---
license: mit
library_name: mlx
pipeline_tag: audio-to-audio
tags:
  - speech-enhancement
  - speech-denoising
  - gtcrn
  - mlx
  - apple-silicon
  - onnx-conversion
---

# GTCRN Simple - MLX

Apple MLX conversion of the GTCRN Simple streaming speech-enhancement model.
This repository contains FP32 safetensors deterministically converted from the
pinned Sherpa-ONNX release asset. It is a layout conversion only: there is no
retraining, quantization, fusion, or weight modification beyond the layout
required by MLX convolutions.

## Files

- `model.safetensors` - 208,427-byte FP32 MLX weight file. Its SHA-256 is
  `d503ed83a35b91f66af97a6a1b07a76425988db7d9744556d0565c336a7e6e3b`.
- `conversion.json` - deterministic conversion record binding the source ONNX,
  audit, converter revision, weight SHA-256, byte count, and 179 tensors.
- `metadata.json` - GTCRN Simple protocol metadata shared with the pinned ONNX
  asset. Its SHA-256 is
  `070f320defc15d4e1781b0c5b9225d9f91031ed5f14f675efa497f244e640615`.
- `LICENSE` - the upstream GTCRN MIT license.

## Provenance

- Upstream implementation and model: [Xiaobin-Rong/gtcrn](https://github.com/Xiaobin-Rong/gtcrn)
  at `9202557caa577baf2ec2220bef82ba9f4b589dc1`.
- Pinned ONNX source: the Sherpa-ONNX `gtcrn_simple.onnx` release asset, SHA-256
  `e77603ac0c23dac3227dd2d7135b3a585cbee2679048aecfa886657d3ae1b534`.
- Conversion: [model-gtcrn](https://github.com/agentable/model-gtcrn)
  `scripts/convert_onnx_to_mlx.py`, using its committed conversion audit.

The converted file preserves ONNX initializer names. ONNX Conv weights become
MLX OHWI; grouped ConvTranspose weights use the audited group-aware layout
transform. GRU, normalization, PReLU, matrix, and structural parameters retain
their source layouts as required by the MLX runner.

## Validation

The MLX runner replays the pinned one-frame GTCRN graph, including all three
recurrent cache families. It is validated against the committed ONNX frame and
cache oracle with maximum absolute error below `5e-5` on Metal FP32 execution.
End-to-end waveform and corpus-quality qualification are not part of this
artifact release; they remain required gates in the consumer library before a
production release.

## Usage

This artifact is consumed by the `engine: mlx` backend in
[model-gtcrn](https://github.com/agentable/model-gtcrn) on Apple Silicon with
the `mlx` build tag. The consumer manifest pins this repository at an immutable
commit and verifies the asset SHA-256 and byte count before MLX allocation.

## License

The upstream GTCRN model is distributed under the MIT license. This derived
format conversion is redistributed under that same license; see `LICENSE`.