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You can run audio-to-MIDI inference in a browser, but the best-supported examples are for piano—not arbitrary mixtures of instruments. A practical design decodes a user-selected audio file, prepares the model input, runs inference locally, converts its output into note events, and offers a MIDI download. “Runs in the browser” describes where inference happens; it does not by itself prove that the page makes no network requests.
What “polyphonic audio-to-MIDI” can reliably mean
Polyphonic means that multiple notes can sound at once. It does not mean that a model can reliably identify every instrument in a mixed recording. The evidence-backed examples here focus on piano transcription: Google’s Onsets and Frames research addresses polyphonic piano, Magenta.js documents a browser implementation, and Transkun identifies itself as a piano transcription system. These examples support a browser piano-transcription product, not a general-purpose transcription claim.
For a first release, define the supported task narrowly: for example, transcribing piano audio into note events. If the product will accept ensembles, vocals, or arbitrary instrument combinations, treat that as a separate capability to evaluate rather than implying that piano results establish it.
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Model inference is only one part of the user-visible conversion. The application must also decode the input, prepare it in the form expected by the model, interpret predictions, and produce a usable MIDI file. The following is an architecture, not a tested implementation:
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- Accept a local audio file. Make supported formats and file-size or duration limits explicit only after the implementation has been validated against them.
- Decode and prepare the audio. Browser audio APIs provide processing building blocks. Your application must decide how to handle sample-rate conversion, channel mixing, and feature calculation to match the model’s requirements. The Web Audio specification does not prescribe a transcription pipeline. W3C Web Audio API 1.1
- Load the runtime and model. ONNX Runtime Web is one route for running an ONNX model in a browser; Magenta.js documents an OnsetsAndFrames implementation for raw-audio-to-MIDI piano transcription. ONNX Runtime Web quickstart · Magenta.js music documentation
- Run inference and decode predictions. Convert model scores into note events, including the timing and any attributes the model supports. Transkun’s documented approach scores possible time intervals, uses semi-CRF dynamic programming to decode intervals, and predicts attributes such as velocity and refined onset and offset positions. Transkun project repository
- Serialize and offer the MIDI. Turn decoded events into a valid MIDI file and provide a download. Validate file creation and playback in the MIDI software your users are likely to use; the cited model sources do not establish a particular browser MIDI-serialization implementation.
Transkun illustrates how much work can sit between a model’s raw output and a useful transcription. A community ONNX port shows that exporting the system to ONNX is possible, but it is a Node.js command-line tool, not evidence of browser speed or compatibility. Transkun ONNX CLI repository
Choose a runtime with a fallback plan
ONNX Runtime Web documents WebAssembly (WASM), WebGL, WebGPU, and WebNN execution options. Their availability varies by browser, operating system, and provider. In the documented support combinations, WASM is the broadest option, while WebGPU is limited to selected Chromium combinations; the matrix also lists WebGPU as unavailable in Safari and Firefox in those combinations. Check the current matrix against the browsers and devices you intend to support rather than treating these details as permanent.
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Provider support also affects whether a model can run: ONNX Runtime states, “All ONNX operators are supported by WASM but only a subset are currently supported by WebGL, WebGPU and WebNN.” ONNX Runtime Web tutorials
A sound product strategy is to verify the chosen model with WASM first, then add an accelerated provider only for combinations you have validated. Define what happens when acceleration is unavailable or a required operator is unsupported: use the validated fallback, explain the limitation, or stop with a clear error. Do not promise a speedup or a particular device’s performance until you have benchmarked the shipped build.
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- Native Kontrol Standard (NKS) Integration - Akai Professional and Native Instruments have partnered to bring NKS support to the MPK Controller series, get ready to Kontrol straight from your MPK
- Choose Your Exclusive Complimentary NKS Bundle - Browse and control Native Instruments presets and sound libraries; select one of three curated Komplete 15 Select bundles: Beats, Band, or Electronic
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Plan asset delivery and first use
A browser deployment may need to deliver the application’s JavaScript bundle, runtime WebAssembly files, and model files. ONNX Runtime’s deployment guidance calls out these assets and describes conditional imports as a way to reduce the application bundle. Large model downloads can affect first use, but the cited sources do not establish a model size, download time, or cache strategy for this converter. ONNX Runtime Web deployment
- Show download progress or cancellation if the implementation supports them.
