Sync)¶

Executive Summary: Variant B centers on a standalone application (“AI Orchestrator”) that handles the entire music generation pipeline outside the DAW, while synchronizing and interoperating with DAWs via standard protocols. It acts as a “virtual producer” that can drive a DAW or function independently to produce stems and MIDI for import. Pros: Decoupling from DAW real-time constraints allows using large models (e.g. 7B param transformers) and lengthy processing without audio dropouts; easier to scale on powerful hardware or cloud; can support multiple DAWs or work headless (useful for batch processing). Cons: Integration is less seamless – requires exporting/importing or using sync protocols; real-time jamming with the AI is limited compared to in-DAW plugins; maintaining project consistency between the orchestrator and DAW can be complex. Riskiest Assumptions: 1) That open sync standards (OSC, JACK, Ableton Link) will provide sufficient integration – we must confirm tight tempo alignment and transport control across applications is feasible with minimal user setup. 2) That the standalone orchestrator can cover all pipeline stages at high quality with open components – essentially we’re assuming we can build a self-contained “AI DAW brain” where licensing of every model and dataset is vetted (for example, using a large text-to-music model like YuE or SongGen without any proprietary pieces). Feasibility of training or fine-tuning such large models on open data is a concern to validate early.

Architecture Overview¶

Variant B is structured as an independent program (could be GUI or CLI tool with a small GUI) that contains the music generation pipeline. It can either drive a connected DAW or operate offline and output files. The key characteristic is that it maintains its own internal timeline and representation of the song, which can be synced or exported to other tools.

System Diagram (Conceptual): The Orchestrator can be visualized as a central engine with inputs (prompt, guides, settings) and outputs (stems audio, MIDI, project files). It interfaces with DAWs through adapters: e.g., a plugin stub in the DAW might act as a bridge (like ReWire used to do) or through network MIDI and audio streaming.

Data/Condition Flow:

1. Input & Analysis: The user provides a prompt (text description, tags, optional lyrics), and optionally reference audio or MIDI. The standalone app first performs analysis if a guide track is given: it will use the same structure/tempo extraction as Variant A (e.g. MSAF for structure【12†L170-L177】, madmom or Essentia for BPM and downbeat). Being outside the DAW, it can handle heavy analysis more freely. If no guide, it may guess an appropriate structure from the prompt with an LLM or a prompt-to-structure model. For example, a prompt “Epic cinematic music with a calm intro” would trigger a programmatic template (intro – build – climax – outro) and an energy curve.

2. Global Planning (LLM Brain): In Variant B, we can afford to use a large-scale model to plan the music. One approach: use an open LLM (like GPT-4 sized, but open variant) that has been fine-tuned on music descriptions to plan structure, instrumentation, and arrangement in natural language or structured text. This LLM could output something like: “Song will be 3min30s at 120 BPM. Sections: Intro 8 bars, Verse 16 bars (energy medium, instruments A, B, ...), Chorus 16 bars (energy high, add instruments X, Y), etc. Chord progression: I–V–vi–IV for verse, etc.” This text plan is then parsed by the orchestrator. The benefit of a standalone is we can include such an LLM (which might be too heavy for a DAW plugin). If using an LLM like LLaMA-2 or GPT-J, we ensure it’s open and fine-tuned only on public data (no proprietary lyrics or such). This stage basically answers: “What should the song contain?” and yields a high-level score or band arrangement.

3. Composition & Generation: The orchestrator now generates content for each part. Unlike Variant A’s per-track plugins, here the generation can be more coordinated. We have two possible paradigms inside the orchestrator:

4. Symbolic-first approach: The orchestrator uses open symbolic models to create MIDI for each part (similar to A, but in one app). For example, use REMI-based transformer to generate multi-track MIDI given the structure and chords. We could leverage research like BandNet (multi-instrument pop generation with coordinated tracks) – open implementations of such exist or can be built by extending Music Transformer to multiple tracks【14†473-L481】【14†545-L553】. The text prompt can influence these via control tokens (like “genre: rock” could bias drum patterns). Once MIDI is generated, the orchestrator has an internal MIDI timeline for all tracks.
5. Audio-first approach: The orchestrator calls a text-to-music audio model to generate raw audio for the whole song or for stems. A promising candidate here is YuE – an Apache-2.0 licensed model that can generate multi-minute songs with vocals from lyrics and metadata【1†308-L316】【4†136-L144】. YuE’s architecture explicitly supports dual-track mode (vocals + accompaniment) and uses a two-stage approach (first generate low-res audio tokens for vocals and accompaniment separately, then refine)【32†259-L268】【32†269-L277】. We can use YuE’s Stage-1 to generate a coherent song with our prompt’s style and lyrics, then use Stage-2 upsampler to get high-quality audio【32†269-L277】. If YuE’s outputs can be separated into stems (they have a TODO for “stemgen mode”【1†363-L370】), we could directly obtain vocal and instrumental tracks. If not, we can run a source separator (Demucs) on the mixed output to split vocals, drums, etc., albeit with some quality loss. Another model in this vein is SongGen (mentioned in YuE’s paper), which is a single-stage transformer that supports generating vocals and accompaniment separately (dual-track mode)【25†37-L45】【25†89-L99】. SongGen’s authors promise to release weights and code【25†47-L52】; if those are under a permissive license, we can incorporate it as well. The advantage of SongGen/YuE is that they learned musical structure and vocals together on large data, potentially yielding more coherent songs than stitching piecewise models. Importantly, YuE is already open under Apache-2.0 and was trained on reportedly in-the-wild data at scale【4†136-L144】. If we use it, we must ensure outputs are safe (the team claims low memorization【4†153-L158】 and that it avoids copying training songs, which is promising). For safety, we can have the orchestrator analyze generated lyrics for inadvertent verbatim lines from known songs via lyric databases, though YuE paper suggests it largely avoids that【4†153-L158】.

In practice, Variant B’s orchestrator might use a hybrid: e.g., use YuE to generate a base audio (especially for vocals, since singing synthesis end-to-end is a strength of YuE【1†308-L316】), then also generate symbolic MIDI for drums and other accompaniment to overlay or guide the audio generation. We could prompt YuE to produce a no-drums version then add our own drum track via MIDI for higher control. Because we have flexibility, we can run multiple models and combine results.

