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ABStemPlayer

ABStemPlayer is a Avalonia 12 audio player application built with .NET, designed for real‑time media processing, modern UI rendering, and cross‑platform deployment. The project focuses on predictable performance, clean architecture, and production‑grade engineering practices.

ABStemPlayer-UI

Features

  • Stem splitting and mixing
    • Drums
    • Bass
    • Other
    • Vocals
    • Guitar
    • Piano
  • Turn each stem on and off independently
  • Pan stems left or right
  • Audio player with A/B looping and speed control

Techy stuff:

  • .NET 10
  • Cross‑platform UI built with Avalonia 12
  • ONNX Runtime integration for ML inference
  • htdemucs_6s.onnx for stem splitting

YOU HAVE TO DOWNLOAD htdemucs_6s.onnx YOURSELF DUE TO GITHUB SIZE LIMITATIONS https://huggingface.co/StemSplitio/htdemucs-6s-onnx/blob/main/htdemucs_6s.onnx

Requirements

  • .NET 10 or later
  • Avalonia 12
  • ONNX Runtime (CPU or GPU)
  • FFmpeg

HTDemucs 6‑Stem ONNX Model Guide

Overview

HTDemucs‑6s is a state‑of‑the‑art music source separation model capable of splitting a stereo mix into six distinct stems:

  • Drums
  • Bass
  • Vocals
  • Guitar
  • Piano
  • Other

The model is designed for high‑fidelity separation, low artifacts, and robust performance on modern CPU inference engines.

Required Model File

File Name: htdemucs_6s.onnx
Placement: Place the model inside your application's data directory: /Data/htdemucs_6s.onnx

Why this model

This ONNX export is specifically built for:

  • CPU inference
  • float32 audio processing
  • 6‑stem output
  • 44.1 kHz stereo input

Using any other Demucs variant (4‑stem, hybrid, GPU‑optimized, etc.) will result in incompatible tensor shapes.

Model Input Specification

Input Tensor Name

mix

Input Shape

[1, 2, N]

Where:

  • 1 = batch size
  • 2 = stereo channels
  • N = number of audio samples in the segment

Audio Requirements

  • Sample rate: 44,100 Hz
  • Channels: Stereo
  • Format: float32 PCM
  • Normalization: Standard waveform scaling

Model Output Specification

Output Tensor Name

stems

Output Shape

[1, 6, 2, N]

Where:

  • 1 = batch
  • 6 = stems
  • 2 = stereo
  • N = same segment length as input

Each stem is returned as a stereo float32 waveform.

Technical Details

HTDemucs‑6s is based on the Hybrid Demucs architecture, combining:

  • Convolutional encoder/decoder
  • Multi‑band processing
  • Transformer blocks for long‑range context
  • Overlap‑add reconstruction
  • Six‑head output layer

This design allows the model to preserve transients, maintain stereo imaging, and reduce musical bleed between stems.

Supported ONNX Operations

The model uses only standard ONNX ops, including:

  • Conv / ConvTranspose
  • LayerNorm / GroupNorm
  • Multi‑Head Attention
  • GELU / ReLU
  • Reshape / Transpose
  • Basic arithmetic ops

This ensures full compatibility with CPU execution providers.

Downloading the Correct Model

Make sure you download the HTDemucs 6‑stem ONNX export, not:

  • 4‑stem Demucs
  • Hybrid Demucs v3/v4 PyTorch checkpoints
  • GPU‑optimized ONNX models
  • Models with different sample rates

If you need a verified download link, choose:

Verification

To ensure you have the correct model, check:

Property Expected
File name htdemucs_6s.onnx
Input tensor mix
Input shape [1, 2, N]
Output tensor stems
Output shape [1, 6, 2, N]
Sample rate 44.1 kHz
Channels Stereo
Stems 6
Execution CPU

SHA256: 48F8E84945579F8AB340E083339E9221E03785DBE733A52C388200B6D3CA779A

Copyright notices

Icons

COLLECTION: Gentlecons Interface Icons
LICENSE: CC Attribution License
AUTHOR: Konstantin Filatov