deepsc-s

DeepSC-S — Semantic Communication for Speech (reproduction)

PyTorch reproduction of DeepSC-S (Weng, Qin & Li, 2021 — arXiv:2012.05369), a neural joint source-channel coding system for speech: instead of separate compression + error-correction + modulation, a single network learns to encode speech directly into channel symbols and reconstruct it at the receiver, trained end-to-end through a simulated noisy channel.

• Code: https://github.com/prashantrajbista/semantic-communication
• Write-up (all 5 stages, with results): https://prashantrajbista.github.io/semantic-communication/
• Paper: arXiv:2012.05369
• License: MIT

Checkpoints

Three final-weights checkpoints, each trained with a different simulated channel during training (ChannelLayer in deepscs/channel.py):

All three share the same architecture (DeepSC_S in deepscs/model.py):

• SemanticEncoder / SemanticDecoder: 4 SE-ResNet blocks, feature depth D=32, 5×5 kernels, cardinality 4, SE reduction ratio r=4
• ChannelEncoder / ChannelDecoder: compression knob depth=8 (8 real channel symbols per frame-column position)
• Trained with Adam (lr=1e-3), MSE loss, SNR sampled uniformly in [0, 20] dB per batch

Where the paper underspecifies (kernel size, r, D, Rician K, optimizer), the official TensorFlow repo was used as ground truth instead of guessing — see docs/initial_plan.md in the code repo for the full list of flagged choices.

Training data & scale

Trained on a 2,000-clip subset of the Edinburgh DataShare / VoiceBank-DEMAND clean 28-speaker set (~10k clips available), 40 epochs. This is a deliberate scope choice for a tractable single-session reproduction, not the paper's full-scale run — expect lower absolute quality (PESQ ~1.0–1.35) than the paper reports. SDR/PESQ curves and the matched-vs-mismatched-channel robustness results are in the write-up linked above.

Usage

import torch
from deepscs.model import DeepSC_S  # from github.com/prashantrajbista/semantic-communication

model = DeepSC_S(depth=8, n_blocks=4, cardinality=4, r=4)
model.load_state_dict(torch.load("deepsc_s_awgn_final.pt", map_location="cpu"))
model.eval()

# x: (B, 1, F, L) framed audio — see deepscs/audio.py for the framing/deframing utilities
# and deepscs/channel.py for ChannelLayer to simulate the transmission channel.

Scope

In: neural transceiver, end-to-end MSE training, AWGN + Rayleigh + Rician, SDR (+ PESQ), cross-channel robustness evaluation.

Out: traditional PCM+Turbo/64-QAM baseline, exact numeric paper reproduction, real RF hardware, estimated-CSI channels, full-scale (~10k clip) training.