1. Human-in-the-Loop Editing: The standalone app will present an interface (or use a textual prompt system) for the user to intervene. For instance, after initial generation, the user might say “Regenerate the guitar solo in the bridge” or “Make the second chorus twice as long”. The orchestrator can execute these changes: since it has a symbolic representation (notes) or can regenerate a segment’s audio by conditioning on prior output. Models like YuE support in-context continuation and style transfer【1†325-L333】【1†339-L347】, which we leverage for partial re-generations. The app might display a timeline with sections; clicking a section allows regeneration or adjusting intensity via a slider (which could e.g. modify the latent space of the model or simply post-process dynamics).

2. Output & DAW Sync: Once satisfied, the orchestrator provides outputs to the DAW. There are multiple integration modes:

3. Export Stems/MIDI: The simplest: export all stems as WAV files (each instrument/vocal) and MIDI files for each instrument, which the user can import into their DAW session. We ensure these stem files have consistent alignment (starting from t=0 with tempo info or a guide click).
4. DAW Project File Export: Where possible, we can generate a DAW session file. For example, exporting a Reaper .RPP file (which is plain text/XML) with all tracks set up with our stems and tempo map – Reaper’s format is open enough that we could script writing it. For other DAWs, if proprietary, we might not do full project files but at least Standard MIDI files with tempo and markers, and Broadcast WAV files with tempo metadata for easy alignment.
5. Sync Live via ReWire/Link/JACK: For a more interactive link, the orchestrator can act as a tempo master using Ableton Link (GPL for library【27†29-L36】, but we can require the user to allow GPL or run as separate process to avoid linking issues). Link would keep the DAW in sync tempo-wise and bar position. Then we can stream the audio from the orchestrator to the DAW via JACK (JACK provides virtual audio I/O; we launch a JACK client in the orchestrator and route audio to DAW input tracks). JACK’s library is LGPL【28†9-L17】 so linking is fine, though the user must run a JACK server (on macOS/Win this is extra step; we might embed JACK binaries if license allows). Alternatively, use ReaRoute or Soundflower style virtual audio drivers. For MIDI, we can use virtual MIDI ports or send MTC (MIDI Timecode) to keep song position sync. This live sync option would let the user press play in the DAW and have the orchestrator playback its generated stems in lockstep, effectively using the DAW as a mixer.

6. Companion Plugin for Sync: We can also provide a lightweight “Companion” VST/CLAP plugin that the user inserts in their DAW, which communicates with the standalone via local network (e.g. gRPC or custom protocol). This plugin could receive audio/MIDI from the orchestrator and inject it into the DAW. It might also send DAW transport commands to the orchestrator (like if user hits play or moves playhead, inform the orchestrator). This is akin to how ReWire worked (a special plugin that bridged the host and external app). We’d open-source this companion plugin (likely MIT license, minimal code). Using such a plugin avoids requiring JACK setup and can handle multiple channels of audio via the plugin’s audio I/O.

7. Mixing & Mastering in Orchestrator: The orchestrator can also perform an auto-mix and master similar to Variant A’s plugins, but now it has all tracks readily available as digital data. It can apply the same Matchering algorithm or deep learning mixing if we integrate one. The user can choose to accept this mix or export raw stems to mix manually. Because we’re not bound by real-time, we might use more CPU/GPU intensive mixing optimization – e.g. an iterative approach that adjusts EQ to match a target spectrum (some research does this via gradient descent on filter parameters). This would be done offline in seconds.

Licensing & Model Use: Variant B must be 100% open-source and legally clean in code, model weights, and training data because it’s a distributed stand-alone product. We carefully select models: - For text-to-music, as discussed, YuE’s code and weights are Apache-2.0【1†343-L351】, which is excellent. We will still double-check the model card for any non-commercial caveats (it encourages crediting the name in outputs but that’s not a license requirement per se【1†345-L354】). If needed, we might retrain or fine-tune YuE on a known CC dataset (though none is as large as what they likely used). - If SongGen is available, we will verify its license. Since it calls itself “fully open-source”【25†35-L43】 and promises to release data, it likely will be MIT/Apache. However, their data (540k song clips, 2000 hours) – we must ensure either it’s open or they provide the model trained on it under an acceptable license【25†119-L127】. It mentions MusicCaps test set (which is licensed non-commercial)【25†127-L134】, but that’s just evaluation, not training. - Other components like separation (we bundle Demucs for any needed separation. Demucs code is MIT【17†13-L17】, and we use a model like htdemucs which was trained partly on extra data. The weights are released by Meta under MIT license as well【17†21-L25】, so okay. If there’s concern about those extra training data (some internal set), we note it but since separation is an analysis not generating new copyrighted content, it’s low risk). - Using JACK, Link libraries is fine as long as we comply (Link we could avoid linking by not using it or by requiring the user to have Ableton Live or such; JACK LGPL is fine). - Any dataset we use to fine-tune or train must allow commercial: e.g., Free Music Archive (FMA) dataset audio (which Stability used for stable-audio) is a mix of CC licenses, many CC-BY【7†192-L201】. We will filter down to CC-BY or CC0 tracks only. Freesound has many CC0 clips (Stability filtered ~266k CC0 clips【7†215-L223】). Those can cover a variety of sounds for SFX or possibly notes. For structure, if using the Harmonix set or SALAMI dataset annotations, we ensure only use annotations (which are often under CC licenses, but original audio might not be – we avoid audio if not open).

Human-in-Loop UX in Standalone: The orchestrator’s UI or CLI will allow iterative refinement. We’ll implement commands like: “re-generate only drums” – this could simply call the drum generation module with a new random seed while keeping others fixed. Or “shift chorus energy +6%” – which might translate to increasing velocity or adding more layers in that section, or if using diffusion model, maybe increasing an “energy conditioning” vector if available, or simplest: post-process by raising amplitude/compression in chorus. We will maintain the session state (like a project file for the orchestrator) so the user can come back and continue tweaking.

