This release contains the evidence-selected SFT LoRA adapter and audio projector from the 4.0 native-audio training run. It pairs a frozen openai/whisper-small encoder with Qwen/Qwen2.5-1.5B-Instruct: Whisper hidden states are compressed into 16 continuous prefix tokens, and the language model emits a validated JSON DSP prescription.

The language model does not directly synthesize a clean waveform. A deterministic DSP executor validates and applies its prescription, computes objective metrics against the available clean reference, and returns the original input whenever the candidate fails the safety gate.

Source and reproducibility code: Jatshi/llm-guided-speech-enhancement.

Why the SFT checkpoint is released

All three training stages completed and their artifacts remain auditable. The final checkpoint was not selected merely because it was last:

Stage	Training status	Held-out structural result	Release decision
SFT	1,500 steps completed	1,416/1,416 valid JSON	selected
conservative DPO	800 steps completed	canary output contains invalid numeric placeholders	rejected
GRPO	300 steps completed	0/1,416 valid JSON; mean reward 0	rejected

GRPO reported 1,200 saturated groups and zero mean reward. The 1,416 GRPO failures are preserved as negative evidence; this model card does not claim preference-optimization or RL improvement.

Training data

Clean speech: LibriSpeech train-clean-100 through a documented ModelScope mirror.
Environmental noise: ESC-50.
Physical materialization: 20,000 noisy WAV records, all with clean reference, source provenance, deterministic materialization seed, auxiliary acoustic measurements, SFT target, and synthetic safety-negative preference response.
Split: 16,164 train, 2,420 evaluation, and 1,416 test examples.
Licenses: LibriSpeech is CC BY 4.0; ESC-50 is CC BY-NC 3.0. The materialized dataset is therefore not bundled in this model repository and must be reconstructed from the documented sources.
Preference labels are rule-generated safety negatives, not human preference judgments.

Optimization

Training ran on one NVIDIA RTX 4080 SUPER with 32,760 MiB VRAM using QLoRA/NF4, BF16 compute, gradient accumulation, and DeepSpeed ZeRO-2 at world_size=1.

Stage	Steps	Initial loss	Final loss	Runtime
SFT	1,500	2.314612	0.087327	3,017.39 s
DPO	800	0.406933	0.325083	3,525.89 s
GRPO	300	1.3355e-5	4.6299e-5	9,689.05 s

Single-GPU ZeRO-2 is integration and optimizer-state evidence, not multi-GPU scaling evidence. A ZeRO-3 CPU-offload configuration is included for memory experiments but is not described as single- GPU parallelism.

Held-out evaluation

The release evaluation covers all 1,416 test records. Cached Whisper embeddings were reused and generation was performed in deterministic batches of 16.

Metric	Result
Valid JSON	1,416/1,416
Failed predictions	0
Inference wall time	843.95 s
Throughput	1.6778 samples/s
Amortized mean latency	595.97 ms/sample
p50 / p95 latency	586.20 / 667.53 ms/sample

The executable DSP benchmark reports candidate deltas before rollback:

Objective result	Mean delta
SI-SDR	+0.4509 dB
PESQ	+0.04068
STOI	+0.002198
Safety accept rate	66.74%
Safety rollback rate	33.26%

All four SNR buckets (<5, 5–10, 10–20, and >=20 dB) have positive mean deltas for all three available metrics. DNSMOS, WER, and speaker similarity are not reported because their backends were not configured; missing metrics are not replaced with fabricated values.

Required components

This repository contains:

the SFT PEFT adapter;
audio_projector.pt, which maps Whisper hidden states into the 16-token language prefix;
model-selection, prediction, benchmark, and run manifests.

It does not redistribute Qwen2.5-1.5B-Instruct or Whisper-small. Load those base models from their original repositories and use lse_v2.native_inference.NativeAudioPlanner from the source repo.

Safety behavior

The generated text must parse as one JSON object.
The prescription must contain diagnosis, actions, rationale, and confidence.
Every DSP action and parameter must pass a strict allowlist and numeric bounds.
The proposed waveform is measured against the noisy input and clean reference in this benchmark.
A candidate with negative SI-SDR gain is rolled back to the original waveform.

The 33.26% rollback rate is an observed safety behavior, not a hidden failure count.

Known limitations

Evaluation uses English LibriSpeech speech mixed with ESC-50 noise; other languages, microphones, rooms, codecs, and conversational overlap remain out of distribution.
Mean gains are positive but modest. This is a control-policy project, not a state-of-the-art neural waveform enhancer.
Clean references are available in the benchmark. A production deployment must replace reference- based gating with calibrated no-reference metrics or human review.
DPO and GRPO regressed output syntax in this run. They are research artifacts, not release models.
Rule-generated preferences are too regular to establish human-alignment quality.
The original STFT/ISTFT implementation used inconsistent overlaps; the repaired version is guarded by a waveform round-trip regression test. Metrics in this card use only the repaired evaluation.

Reproducibility

Use configs/native_audio_autodl_32gb.json and follow the 4.0 AutoDL runbook. The release bundle includes machine-readable reports and SHA-256 checksums. Exact package versions are recorded in environment.freeze.txt.

The source code is MIT licensed. Use of the adapter and reconstructed data remains subject to the Qwen, Whisper, LibriSpeech, and ESC-50 terms.

jatshi/LLM-Guided-Speech-Enhancement-v4-SFT

LLM-Guided Speech Enhancement 4.0 — Native-Audio SFT