Basic Pitch (Spotify, ICASSP 2022, Apache-2.0) re-authored to a GPU-native LiteRT .tflite — including the conv-based CQT front-end (9 octaves, shared 36×256 kernel banks, lowpass downsample chain, no FFT). Bit-exact torch re-implementation of the official ONNX (corr 1.000000). FP32, 0.84 MB.

Pixel 8a (Tensor G3): 241/241 nodes LITERT_CL (1 partition), ~4.4 ms per 2 s window; note-event F1@0.5 0.98 vs the official model, per-frame argmax agreement 98%.

I/O

Input [1, 43844] float32 — 2 s @ 22 050 Hz mono, [-1, 1] (official window; overlap windows by 7 680 samples and keep center frames when stitching).
Outputs: contour [1,172,264], note [1,172,88], onset [1,172,88] — sigmoid posteriorgrams (~11.6 ms frames; note/onset bins = MIDI 21–108).

Minimal usage

python

import numpy as np, soundfile as sf
from ai_edge_litert.interpreter import Interpreter

wav, _ = sf.read("audio_22050.wav", dtype="float32")   # mono 22.05 kHz
x = np.zeros(43844, np.float32); n = min(len(wav), 43844); x[:n] = wav[:n]
it = Interpreter(model_path="basicpitch.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x[None]); it.invoke()
contour, note, onset = (it.get_tensor(o["index"])[0] for o in
                        sorted(it.get_output_details(), key=lambda o: o["index"]))
active = np.argwhere(note > 0.5)          # (frame, key); midi = key + 21, t = frame * 256/22050

Kotlin (Android, LiteRT CompiledModel GPU)

kotlin

// implementation("com.google.ai.edge.litert:litert:2.1.5")
val model = CompiledModel.create(File(ctx.filesDir, "basicpitch.tflite").absolutePath,
    CompiledModel.Options(Accelerator.GPU), null)
val inBuf = model.createInputBuffers(); val outBuf = model.createOutputBuffers()
inBuf[0].writeFloat(window43844)          // 2 s @ 22.05 kHz mono
model.run(inBuf, outBuf)
val note = outBuf[1].readFloat()          // [172 * 88], frame-major; midi = bin + 21
val onset = outBuf[2].readFloat()         // onset-triggered decoding -> note events

Conversion

Extracted from the official nmp.onnx (102 constants; no TensorFlow). Reflect-pad → anti-diagonal-constant FULLY_CONNECTED; PACK → concat + static slices. Two fp16-on-GPU fixes, both exact: post-log clamp clamp(10·log10(p+1e-10), min=-100) (recovers log(0) from the fp16-flushed floor; desktop no-op) and the per-bin CQT norm folded into per-octave kernel copies (magnitude is linear in kernel scale). FP32 flatbuffer (fp16 weights cost ~0.005 corr on the tiny CQT kernels).

Upstream

spotify/basic-pitch (Apache-2.0). Please cite Bittner et al., A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation (ICASSP 2022).

Performance

Measured on a Pixel 8a (Tensor G3, Android 16) with the standard TFLite benchmark_model tool — 10 warm-up runs then 50 timed runs, reported as the tool's mean.

Runtime	Backend	Graph on GPU	Latency
LiteRT `CompiledModel` (`LITERT_CL`)	GPU	241 / 241	~4.4 ms
TFLite `benchmark_model` (`TfLiteGpuDelegateV2`)	GPU (OpenCL)	241 / 241	did not run

The two GPU rows are different runtimes, not a contradiction. The LITERT_CL figure is the one recorded when this model shipped, taken through LiteRT's own CompiledModel accelerator — the path the Kotlin sample app and the LiteRT API use. The TfLiteGpuDelegateV2 figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the TfLiteGpuDelegateV2 row as a reproducible floor, not as this model's speed on LiteRT.

litert-community/Basic-Pitch-LiteRT

Basic Pitch — LiteRT (CompiledModel GPU) music transcription

I/O

Minimal usage

Kotlin (Android, LiteRT CompiledModel GPU)

Conversion

Upstream

Performance

litert-community/
Basic-Pitch-LiteRT