Validation Plan: We will evaluate Variant B by the final audio output’s quality and how well it syncs with DAWs: - Musical coherence: Use metrics like structure accuracy (did the generated audio follow the planned section lengths? We can analyze the audio with our own structure detector and compare to the plan). - Quality: Use CLAP score (open music-text alignment metric) to see if prompt<->audio relevance is high【4†147-L155】 (noting YuE found CLAP alone isn’t perfect【4†147-L155】, but a new metric CLaMP3 correlates better which we might implement if published). - Vocal intelligibility: measure word error rate of lyrics via a speech recognizer【32†315-L324】. We expect to meet some threshold near what YuE reported. - Integration: Test in at least 2 DAWs (e.g. Reaper and Ableton) for sync drift less than e.g. 5ms over 3 minutes, and round-trip export convenience.

In summary, Variant B aims to be a comprehensive AI music creation station that can either output a finished track or feed stems to a traditional DAW. It sacrifices the immediate inline feel of Variant A for the ability to use more powerful models and processes. It’s well suited to an offline workflow: e.g., a user writes a prompt and gets a fully produced track to fine-tune. Legally, this variant can be kept clean by isolating any GPL components (maybe as separate optional modules) and by relying on the open community models that have emerged (YuE, SongGen, Demucs, etc.). It’s essentially future-proofing: if a better open model comes, the orchestrator can swap it in (given microservice-like design internally).

Variant C: Hybrid Web/Local Microservice Platform (Modular & Distributed)¶

Executive Summary: Variant C proposes a hybrid architecture where the music generation pipeline is split into microservices or modules, potentially running in separate processes or machines (local or cloud), coordinated by a lightweight front-end (which could be a web app or a thin client in the DAW). It combines the strengths of local processing for heavy tasks with the flexibility of web technologies for UI and connectivity. Pros: Highly modular – each service (e.g. composition, vocal synthesis, mixing) can be scaled, updated, or even implemented in different languages independently; a web-based interface allows easy updates and remote collaboration; heavy models can run on dedicated servers (or a local GPU server) while the user interface remains responsive. Also, companion DAW plugins can act as control surfaces, sending data to/from these services (enabling integration without heavy computation inside the DAW). Cons: More complex deployment (networking, IPC, possible latency in communication); requires robust handling of service failures or disconnects; offline rendering means the user may have to wait for results (though parallelism can help). There is also the question of data privacy and local vs cloud – we assume here local microservices by default (to keep everything under user control and avoid legal issues of hosting a model with possibly copyright-affecting training data), with an option to connect to cloud for more power if the user opts in. Riskiest Assumptions: 1) That the latency of splitting tasks across services won’t hinder user experience – we need to design efficient data exchange (large audio files between services could be a bottleneck). 2) That we can maintain consistency and state across the microservices (e.g. the structure service and melody service must refer to the same timeline and key signature). This requires a well-defined central state or messaging system, whose design needs validation.

Architecture Overview¶

In Variant C, think of each major function as a microservice with an API. For example: a “Section Planner” service, a “Melody Generator” service, a “Audio Render (diffusion)” service, “Vocal Synthesis” service, etc. These could be Docker containers or just separate programs that communicate via HTTP or a message bus (like RabbitMQ or ZeroMQ for low-latency). On top, a web-based UI (running locally in a browser, or as an Electron app) serves as the primary user interface. Additionally, small DAW companion plugins (VST/CLAP) can be used for synchronization – their job is mainly to pipe tempo/MIDI from the DAW to the services, or trigger generation from within the DAW.

Data/Condition Flow:

1. User Interface: The user opens the AI music web app. They input their prompt, upload any guide audio/MIDI, and enter lyrics if applicable. The web front-end could be similar to a DAW-like interface with a timeline, or a form-based wizard for now. The front-end communicates with a backend coordinator (could be a local Node.js or Python server). This backend orchestrator holds the global state (song structure, current MIDI, etc.) and coordinates calling the microservices in order.

2. Microservice calls: When the user clicks “Generate”, the orchestrator service calls the Structure Service API with the prompt/guide. This Structure service (could be a Python Flask app encapsulating our section prediction model) returns a JSON of sections and intensities. Next, the orchestrator calls the Composition Service – e.g., a melodic composition service which might expose an endpoint POST /generateMelody that takes sections, chords (if any), and returns a MIDI (perhaps in JSON or standard MIDI file). Similarly, a Chord service might provide chords given a prompt or selected genre (or we integrate that into composition). Each service can be independently scaled; for example, if melody generation is heavy, one could deploy it on a GPU box. The key is each module is independently replaceable – if a better drum generator comes along, we swap that container as long as it adheres to the API (this is beneficial for open development and collaboration).

3. Concurrent Generation and Caching: Because we have multiple services, we can generate different parts in parallel. The orchestrator could invoke drum, bass, guitar generation services concurrently once the structure is known, since they are conditionally independent given the structure (unless we design them to depend on each other for coherence; if so, they might iterate or share a random seed to ensure consistent style). To speed up regeneration, results from services can be cached: e.g. if the user changes something small, we avoid regenerating unchanged parts. The backend maintains these caches (maybe in a database or in-memory). We tag each generation with a uuid and seed so the user can reproduce or roll back.

4. Model Servers vs Functions: Some services might host an ML model that stays loaded in memory for multiple requests (to avoid model load time overhead each call). For instance, a Diffusion Audio Server might load the diffusion model weights once and then each request gives it a conditioning (like text prompt plus a reference beat) and it outputs an audio file. This approach is used in e.g. Hugging Face’s inference endpoints. We ensure these model servers are under open licenses (for example, if we use a diffusion model, we choose one trained on open data like Stable Audio Open – though its license is special【7†180-L188】【7†182-L190】, we might instead spin our own diffusion trained on the same CC data if needed). Each such service will have its license documented in the matrix – e.g. a “Text2Audio Service” running a model under the Stability AI OpenRAIL license requires user to accept certain terms【7†182-L190】【7†184-L192】. We likely avoid any service that introduces a non-commercial or highly restrictive license.

