lna-speech

LNA — Learning Noise Adapters for Incremental Speech Enhancement

Reimplementation of the paper "Learning Noise Adapters for Incremental Speech Enhancement".
Code: annkisluk/speech

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Files

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Architecture

• Backbone: SepFormer (N=256, L=16, 2 DPT blocks × 8 layers, 8 heads, d_ffn=1024)
• Adapters: Noise adapters with bottleneck dim Ĉ=1 (FFL + MHA per transformer layer)
• Parameters: ~25.6M total (~98k per new noise adapter)
• Input: 8 kHz mono waveform

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Usage

import torch
from huggingface_hub import hf_hub_download
import sys, os

# Clone the code repo
# git clone https://github.com/annkisluk/speech && cd speech

from src.models.lna_model import LNAModel

# Download checkpoint
ckpt_path = hf_hub_download(repo_id="Annkisluk/lna-speech",
                            filename="session0_pretrain/lna_pretrained.pt")

# Build model
model = LNAModel(
    n_basis=256, kernel_size=16, num_layers=8, num_blocks=2,
    nhead=8, dim_feedforward=1024, dropout=0.1,
    adapter_bottleneck_dim=1, max_sessions=6
)
model.load_checkpoint(ckpt_path)
model.eval()

# Enhance speech  (noisy: [1, T] tensor at 8 kHz)
noisy = torch.randn(1, 32000)  # 4-second example
with torch.no_grad():
    enhanced = model(noisy, session_id=None)  # session_id=None → base model only

For incremental sessions, load lna_session{N}.pt and add adapters first:

# Load session 4 model (knows all 4 noise domains)
ckpt_path = hf_hub_download(repo_id="Annkisluk/lna-speech",
                            filename="session4_incremental/lna_session4.pt")

model = LNAModel(n_basis=256, kernel_size=16, num_layers=8, num_blocks=2,
                 nhead=8, dim_feedforward=1024, dropout=0.1,
                 adapter_bottleneck_dim=1, max_sessions=6)
for sid in range(1, 5):
    model.add_new_session(session_id=sid, bottleneck_dim=1)
model.load_checkpoint(ckpt_path)
model.eval()

with torch.no_grad():
    enhanced = model(noisy, session_id=2)  # route to session 2 adapter (cough)

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Training Data

SNR range: {−5, 0, 5, 10} dB. Sample rate: 8 kHz. Speech: LibriSpeech train-clean-100.