Audiosronnx Gtcrn
ONNX weights for the gtcrn denoise engine in audiosronnx — a mirror of the streaming GTCRN graph from Xiaobin-Rong/gtcrn (MIT).
Modellquelle
- Art
- Other
- Dateigröße
- 523 KB
- Version
- vGtcrn simple
- Aktualisiert
- 12. Okt. 2026
- Quelle
- Hugging Face
Quellenbeschreibung
audiosronnx-gtcrn
ONNX weights for the gtcrn denoise engine in
audiosronnx — a mirror of the streaming
GTCRN graph from Xiaobin-Rong/gtcrn (MIT).
GTCRN (Grouped Temporal Convolutional Recurrent Network) is an ultra-light 16 kHz speech denoiser — 23.7 K parameters, 33 MMACs/s, ~0.5 MB — intended for embedded and on-device use. The graph is stateful and consumes one STFT frame at a time:
mix[1, 257, 1, 2], conv_cache, tra_cache, inter_cache
-> enh[1, 257, 1, 2], conv_cache_out, tra_cache_out, inter_cache_out
The ERB filterbank and subband feature extraction live inside the graph; only a sqrt-Hann STFT/ISTFT runs outside it, in numpy, so inference is onnxruntime-only.
| File | Rate | Size |
|---|---|---|
gtcrn_simple.onnx | 16 kHz | 0.54 MB |
Usage
from audiosronnx import load_denoise
dn = load_denoise("gtcrn")
clean, rate = dn.denoise("noisy.wav") # -> (float32 mono, 16000)
License
MIT, inherited from the upstream GTCRN release.
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 12. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- license: mit library_name: audiosronnx pipeline_tag: audio-to-audio tags: - speech-enhancement - denoising - noise-suppression - on-device - onnx --- # audiosronnx-gtcrn ONNX weights for the **`gtcrn`** denoise engine in [audiosronnx](https://github.com/TigreGotico/audiosronnx) — a mirror of the streaming GTCRN graph from [Xiaobin-Rong/gtcrn](https://github.com/Xiaobin-Rong/gtcrn) (MIT). GTCRN (*Grouped Temporal Convolutional Recurrent Network*) is an ultra-light 16 kHz speech denoiser — **23.7 K parameters, 33 MMACs/s, ~0.5 MB** — intended for embedded and on-device use. The graph is stateful and consumes one STFT frame at a time: ``` mix[1, 257, 1, 2], conv_cache, tra_cache, inter_cache -> enh[1, 257, 1, 2], conv_cache_out, tra_cache_out, inter_cache_out ``` The ERB filterbank and subband feature extraction live inside the graph; only a sqrt-Hann STFT/ISTFT runs outside it, in numpy, so inference is onnxruntime-only. | File | Rate | Size | |------|------|------| | `gtcrn_simple.onnx` | 16 kHz | 0.54 MB | ## Usage ```python from audiosronnx import load_denoise dn = load_denoise("gtcrn") clean, rate = dn.denoise("noisy.wav") # -> (float32 mono, 16000) ``` ## License MIT, inherited from the upstream GTCRN release.
Source context: 0 downloads · 0 likes · Pipeline audio-to-audio · Library audiosronnx · Repo TigreGotico/audiosronnx-gtcrn