5. DAW Integration (Round-Trip): The microservice setup can facilitate DAW round-trip in a controlled way. For example, we have a service for DAW sync that the companion plugin communicates with. Suppose the user is working in Ableton and has an arrangement; they hit a button on the companion plugin “Send to AI”. The plugin collects the tempo map, any existing MIDI or audio from the DAW (maybe the user flagged certain tracks as “guide” or “partial ideas”) and sends that via HTTP to the orchestrator (possibly using a small local WebSocket for continuous updates). The orchestrator can then incorporate that – e.g., it passes the guide audio to the Structure service. After generation, the orchestrator can deliver the new tracks back to the DAW: the plugin could receive a bundle of MIDI clips or audio files (perhaps as URLs or local file paths) and automatically insert them into the DAW project (some DAWs allow this via scripting or a custom extension API). If automation isn’t possible due to DAW limitations, the plugin can at least facilitate by opening a file dialog with the correct file ready. In a more seamless case, if using something like JUCE with Inter-Process Communication (IPC), the standalone can directly drive a headless instance of the DAW or a minimal audio host to place clips (this is advanced and DAW-specific). At minimum, the architecture allows a manual but straightforward round-trip: user clicks “Export to DAW” in web UI, the stems and MIDI are saved in a folder and the DAW plugin pops up a message “New AI stems ready to import” with a sync to timeline.

6. Offline Rendering & Preview: When the user triggers a final render, the orchestrator will gather all parts (MIDI from various services, any raw audio from e.g. a guitar audio generator service) and either assemble them itself (it could call a Mixing/Mastering Service as well) or package them for the DAW. The user can preview the mixed audio in the web UI thanks to the mixing service producing a stereo preview. The web UI can have audio players for each stem too for quick auditioning (leveraging HTML5 audio).

Human-in-the-Loop in Variant C: Because everything is service-based and likely provides APIs, the user (or even an advanced user via scripting) can intervene at any level. For example, after initial generation, the user might open the Piano Roll editor in the web UI for the Melody track (we can integrate an existing open-source web piano roll component). They tweak a few notes. When satisfied, they hit “re-render audio” for that track – this triggers perhaps the Timbre service (if we have one for rendering MIDI to audio, like using a FluidSynth server or DDSP model) to regenerate just that instrument’s audio. This change is then reflected in the mix. Because of modularity, the user could even swap out modules on the fly: say they want a different guitar generator, they could select from a list of available community models (assuming the back-end has them available or can download a Docker image for one).

Technology stack and licensing: We lean on permissive licenses for the infrastructure: - Use Apache-2.0 or MIT licensed frameworks for web and server (e.g. FastAPI (MIT) for Python APIs, React (MIT) for UI). - Communication using standard protocols (HTTP, WebSockets – no licensing issues). - Each microservice will have its own container with clear license for code and model. For instance, a service running PyTorch with an open model – PyTorch is BSD-style license, model code maybe MIT, weights specific license we check. - If any service uses GPL code (e.g. a service that wraps an existing GPL audio tool like Rubber Band for time-stretch), that service can be run as a separate process; the overall system can remain a collective of independent tools (the user essentially is assembling a pipeline of open-source tools, each abiding by its license – this avoids forcing the entire system under GPL, as communication over network/IPC usually doesn’t constitute a single derived work). We’ll still likely publish the orchestrator and glue under GPLv3 or dual-license to be safe, as combining various licenses in a microservice architecture at worst means we must allow GPL combination. - Dataset handling: since this variant might allow plugging in new models on the fly, we will maintain a registry of approved models/datasets with licensing info (this could even be shown in the UI when selecting a model: e.g. “Model X – trained on Y dataset – Licensed CC-BY, outputs © to user”).

Advantages of Microservice Modularity: - Upgradability: If a better model for mastering comes out, we can deploy a new mastering service without touching the rest. Users could opt to use that new service. - Fault Isolation: If, say, the vocal synthesis service crashes or has a memory leak, it doesn’t take down the whole system; the orchestrator can catch that error and inform the user or restart that container. - Scalability: A user with a beefy PC could run all services locally for fastest performance. Another user might offload the heavy model services to cloud instances (we could allow specifying a remote endpoint for, e.g., diffusion generation, possibly an upcoming open API from Stability or others). This means even low-end devices can partake by outsourcing heavy tasks to a server (provided license permits running the model on a server for them – since all is open, it should, but we must ensure things like the StabilityAI license doesn’t forbid hosted commercial use beyond a certain revenue without permission【8†125-L134】 – since we’re not selling a service, just enabling user to point to their own server, we should be fine).

Example Workflow in Variant C: The user opens their browser to localhost:8000 where the app is running. They log in (if multi-user scenario or to access cloud). They create a new project, describing the song. The backend spins up the necessary services (could even dynamically allocate GPUs if available). The user sees a suggested structure and can modify lengths with drag handles. They click generate – they see MIDI notes appear for each section as services fill them in (like watching an AI “type in notes”). After a short wait, audio preview is ready. They listen, decide the bass is too busy. They click “simplify bass” – which calls the Bass service with a parameter to reduce note density (if supported), or regenerates with a different random seed for bass. New bass line comes, they like it. They then hit “Sync to DAW” because they want to do final mixing in Pro Tools. The companion plugin they installed in Pro Tools now receives the stems and inserts them on new tracks. The user can then use their familiar tools for fine mixing, or just bounce as is.

Validation & Metrics: We’ll measure: - Responsiveness: Time from user action to updated result (target interactive latency < 2s for small changes like regenerating one instrument). - Throughput: It should handle full 3.5 minute song generation perhaps in a few minutes. Using parallel services and possibly quantized models (8-bit or ONNX) can help. We can log each service’s time. - Synchronization accuracy: if using companion plugin for live sync, test drifting over long durations. - User satisfaction: Possibly conduct a small user study with music producers using the web UI vs a baseline (like using A or B) to see if modular approach is appreciated or too complex.

Risks and Mitigations: - State synchronization: We will implement a central store (like a Document database or even just in-memory Python object) that holds the current “truth” of the song (sections, tempo, each instrument’s latest MIDI, etc.). All services either are stateless (generate fresh output from input) or update state via orchestrator. This avoids divergence. - Security: If some services are exposed via network (even locally), ensure proper access control (e.g. use token auth or restrict to localhost). - Licensing compliance: Because some services might be separate binaries, we will provide attributions and ensure any network-served code (like web UI including any third-party JS libs) is under compatible license. If we include an online model repository, we will clearly label ones that are e.g. CC-BY-SA (sharealike) so that the user knows derivative model weights must also be open if fine-tuned, etc. The orchestrator glue likely GPL or Apache.

Variant C is somewhat an ecosystem approach: it envisions the AI music system as a set of open components that can be maintained by different contributors (for example, one team improves the vocal service, another focuses on mixing). It also paves the way for a community hub of models – since everything is open, users could drop in their own model (as long as it speaks the expected API). This is akin to how Hugging Face model servers work, but specialized for our pipeline.

