Kirexa MDX-Net β€” LiteRT models

.tflite builds of the MDX-Net music source-separation models used on-device by Kirexa (Android), converted from the original .onnx via onnx2torch β†’ litert_torch.

The app uses the fp32 files. They run on the LiteRT GPU delegate, which executes them in fp16 internally (precisionLossAllowed) β€” so we get GPU fp16 speed without baking float16 into the file. (A float16-quantized .tflite carries explicit DEQUANTIZE nodes that shatter GPU graph partitioning and run an order of magnitude slower on-device; fp32 has none.) On devices without a usable GPU the same fp32 file runs on the XNNPACK CPU backend.

Files (use these β€” fp32)

file stem n_fft dim_f dim_t size
uvr_mdx_voc_ft_fp32.tflite vocals 6144 3072 256 66.8 MB
kuielab_a_drums_fp32.tflite drums 4096 2048 512 29.8 MB
kuielab_a_bass_fp32.tflite bass 16384 2048 512 29.8 MB
kuielab_a_other_fp32.tflite other 8192 2048 512 29.8 MB

Input tensor [1, 4, dim_f, dim_t] (NCHW: 2 channels Γ— {real, imag}), float32. The host app computes the STFT, runs the model, and applies the iSTFT.

The *_fp16.tflite files are deprecated (half the size, but the DEQUANTIZE nodes cripple the GPU delegate). Kept only for reference β€” do not use.

Attribution

Converted re-hosts of third-party weights β€” credit to the original authors:

License

MIT, inherited from the upstream MDX-Net / UVR projects. The .tflite conversion adds no new restrictions.

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