LSE Prescription-MM-DiT v5

This release is a three-component, safety-gated speech-enhancement research system:

1. a 51.7M-parameter Prescription-MM-DiT trained as an observed-source residual rectified flow;
2. a Qwen2.5-1.5B LoRA planner that emits strict JSON actions and explanations;
3. an ExtraTrees acoustic router calibrated on the validation split and restricted to executable white/pink-noise actions.

The matching source is available at Jatshi/llm-guided-speech-enhancement, tag v5.0.0.

Package layout

Only load the PyTorch checkpoint and joblib router from a trusted source: both serialization formats can execute code during deserialization. Verify hashes before use.

Training and data

• Public source: LibriSpeech dev-clean.
• Conditions: clean, white noise, pink noise, reverberation, and telephone-band filtering.
• Split: 1,600 train / 200 validation / 200 test, with 25 / 4 / 4 disjoint speakers.
• Test labels were not used to fit MM-DiT, LoRA, or ExtraTrees.
• MM-DiT: 10,000 steps, best validation loss 0.0735645, 1,803.97 seconds on one RTX 4080 SUPER 32 GB.
• Planner SFT: 300 steps; 200/200 test generations were strict-valid JSON.
• Confidence threshold: 0.70, selected on validation only.

Held-out evaluation

The final test ran 200 examples across six arms (1,200 rows). The safety-gated predicted arm reports:

DeepFilterNet3 achieved +0.374 dB mean SI-SDR improvement on the non-clean subset, compared with +0.142 dB for this system. The v5 claim is therefore safe selective enhancement, not universal or state-of-the-art denoising. Aggregate DeepFilterNet SI-SDRi is distorted by the clean condition's near-degenerate reference SI-SDR and should not be used as the sole comparison.

Inference

Download the model package into the source repository and run:

python -m lse_v2.mmdit.infer \
  --checkpoint mmdit/checkpoint-best.pt \
  --input noisy.wav \
  --prescription examples/mmdit_prescription.json \
  --output enhanced.wav

To reproduce the final hybrid planner and full six-arm benchmark, follow docs/MMDIT_AUTODL_RUNBOOK.md.

Limitations

• The trained enhancement action currently supports white/pink noise; clean, reverb, telephone, and low-confidence cases are intentionally rejected or returned unchanged.
• The pure LLM diagnosis accuracy is 29.5%; the published 79% value belongs to the ExtraTrees acoustic router, not the LLM.
• The test set is small and English-only. No claim is made for multilingual, overlapping-speaker, far-field, or real production traffic.
• The benchmark's reference-based rollback is an evaluation tool. Production use requires a calibrated no-reference quality/safety estimator.
• The package does not redistribute Qwen base weights or LibriSpeech audio.

Integrity

Key SHA-256 values:

d8e1677d15b3ad6b53b87a1a08c3b95c4dc1c028dfa237267646d5d7b8306c21  evaluation_report.json
777a4ca76efe2d00ad66a74e5cf198c018f7639951b93c04630a6c6bed51b27f  extra_trees_safe.joblib
868bf0918a649f3e7b0feb7638dfface83dd2abcbd5274510823db4cb43832e7  planner adapter_model.safetensors

See the packaged SHA256SUMS for the MM-DiT checkpoint and every uploaded release file.