In conclusion, Variant C emphasizes flexibility and maintainability, at the cost of extra complexity in deployment. It might be the best path for a project that aims to live on as an open-source platform, inviting collaboration. Early prototyping can be done with all services simply running on one machine, then gradually split out. The result should be a robust system where each stage of music production is encapsulated and replaceable, ensuring longevity and adaptability to new research.

Comparative Analysis of Variants A, B, C¶

To summarize differences and trade-offs:

• Integration and Workflow: Variant A is inside the DAW, providing real-time co-creation. Variant B is outside but DAW-connected, focusing on complete song generation then transfer. Variant C sits in-between, with an independent platform that can feed into DAWs as needed. If a user wants interactive composition using their DAW tools, A is superior. B and C allow working without a DAW at all (B as a standalone app, C as a web service), which might attract non-producers or rapid prototyping needs.
• Real-Time Capability: A is designed for near real-time feedback (except heavy tasks offline), leveraging DAW’s low-latency audio pipeline. B is largely offline (generate then play/import), though live sync can approximate real-time jam, it's not as tight as A. C depends on network/IPC – could achieve semi-real-time but likely a short delay for each request; good for iterative editing but not live performance.
• Model Size & Quality: A has to use lighter models or split tasks to fit plugin constraints (e.g. not using 20 GB RAM in a plugin). B and C can handle huge models by running externally (B in one block, C in microservices). Thus, B and C can more readily employ state-of-the-art large models (like YuE 7B, diffusion models) and potentially yield higher fidelity or more complex compositions out-of-the-box. A might rely more on pre-curated loops or smaller ML models, possibly sacrificing some originality or quality unless the user provides high-quality instrument plugins.
• Editability and Human Control: Variant A excels in fine-grained control (each instrument’s MIDI is accessible, user can tweak in DAW at will). Variant B tends toward one-shot generation (though with iterative regens guided by text). Variant C is also highly editable (it encourages modular adjustments, though through its custom interface or APIs). So A and C are better for “co-writing”, B is more for “type prompt -> get song -> minor fixes”.
• Latency/Throughput: In A, generating a whole song might be slower if done through many plugins sequentially, but user might generate piece by piece. B could generate whole song in one go using a powerful model (faster overall if the model is parallel, but the user waits for entire track). C can parallelize generation of parts (potentially fastest if many compute resources are available). For example, to make a 3.5 min track, A might require user to step through sections, B might call SongGen and wait ~30 seconds to a few minutes for output, C might distribute tasks and finish in under a minute if well-optimized.
• Technical Complexity: A requires deep DAW plugin development and careful real-time programming; B requires training/integrating a giant model and handling sync but is conceptually simpler; C requires building a distributed system with networking, which is the most engineering heavy (though each piece individually simpler). From a team skill perspective: A needs strong C++/DSP/DAW SDK expertise; B needs expertise in ML model training and some C++ for integration; C needs full-stack web, devops, and ML – a broader skill set but maybe each component is decoupled enough to develop in parallel.
• Licensing Risk: A is somewhat safe because it mostly uses local resources and standard plugin frameworks. But one concern is VST3 SDK – Steinberg’s license forbids sharing the SDK source, but we can include binaries. Using CLAP (MIT) or ARA (Apache) mitigates that【10†L13-L21】【10†L27-L33】. A also might bundle or recommend third-party plugins for sound which might not be open (though not our problem if user uses them). B and C have to ensure any model weight used is open for commercial use – e.g. B using YuE (Apache), no issue; but if one accidentally used MusicGen (CC-BY-NC), that would taint it (we will explicitly avoid such “unsafe” weights). C’s microservices need careful curation but also easier to isolate something questionable (you just don’t run that service or replace the model).
• Maintenance and Community: Variant C is most aligned with a community-driven platform (microservices allow community contributions, and web UI can be improved by many). Variant A might appeal to pro audio developers but is less accessible to casual open-source contributors (C++ DSP is niche). Variant B is somewhere in middle – an open app with perhaps some plugin or script for integration; could have community around model training (like making new styles for the generator).
• Latency for human-in-loop changes: In A, changing one note is trivial (just DAW edit). In B, changing one note means you either have to regenerate partially (if model supports) or export MIDI to DAW and edit there (which breaks the link with the generative process). In C, changing one note can be handled by calling one microservice (fast if it’s just re-render MIDI to audio, for example). So A is best for micro-edits, C second, B last.

The table below summarizes key differences:

In essence, Variant A is ideal when tight DAW integration and fine control are top priority (targeting power users who treat the AI as assistive tools within their mixing workflow). Variant B is geared towards an autonomous composition experience – potentially the quickest path to getting a full song, good for users who want result with minimal tweaking or for generating many ideas fast. Variant C is an architecturally future-proof and collaborative-friendly solution, appealing to those who want a highly customizable system and perhaps to integrate this into larger ecosystems or research platforms.

After analyzing A–C, we see areas for improvement or alternative paradigms. Notably, Variant A’s limitation is handling very large models or global coherence (due to fragmentation into plugins), while Variant B and C might over-rely on massive learned models which, while open, might still have hidden biases or limitations, and reduce human control. We propose two additional variants (D and E) to address these:

Variant D: “Symbolic+Differentiable Synthesis” (Fully Interpretable Composition with Learnable Synthesis)¶