- Explain whether model assets are downloaded on first use and whether the browser can reuse cached assets.
- Measure download size, inference time, and memory use on the devices and browsers you plan to support; do not infer those numbers from a model’s research metrics.
- Validate long recordings and memory limits before advertising a maximum duration or file size.
Make precise privacy and offline claims
Local inference can keep the audio on the user’s device for the inference step. It does not prove that the full page makes no network requests: the app may download its code or model, and may send analytics, crash reports, or other requests. ONNX Runtime describes on-device inference and potential offline use, but that is not an audit of a particular application’s network behavior. ONNX Runtime Web tutorials
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- Full Creative Control - A dynamic 37-Key MPK Mini keybed for 3 full octaves of melodic and harmonic performance; Easily connect to your DAW or studio equipment with the USB-powered MIDI Controller
- Advanced Connectivity - Connect to different sound sources with CV/Gate and MIDI I/O; Control modular gear, sound modules, synthesizers, and more to bring new sound sources into your music production
- Native Kontrol Standard (NKS) Integration - Akai Professional and Native Instruments have partnered to bring NKS support to the MPK Controller series, get ready to Kontrol straight from your MPK
- Choose Your Exclusive Complimentary NKS Bundle - Browse and control Native Instruments presets and sound libraries; select one of three curated Komplete 15 Select bundles: Beats, Band, or Electronic
- Record and Compose Without a Computer - Connect to your production station and use the built-in 64-step sequencer featuring one track for drums and one for melodies or chords, with up to 8 notes each
Use wording that matches the product’s actual behavior. Claim “audio never leaves your device” only after checking the entire path, including file handling, telemetry, crash reporting, and third-party resources. If first use requires downloading model assets, distinguish that download from sending the user’s audio for inference. Claim offline operation only for the workflows and assets that have been verified to work without a connection.
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Set expectations with task-specific evidence
What piano metrics do—and do not—show
Transkun’s project model card reports these checkpoint figures on the MAESTRO V3 evaluation set: activation F1 of 0.9530, onset-plus-offset F1 of 0.9349, and onset-plus-offset-plus-velocity F1 of 0.9296. These are project-reported results, not measurements of a browser build and not a guarantee for arbitrary recordings or instrument mixtures. Transkun project repository
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Comparisons also depend on note-duration conventions. Transkun says its shipped checkpoint was trained without extending notes through sustain-pedal durations, and cautions that earlier piano-transcription conventions extend notes through those durations. Its README also says the evaluation module does not currently support multitrack MIDI. A meaningful comparison therefore needs to name the dataset, metric, and pedal convention; one score alone does not establish performance on mixed-instrument audio.
What the browser examples establish
Google’s Onsets and Frames work describes a model that jointly predicts note onsets and frame information for polyphonic piano music, with relative velocity prediction. Magenta.js documents an OnsetsAndFrames implementation for browser audio-to-MIDI use. Together, these are concrete piano-focused precedents for browser transcription, not proof of broad instrument coverage. Google Research: Onsets and Frames · Magenta.js music documentation
Validate the claims before shipping
Test the actual application, not just the model or runtime in isolation. Build a validation set that reflects the product’s declared scope, then check the complete path from file selection to MIDI download. Record results separately for supported browser and provider combinations.
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Quick Recap
- Input behavior: confirm accepted formats, sample-rate handling, channel handling, and any duration or size limits.
- Transcription quality: evaluate onset, offset, and velocity as relevant to the product, using a named dataset and a stated note and pedal convention.
- Runtime behavior: measure load time, inference time, and peak memory on target devices, and verify the fallback path.
- Output quality: confirm the downloaded file opens and represents the decoded events as intended.
- Network behavior: inspect requests during initial load, model download, inference, and error reporting before making privacy or offline claims.
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