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.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 470 MB
- Modelo base
- sarulab-speech/sidon-v0.1
- Versión
- vRevision 36d7e46
- Actualizado
- 12 oct 2026
- Descargas
- 38
- Fuente
- Hugging Face
Descripción de la fuente
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:
7E27282A09BE71E2F37E87731A074B291A2CAB0D3F1DE4A8F8EB48421587E912 sidon-v0.1-f16.gguf
CrispASR usage
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
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.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 12 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- license: mit language: - multilingual datasets: - google/fleurs-r - parler-tts/libritts_r_filtered tags: - speech-restoration - speech-enhancement - dereverberation - gguf - crispasr base_model: sarulab-speech/sidon-v0.1 base_model_relation: quantized pipeline_tag: audio-to-audio --- # Sidon GGUF GGUF quantization of [SaruLab Sidon v0.1](https://huggingface.co/sarulab-speech/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](https://github.com/CrispStrobe/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`](https://huggingface.co/sarulab-speech/sidon_raw_weight) adapter/decoder and [`facebook/w2v-bert-2.0`](https://huggingface.co/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 co...
Source context: 38 downloads · 0 likes · Pipeline audio-to-audio · Repo KevinAHM/Sidon-GGUF
Source context: 175 downloads · 0 likes · Pipeline audio-to-audio · Repo KevinAHM/Sidon-GGUF