Executive Summary: Variant D is a re-imagining of the pipeline focusing on symbolic generation first, followed by differentiable synthesis for realism, all while maintaining interpretability and editability of the composition. It addresses Variant A’s potential quality gap by introducing learning in the sound synthesis, and addresses B/C’s opacity by keeping the composition in a human-readable form (notes, scores). Essentially, Variant D would generate a detailed symbolic score (with melody, harmony, dynamics, expression markings) using AI, then use a set of differentiable synthesizer modules or neural instruments that can be fine-tuned or optimized to match a target sound or style. This approach aims for the ultimate transparency: every note is explicit and adjustable, and the synthesis stage can be inspected and even manually tweaked (like changing an instrument’s timbre parameter). Pros: Maximum editability and human control (since the intermediate representation is a score); potentially higher fidelity than MIDI->soundfont if the neural synths are high-quality and can capture nuances; allows applying music theory constraints easily (we can enforce key, prevent dissonance directly at symbolic level). Cons: Requires development of or integration with high-quality open neural synthesizers for many instruments (which is an active research area, e.g. Google’s DDSP library【14†421-L430】, or multi-instrument VAEs – not all instruments have ready models); the symbolic generator must be quite advanced to cover expressive performance (timing, velocity, articulation) so that the end result doesn’t sound mechanical. Also, achieving the same “wow factor” as end-to-end models might require complex reward or iterative optimization (making sure the final audio is as good as if we had used diffusion). Riskiest Assumptions: 1) That open-source neural audio synthesizers can match the quality of huge diffusion models or recorded samples – we’d need to validate with something like a violin or guitar DDSP model to see if it’s convincing and under what conditions (some may still sound a bit synthetic). 2) That training a robust symbolic generator on permissible data is feasible – we might need to train on a lot of MIDI (ensuring licensing) or use a model like MusicTransformer and hope it generalizes well to various styles without plagiarizing specific melodies (mitigate by training on public domain music and original user-contributed compositions).

Variant D Detailed Concept¶

System Diagram: The pipeline splits into two broad stages: (1) Symbolic Composer and (2) Neural Performance Renderer.

1. Symbolic Composer: This is a sophisticated composition engine that outputs something akin to a musical score with multiple parts, including notes (pitch, duration), structure (sections, repeats), and expression (dynamics markings like forte/piano, legato/staccato indications, pitch bends, etc.). We can implement it as a combination of rule-based frameworks and ML:
2. A high-level planning module (possibly LLM-based or a custom algorithm) decides on structure and motifs. It might even generate a short “lead sheet” (melody + chords) textually which is then realized.
3. A transformer-based music model (like those from Magenta or recent symbolic models) fills in the details. Because we require open data, we train/evaluate on datasets like Lakh MIDI (though not all Lakh is clearly licensed, many are covers), MAESTRO (classical piano, CC0), OLGA or Wikifonia (public domain folk songs), and newly composed pieces by contributors under CC0. If licensing is tricky, we can opt for public domain and folk tunes only; the model might then need style conditioning to produce modern genres – maybe by ingesting chord patterns or rhythms from royalty-free loops or the Ultimate Guitar chords dataset (if we obtain rights or use only those for songs in public domain).
4. The composer can enforce theory: e.g., integrate music21 toolkit (BSD) to analyze the generated score for part-writing errors or out-of-range notes and correct them. Because it’s symbolic, this is feasible.

5. Output is a multi-track MIDI that is rich in expression. Possibly use advanced MIDI 2.0 or MusicXML to capture articulations.

6. Neural Performance Renderer: Instead of routing MIDI to static sample libraries, we use differentiable synths and generative models per instrument:

7. DDSP (Differentiable Digital Signal Processing): Google’s DDSP (Apache-2.0) provides trainable models of musical instruments (like a violin, flute, etc.) by combining oscillators, filters, etc., with neural control【14†421-L430】. We can use pre-trained DDSP models (they released some for violin, trumpet under Apache). These models take as input a conditioning like loudness curve and pitch curve to produce realistic timbre. For example, we feed the violin model the exact notes and loudness from our score’s violin part, and it outputs audio. Because it’s differentiable, we could fine-tune the timbre to match a reference sound if needed (ensuring any fine-tuning data is CC0 or user-provided).
8. Neural Samplers: For drums or other percussive sounds, one could use WaveGAN or diffusion trained on drum one-shots (there are open drum sample packs, many CC0 or created by us). Or use physically-informed models (drum synthesizers like there’s an open source drum synth we can calibrate).
9. Voice: For vocals, rather than end-to-end TTS, we might use a source/filter model. E.g., use a neural source model that generates a vocal excitation given the score’s phonemes and desired style, and a vocal tract model (like VOCODER or WaveRNN variant). Since singing synthesis is complex, maybe for vocals we incorporate a hybrid: generate a rough vocal via an existing open model (like as done in A/B) and then allow the renderer to refine it. Or incorporate an interactive step where a user can sing a rough melody and the system uses voice conversion to match it to the AI composition – a different angle but keeps things interpretable (the user’s own voice).
10. Because these synths are neural and differentiable, we can apply automated mastering while rendering by including mastering effects in the computation graph. For example, include a learnable equalizer whose parameters are optimized to make the output match a target spectrum (which could be part of training or a post adjustment step).
11. Key point: all components here can be open-source and trained on open data (e.g., DDSP violin was trained on solos that might be from public domain pieces performed by pros – likely they used some open content, if not we retrain on something like violin sample libraries that are open).

12. We ensure all learned models (the instrument models) are released under MIT/Apache and weights either trained from public data or from our own multi-sample recordings (one could imagine recording or using existing CC0 multi-sample instrument packs to train these).

13. Human-in-Loop and Interpretability: The user can see the full score that the AI composed, and can edit any note or dynamic marking. Those edits will directly change the rendered audio. This is incredibly transparent: if something sounds off, you pinpoint the note or articulation causing it. It’s akin to having a super advanced MIDI editor that also handles timbre realistically. The user can also swap instrument models easily – e.g. take the same saxophone MIDI and render it with a “clean sax” vs a “breathy sax” model.

14. We can also allow differentiable feedback: for instance, the user says “make it sound more like this reference track” – we can define a loss between our output and the reference on some audio features (spectral, loudness envelope, etc.) and use gradient descent to adjust some global parameters (like overall EQ, reverb amount, or even some model weights if fine-tuning). Because everything from notes to audio is differentiable to some extent (notes discrete though – but maybe we adjust continuous parameters only), we can explore fine-tuning the performance to match style targets without altering the fundamental composition.

15. DAW Integration: This variant can integrate with DAWs in a manner similar to A (as plugins generating MIDI and using something like LV2 plugins for synths) or as its own environment that exports stems. Because the emphasis is on symbolic, one can easily export a standard MIDI file plus an accompanying package of the neural synth models, or simply export audio stems that reflect the final output. For users uninterested in the neural synth detail, it works like a normal export. For those who want to tweak, they could import the MIDI to a DAW and use their own instruments as alternative (the composition remains valid). In fact, this variant yields probably the best sheet music if one wanted to hand to real musicians, since it was explicitly representing everything in that form.

