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claroche1/
convfsenet

Name: convfsenet
Availability: InStock

JacobLinCool/VoiceBank-DEMAND-16k

mit

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ConvFSENet

A causal, fully-convolutional speech enhancer trained on VoiceBank-DEMAND-16k. Source: github.com/LarocheC/sparse-nsnet2. See RESULTS_CONVFSENET.md for the full results, architecture description, and the magnitude-compression trick that makes int8 deployment essentially loss-free.

This repo holds two channel-width variants:

variant	location	params	FP32 PESQ	int8 PESQ	Δ (FP32→int8)	int8 RTF	int8 size
192/384 (deployed)	repo root	1.45 M	2.931	2.911	+0.020	0.017	1.6 MiB
128/256 (compact)	`128-256/`	0.67 M	2.891	2.883	+0.008	0.032	0.80 MiB

n_channels_res / n_channels_conv are the only differences — identical recipe (mag-compressed input, 200-epoch PESQ metric-GAN, cosine LR). The 128/256 variant is ~54% fewer params and half the int8 size for ~0.03 PESQ, with int8 PTQ again essentially loss-free (+0.008). PESQ is on the full 824-utterance VoiceBank-DEMAND test split; RTF is the int8 streaming session under onnxruntime CPU (single thread).

Files (per variant)

file	what it is
`g_best`	PyTorch checkpoint (`{"generator": state_dict}`)
`g_best_fp32.onnx`	Streaming FP32 ONNX (per-frame inputs + FIFO state buffers)
`g_best.onnx`	Static int8 ONNX (QDQ, per-channel weights, MinMax calibration; compression prologue kept FP32)
`config.json`	Training config (architecture + STFT params)

The root files are the 192/384 model; the same four files under 128-256/ are the compact model.

Loading

PyTorch (set SUB = "" for the root 192/384 model, or "128-256/" for the compact one):

python

import json, torch
from huggingface_hub import hf_hub_download
from common.env import AttrDict
from convfsenet.model import build_causal_model

REPO, SUB = "claroche1/convfsenet", "128-256/"        # or SUB = "" for the deployed 192/384 model
cfg  = json.load(open(hf_hub_download(REPO, SUB + "config.json")))
ckpt = torch.load(hf_hub_download(REPO, SUB + "g_best"),
                  map_location="cuda", weights_only=False)
model = build_causal_model(AttrDict(cfg)).cuda().eval()
model.load_state_dict(ckpt["generator"])

ONNX (FP32 or int8):

python

import onnxruntime as ort
from huggingface_hub import hf_hub_download

REPO, SUB = "claroche1/convfsenet", "128-256/"        # or SUB = "" for the root model
sess = ort.InferenceSession(
    hf_hub_download(REPO, SUB + "g_best.onnx"),   # or SUB + "g_best_fp32.onnx"
    providers=["CPUExecutionProvider"],
)
# Streaming shape: feed one frame of magnitude STFT (B, n_freq) + the per-block
# FIFO state buffers per call. End-to-end RMS-norm + STFT + frame loop + iSTFT
# pipeline lives in convfsenet/inference_onnx.py in the source repo.

License

MIT. See the source repository for training code and full attribution.