Model Card for FullSubNet (IB-Robot)

FullSubNet speech enhancement model with cumulative Laplace normalization for streaming 4-channel microphone array processing, packaged for the IB-Robot framework with three deployments sharing one stream contract.

Deployments

deployment	backend	artifacts	notes
ascend_310p	Ascend ACL (Ascend310P1)	stateful FB/SB OM pair	board-side streaming (LSTM state via state_links)
torch_cuda	PyTorch CUDA	— (ckpt in assets/)	host-side streaming executor
torch_cpu	PyTorch CPU	— (ckpt in assets/)	CPU fallback (watch the 128 ms hop budget)

All deployments share tensor_model/fullsubnet/enhance (observation.audio_4ch [-1,4] -> voice.audio_enhanced_4ch [-1,4]) with a stateful stream execution contract (state_bank_mode: runtime_exclusive, max_open_streams: 1).

Weights provenance

• assets/cum_fullsubnet_best_model_218epochs.tar = Audio-WestlakeU/FullSubNet official v0.2 release checkpoint (sha256 d08d09107eb276b8dc3d2d9fff995f4354a51fa3347125f52f8b9aea7c339f81)
• The 310P stateful OM pair was converted from the same checkpoint
• assets/cum_fullsubnet_best_model_218epochs.manifest.json pins the digest and norm_type: cumulative_laplace_norm (do not mix with offline-norm checkpoints)

Note: the .tar file is the upstream PyTorch serialization container (legacy torch.save format), loaded directly by torch.load — no extraction step.

Repository Structure

• inference_manifest.json — deployment routing (schema v3, stateful stream contract)
• assets/adapter.json — algorithm contract (STFT 512/256, T=2, look-ahead 2)
• assets/cum_fullsubnet_best_model_218epochs.tar — Torch checkpoint
• artifacts/ascend/fullsubnet/*.om — stateful FB/SB OM modules

Usage

Host: voice_asr_service.speech_direction with backend stateful_torch_cuda / stateful_torch_cpu; board: select ascend_310p via the unified inference runtime.