Why Variant D could be better: - No Black Box: Every note is accounted for, addressing trust issues. - Legally safe: The composition being an intermediate means even if a model was trained on some copyrighted MIDI unknowingly, the output can be treated like a generated melody (which is new and not a literal copy, assuming model isn’t overfitting). And our synths would be trained on individual notes or scales, which is not copyrightable (timbres aren’t copyright in outputs). - Quality: By leveraging learned synthesis, we avoid the sometimes stale sound of General MIDI or static samples, hopefully achieving expressive playback (vibrato, etc. learned from data). - Performance: Rendering with DDSP is real-time capable (they are lightweight, as they rely on analytical components) – so this could run live in a plugin even. The heavy part might be large symbolic models, but those can be optimized or run offline to produce the score, then the playback is light. So variant D could even be integrated into A or as a unique plugin that “does everything via symbolic+DDSP”.

Challenges: Need to ensure the symbolic AI doesn’t produce boring or cliche music – large training on varied styles and maybe some creativity rules can help. Also combining multiple instrument neural renderers can lead to mixing issues (each was trained separately, so balancing them is an external step – we’d still have to auto-mix the outputs, but at least each instrument’s track is clean). We can incorporate a mixing optimization as mentioned (slight weights on each track to match loudness targets or reference).

In summary, Variant D offers a highly transparent and controllable AI music creation approach by bridging symbolic AI and differentiable synthesis. It might require more research and development time (and maybe training of new models or fine-tuning open ones), but it aligns with creating a system that musicians can trust and even learn from (since the AI’s “thought process” is visible in the score).

Variant E: “End-to-End Adaptive Transformer with Feedback” (Interactive Prompt-to-Track with Continual Learning)¶

Executive Summary: Variant E explores a radical approach: an end-to-end transformer-based system that generates music in a DAW project format (MIDI + audio stems) directly, and is designed to adapt and improve with continuous user feedback. This concept merges generation and arrangement in one model but introduces a feedback loop where the model can take into account user corrections or preferences and update the track. Think of it as ChatGPT for music, but instead of text, it produces multi-track music, and it can have a conversation with the user about the music (via natural language or via the DAW state). Pros: Potentially the most intuitive to use – a user could say “Make the drums heavier in the second verse” and the model (which has the entire context of the song in its memory) alters the appropriate elements, similar to how one would ask an assistant. It learns from each interaction, potentially personalizing to the user’s style over time. Because it’s end-to-end, coherence can be very high – the same model decides on melody, accompaniment, mix, etc., possibly avoiding fragmentation issues. Cons: This pushes the boundary of current models – a single model that handles such complex structured output is experimental. It would require an enormous training effort with sequences that represent full songs plus stems. Also, real-time interactive updating with a transformer might be slow if the model is large (unless using efficient inference or partial re-generation strategies). Legally, assembling a training set of full multitrack productions that are open-licensed is difficult – so we may need to generate a lot of training data ourselves or use synthetic data (which might not capture full realism). Riskiest Assumptions: 1) That we can encode a music project (including audio) in a sequential representation that a transformer can generate effectively without huge gaps in quality. 2) That we can allow the model to be fine-tuned or updated on user data (for personalization) without violating licenses – any fine-tuning dataset must also be clean.

Variant E Detailed Concept¶

Core Idea: Use a GPT-like model on a representation that includes text (prompts, instructions), symbolic music tokens, and audio tokens all in one sequence. For instance, the model input-output might look like: “[User prompt: ‘Pop song, female vocal, 120bpm, like Adele’][Start tokens] [Section: Intro][Chord: Cmaj][Melody tokens...][Audio tokens for some bars] … [Section: Verse1]… etc.” The model would be trained to predict the sequence of a whole song’s content given an initial prompt.

Architecture: - Possibly a multi-modal transformer: part of it deals with symbolic (like a Music Transformer) and part with audio tokens (like a Jukebox or Encodec tokens generator). OpenAI’s Jukebox attempted something similar (music with lyrics conditioning) but was not interactive【4†219-L227】. Our twist is adding dialog/instructions and using open base models. - We can build on YuE’s approach【32†269-L277】 (which had text tokens (lyrics) + audio tokens with a LLaMA2 base). YuE is already multi-track aware【4†163-L172】 and has shown long-form capability (5 minutes)【3†17-L25】【4†139-L147】. We could fine-tune or modify YuE to also accept user instructions at inference. YuE introduced music in-context learning where you can prompt it with reference audio to influence style【1†337-L344】. We extend that to arbitrary instructions. - Another base could be Meta’s AudioCraft codebase (which included MusicGen) but since MusicGen weights are NC【6†5-L13】, we’d train new weights on open data. However, combining text, midi, and audio in one model likely calls for a custom training pipeline.

Training Data: This is the biggest challenge. Ideally, we have pairs of {instruction, music project} to train supervised. Lacking that, we can use a self-play style: generate some music with our existing pipeline or models, then treat differences between versions as “edits” to learn from. Also, we can incorporate symbolic data by representing it in the sequence (like treat a MIDI file as a series of “notes on” tokens, audio as spectrogram tokens, etc.). Perhaps we break the task: pre-train on symbolic music (like a large MIDI corpus) and on audio generation (like YuE or a smaller stable-audio model with open data), then fine-tune the unified model on smaller dataset that has aligned symbolic and audio (maybe from sources like Guitar Pro files with audio, or multi-track MIDI aligned with audio from CC songs).

Interactive Loop: Once such a model is partially trained, it can be used with reinforcement learning or online learning. The user interacts: “the piano is too loud, lower it.” We have the model generate a new mix (it modifies the amplitude tokens for the piano stem segment). If the user likes it and says “good”, we treat that as a reward and fine-tune the model (or store the interaction to improve future versions). Over time, the model becomes better at understanding commands and making musical adjustments that humans consider improvements – essentially learning a representation of the mixing and arrangement semantics behind user feedback. - This could borrow techniques from RLHF (Reinforcement Learning with Human Feedback) used for language models. Here we’d need a way to quantify “musical preference” for model outputs; human ratings can train a reward model (like how OpenAI uses a reward model for ChatGPT). We can do similar with a smaller model that predicts a rating given two versions of a song and a user instruction whether one is better. - A safe starting point: the model generates something, user corrects by direct manipulation (like actually adjusting a fader in DAW). We can record that as a sample: (context: song before, user action: “increase vocal volume by +3dB”, result: modified audio). Train model to predict similar adjustments when asked.

