---
license: mit
language: en
tags:
  - speech-enhancement
  - audio
  - speech
  - svod
  - gtcrn
  - real-time
pipeline_tag: audio-to-audio
library_name: safetensors
base_model:
  - Xiaobin-Guang/GTCRN
---

# GTCRN — Grouped Temporal Convolutional Recurrent Network (svod port)

Speech enhancement model ported to run natively in [**svod**](https://github.com/npatsakula/svod) —
a pure-Rust inference stack on top of a JIT-compiled tensor runtime. This repo
holds the converted weights only; the model implementation lives in
`svod-model` (`model/src/gtcrn/`).

Ultralight real-time noise suppression: **48.8 K parameters**, **33.0 MMACs/s**.
Takes a noisy 16 kHz mono waveform, returns an enhanced waveform.

## Source

A faithful Rust port of the upstream
[Xiaobin-Guang/GTCRN](https://github.com/Xiaobin-Guang/GTCRN), checkpoint
`model_trained_on_dns3.tar` (trained on the DNS-Challenge 3 dataset). The
upstream architecture and training are unchanged; this repo republishes the
weights in `safetensors` with the small remaps described below.

## Architecture

```
noisy WAV → STFT(n_fft=512, hop=256, √hann)
         → ERB analysis (257 bins → 129 bands)
         → SFE (subband unfold)
         → Encoder: 2× ConvBlock + 3× GTConvBlock (ShuffleNetV2)
         → 2× DPGRNN (dual-path grouped RNN: intra-frame bidir GRU + inter-frame GRU)
         → Decoder: 3× GTConvBlock (transpose) + 2× ConvBlock (transpose)
         → ERB synthesis (129 → 257)
         → complex ratio mask × input spectrogram
         → ISTFT → enhanced WAV
```

## Usage

```bash
# Build svod-model and run the bundled example (noisy.wav → enhanced.wav):
cargo run -p svod-model --release --example gtcrn_enhance -- \
    --in noisy.wav --out enhanced.wav --hub
```

In Rust:

```rust
use svod_model::gtcrn::{Gtcrn, GtcrnJit};
use svod_model::jit::InputSpec;

let model = Gtcrn::from_hub()?;            // pulls gtcrn.safetensors from this repo
let mut jit = GtcrnJit::new(model);
jit.prepare(InputSpec::f32(&[1, 257, T, 2]))?;  // T = number of STFT frames
// copy the [1, 257, T, 2] complex spectrogram into jit.spec_mut()?, then:
jit.execute()?;
let enhanced = jit.output()?;
```

STFT/ISTFT run eagerly on the host via `realfft`; the network forward pass is
JIT-compiled. See `model/examples/gtcrn_enhance.rs` for the full waveform →
waveform pipeline (it processes long audio in fixed-size frame chunks because
the in-graph GRU recurrence unrolls one IR node per time step).

## Files

| File | Description |
|---|---|
| `gtcrn.safetensors` | Converted model weights (249 tensors, 48.8 K params). |
| `golden.safetensors` | PyTorch reference output for the parity test (a 24-frame slice of `mix.wav`). |

## Conversion notes

Generated by `scripts/convert_gtcrn.py` (run `uv run scripts/convert_gtcrn.py
--selfcheck` to reproduce). Two remaps from the upstream PyTorch checkpoint:

1. **GRU gate order.** PyTorch `nn.GRU` stores gate rows as `[reset, update,
   new]`; svod's `gru()` op expects `[z, r, h]`. The first two hidden-sized
   gate blocks of every GRU weight/bias are swapped.

2. **Bidirectional key split.** The DPGRNN's bidirectional grouped RNNs expose
   `rnn1`/`rnn2` modules whose reverse-direction weights
   (`*_l0_reverse`) are renamed to separate `rnn1_b`/`rnn2_b` keys, matching
   svod's representation of a bidirectional GRU as two unidirectional passes
   (forward over the sequence + forward over the time-flipped sequence,
   concatenated).

`num_batches_tracked` entries are dropped; BatchNorm `running_var` is kept
verbatim (svod folds it into `invstd = 1/√(var+ε)` at load time). The `--selfcheck`
flag verifies the GRU remap against svod's documented recurrence equations.

## Verification

The svod forward matches the upstream PyTorch `GTCRN.forward` to ~6 significant
figures on a fixed-frame slice (`max |Δ|` relative < 1e-3), validated by the
`gtcrn::parity` test in `svod-model`.

## License

MIT — same as the upstream model. Weights © their respective authors; this repo
only republishes them in a converted format for use with svod.