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KevinAHM/Sidon-GGUF

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Sidon GGUF

GGUF quantization of SaruLab Sidon v0.1, a multilingual speech-restoration model that removes noise and reverberation and restores speech bandwidth. This repository packages the model for native inference with CrispASR.

The model contains the first eight layers of w2v-BERT 2.0 with the Sidon LoRA adapter merged, followed by Sidon's continuous DAC decoder. The exact SeamlessM4T log-mel window and filter bank are embedded in the GGUF.

Model

File	Precision	Size	Input	Output
`sidon-v0.1-f16.gguf`	FP16 weights, FP32 norms/biases	469.8 MiB	16 kHz mono	48 kHz mono

SHA-256:

text

7E27282A09BE71E2F37E87731A074B291A2CAB0D3F1DE4A8F8EB48421587E912  sidon-v0.1-f16.gguf

CrispASR usage

powershell

crispasr --s2s `
  -m sidon-v0.1-f16.gguf `
  -f input.wav `
  --s2s-output restored-48khz.wav

CrispASR detects the sidon architecture from GGUF metadata. The current S2S interface processes a complete clip and returns a complete restored clip; it is not a streaming interface.

GPU execution can be selected explicitly with --gpu-backend cuda or --gpu-backend vulkan. The Vulkan predictor uses native decompositions for operations that GGML's Vulkan backend does not currently fuse.

Conversion

powershell

python models/convert-sidon-to-gguf.py `
  --base w2v-bert-2.0 `
  --sidon sidon_raw_weight `
  --output sidon-v0.1-f16.gguf `
  --dtype f16

The converter consumes the original `sidon_raw_weight` adapter/decoder and `facebook/w2v-bert-2.0` base weights.

Validation

On a one-second validation clip, native CrispASR output matched a PyTorch reconstruction of the same merged raw checkpoint at waveform cosine similarity 0.999966 for both the FP32 diagnostic conversion and this FP16 GGUF.

Sidon's separately published TorchScript bundle is a slightly different checkpoint revision, so direct comparison to that bundle is not an exact raw-checkpoint parity test.

The complete native pipeline was also tested on an NVIDIA GeForce RTX 5070 Ti with a 9.14-second input:

Backend	Total time	Relative to realtime	Validation
CPU	10.53 s	0.87x	Reference
CUDA	0.32 s	28.2x	Vulkan waveform cosine `0.999490`
Vulkan	0.66–0.74 s	12.3–13.9x	Predictor 701/701 and DAC 704/704 graph nodes native

Vulkan-to-CPU waveform cosine was 0.999559. Backend timings include feature extraction, graph setup, inference, and output transfer; they are hardware- and driver-dependent.

License and attribution

Sidon, w2v-BERT 2.0, and the DAC decoder are released under the MIT license. See the Sidon repository and Sidon paper for authorship and training details.

Contributors

—Wataru Nakata
—Yuki Saito

Acknowledgements

The development of the original model was supported by project gamma of the National Institute of Advanced Industrial Science and Technology.