Deployment: This model could run as a local server or even partly in the cloud due to its size. Possibly it would be accessible through a chat-like interface: e.g. the user opens a chat with their music assistant, uploads any reference and says what they want. The assistant (the model) outputs new audio or MIDI. Because of the large data output, it might provide a download link rather than inline content. This is more freeform than the structured microservices, but for users less technically inclined, simply describing what they want in natural language is powerful.

Quality & Coherence: An end-to-end model could ensure that vocals, accompaniment, mixing are all consistent with each other because it’s basically one generator for all. There won’t be weird mismatches like sometimes occur when separate models for composition and mixing don’t align. However, it might also propagate errors globally – e.g. if it fails at a certain point, the whole output might degrade. We will incorporate some hierarchical strategy – maybe generate in sections then stitch, or generate a low-res plan and refine. Possibly use iterative generation (like generate a draft, then in a second pass as input, the model corrects it – analogous to self-refinement, which large models can do in text and might in music too).

Openness and Licensing: The model weights we produce will be open (license TBD, likely CC BY or Apache). Any training data assembled must be thoroughly vetted: using only CC0/CC-BY audio from FMA/Freesound (like Stability did【7†192-L201】, which gave a smaller music variety but legally safe). For text prompts, we could use public domain lyrics or fake lyrics. Possibly we can include MusicCaps descriptions (since those texts are from a Google dataset – unfortunately MusicCaps audio is YouTube (no redistribute)【18†15-L23】, but the text part might be okay or easily recreated). We can crowdsource prompts and corresponding music from volunteers under CC0 to augment.

Why Variant E could be better: - It’s user-friendly: You don’t need to know about tracks or tech, you just tell it in plain language, akin to instructing a human producer. - It can potentially personalize: Over time it might learn the user’s preferences – e.g. some users might always want a certain style of drum fill, and it could incorporate that by learning from their corrections. - It’s a unified approach that might handle edge cases more gracefully (the model could be aware, for example, if the generated vocals are drowning under guitars and adjust them, all in one system). - It’s very forward-looking: if achieved, it essentially becomes an AI collaborator that you can talk to while making music, which is a holy grail in some sense.

Major challenges: large-scale training resource requirements (we might need to train or at least fine-tune a model with billions of parameters and possibly hundreds of hours of data – not trivial for an open project unless using community resources or smaller distilled models). We could start with moderate scale (like 1B param transformer) and see how it does, perhaps leveraging the fact that YuE already did a lot of heavy lifting for lyrics alignment and such – maybe fine-tune YuE to respond to non-lyric instructions.

Relation to other Variants: Variant E in a way tries to combine everything: like A, it is interactive; like B, it can generate full songs; like C, it’s likely a service or cloud-assisted; like D, it could incorporate user feedback to refine the symbolic aspects (if the user gives explicit musical feedback). It may not use microservices but still can benefit from any open models (e.g. we could embed an Encodec tokenizer for audio in it).

If successful, Variant E would yield a system that’s very easy to redistribute and reuse (just one model and some interface code) and falls under strong copyleft or permissive license along with its training pipeline. All training data decisions will be documented so there’s no ambiguity on legal use.

To conclude the variant designs: we delivered A, B, C in detail and proposed D, E as promising alternatives. Variant D addresses the trade-off between human control and audio quality by uniting symbolic AI with neural synthesis, whereas Variant E pushes towards an AI that can truly collaborate through natural instruction and continuous learning. Depending on project goals and resources, one might even combine approaches (for example, one could implement A or C now for a practical tool, while researching D or E for next-generation releases).

License & Dependency Matrix¶

Below is a matrix listing the key dependencies, models, and datasets across these architectures, alongside their licenses and allowances for commercial use and redistribution. This ensures we only use components that are legally clean for perpetual, open reuse.

This matrix demonstrates our commitment to using only permissive or copyleft licenses that do not restrict redistribution or commercial use, except where copyleft might require us to open our improvements (which we are fine with). No Non-Commercial content will be included in the final shipped product – whenever we encountered NC licenses (MusicGen weights, certain datasets), we have chosen either to exclude them or plan a clean re-training using alternative data.

For each variant implementation, we will include in documentation a bill-of-materials of licenses. For example, if Variant A plugin suite is delivered, its README will list all libraries and their licenses, as well as a folder with license texts of each (to comply with MIT/BSD attribution requirements and GPL if any). Similarly, any model weights distributed will include the license terms (e.g. YuE’s Apache license file, CC-BY credits for training data if required by dataset terms). The share-alike obligations (like CC BY-SA or GPL) mean that if our product includes those, the product itself or at least that component must carry the same license – which is acceptable as we plan to release overall under GPL-3.0 or similar if needed. Specifically, if we use any CC BY-SA music data to train a model, arguably the weights might have to be CC BY-SA (legal area isn’t crystal clear, but we’ll err on the side of caution). We will try to avoid BY-SA in training to keep things simpler (prefer CC0/CC-BY which do not impose copyleft on output). If we do end up with a CC BY-SA trained model (say a model trained on MAESTRO), we’ll mark that model as CC BY-SA and ensure any derivative of it (fine-tunes) stay open.

Lastly, any evaluation datasets we use for metrics (like if we use a test set of songs to compute key accuracy, etc.) will also be open. For example, we might compile a small set of public domain melodies to test if key detection works, or use the MARBLE benchmark mentioned in YuE (if available openly). If an evaluation set is under a research-only license, we’ll avoid it or get permission for at least internal testing, but not distribute it.

Conclusion: Through rigorous license vetting and planning for alternatives (re-train or exclude), we ensure the architectures A–E can be implemented in a completely open-source manner, with no encumbered baggage. Each variant’s composition can thus be redistributed, modified, and used commercially by users of our system, fulfilling the goal of a legally unencumbered, forever-reusable AI music toolset. All our new code will be released under a permissive or copyleft license (we lean towards GPL-3.0 for the overall integration to ensure anyone who uses and modifies it must also share alike – aligning with open culture, but we can use LGPL or Apache for libraries if needed to allow wider use). The models we train will come with Model Cards listing training data and their licensing, so anyone can decide how to use them responsibly or retrain if needed.