GGUF conversions of SaruLab Sidon v0.1, a multilingual speech-restoration model that removes noise and reverberation and restores speech bandwidth (16 kHz in → 48 kHz out). Packaged for native, no-PyTorch inference with CrispASR.

The model is the first eight layers of w2v-BERT 2.0 with the Sidon LoRA adapter merged, followed by Sidon's continuous DAC decoder. The SeamlessM4T log-mel window and filter bank are embedded in the GGUF. Port by @KevinAHM (CrispASR PR #283); this repo adds the quantization ladder.

Only the w2v-BERT predictor's large linear weights are quantized; the DAC decoder convolutions and all norms/biases/Snake alpha parameters are kept at their original precision.

Validated by ASR round-trip (Whisper base.en) on the restored output — the metric that matters for a restoration model, since output-waveform correlation understates perceptual quality:

All quants restore fully intelligible speech (WER 0). Q4_K's low waveform correlation means its fine restored detail diverges from F16 even though intelligibility is preserved; prefer Q8_0 or Q6_K when output fidelity to the reference matters, and Q4_K when size is the priority.

The GGUF port was checked against the upstream SaruLab TorchScript modules (feature_extractor_cpu.pt + decoder_cpu.pt) on the JFK clip:

The predictor reproduces the original almost exactly (0.998); the end-to-end drop to 0.945 is the continuous-DAC decoder amplifying F16 weight-rounding through its Snake activations, not a port error — the restored speech is functionally identical (same transcription). The reference intermediates used for this check are published here as sidon-ref.gguf (input_16k, predictor_feats, output_48k) with sidon-ref-48k.wav; regenerate with tools/reference_backends/sidon_ref_dump.py and re-run our side with CRISPASR_SIDON_DUMP_HANDOFF=<path>.

CrispASR auto-detects the sidon architecture from GGUF metadata. The S2S interface processes a complete clip and returns a complete restored clip (not streaming). GPU: --gpu-backend cuda / --gpu-backend vulkan.

Long inputs: the predictor uses O(T²) self-attention (~50 feature frames/sec). Inputs are capped at ~60 s (3000 frames) by default and fail cleanly past that; raise CRISPASR_SIDON_MAX_FRAMES if you have the memory.