---
license: cc-by-nc-4.0
language: en
task_categories:
  - audio-classification
  - text-generation
tags:
  - speech-language-models
  - paralinguistic
  - sentence-stress
  - counterfactual
size_categories:
  - 10K<n<100K
---

# Beyond Transcript Alignment — derived data

Derived data + per-seed evaluation results for the
Beyond Transcript Alignment research project (frozen-frozen
speech-to-LLM adapters under counterfactual training on the
StressTest benchmark).

**Code:** <https://github.com/Nurgali-Kadyrbek/frozen-speech-llm-stress>

## Contents

- `cf_pairs/cf_pairs_train.jsonl` — 3666 same-transcript counterfactual
  pairs from Stress-17K-raw probe-train (transcript IDs + stress
  indices; **no raw audio**).
- `cf_pairs/cf_pairs_artifact.jsonl` — 6846 artifact-matched
  negatives (same Φ, different surface features).
- `cf_pairs/cf_pairs_train_shuffled.jsonl` — Stage 4 Control B:
  3666 pairs with audio decorrelated from `(transcript, Φ)` labels.
- `proj_P/proj_P.pt` — Stage 4 Control A: 4096→1024 lstsq-fit linear
  projection (train MSE 0.090, train cos-sim 0.999) used to construct
  the text-only adapter baseline.
- `desc_only_baseline.json` — pinned cross-domain absolute Probe-K
  floor (0.361).
- `eval_results/stage{2,3p8,4,6,6_r4,7}_eval/` — per-seed `summary.json`
  + `rows.json` files for every cohort and pilot in the project.

## Audio access

**No raw audio is included.** Audio for the four datasets used in this
project is accessed via HuggingFace Hub:

- `slprl/StressPresso` (CC-BY-NC-4.0) — n=202 test items
- `slprl/Stress-17K-raw` (CC-BY-NC-4.0) — cf-pairs derive from this
- `ylacombe/expresso` (CC-BY-NC-4.0) — artifact-matched negatives
- `openslr/librispeech_asr` config `clean` (CC-BY-4.0) — domain mix

The cf-pairs JSONL files contain `transcription_id` + `stress_index` +
`speaker_id` + `style` references; users instantiate audio at load
time from the upstream HuggingFace datasets.

## Reproducibility

```python
from huggingface_hub import snapshot_download
import json

# Download
local = snapshot_download("nur-dev/stress17k-counterfactual-pairs", repo_type="dataset")

# Reproduce R1.8 cohort Probe-G_neutral (= 0.5122 ± 0.0039 across 5 seeds)
import numpy as np
seeds = [1234, 1235, 1236, 1237, 1238]
vals = [json.load(open(f"{local}/eval_results/stage3p8_eval/seed{s}/summary.json"))
            ["adapter"]["accuracy_neutral"] for s in seeds]
print(f"R1.8 Probe-G_neutral cohort: {np.mean(vals):.4f} ± {np.std(vals):.4f}")
```

## License

CC-BY-NC-4.0 (inherited from Stress-17K-raw upstream). Permitted for
academic research, ablation studies, reproducibility checks, pedagogy.
Commercial use is not permitted under upstream dataset licenses.

## Citation

```bibtex
@software{kadyrbek_frozen_stress_llm,
  author  = {Kadyrbek, Nurgali},
  title   = {frozen-speech-llm-stress: research code for frozen-frozen speech-to-{LLM} adapters under counterfactual training},
  year    = {2026},
  url     = {https://github.com/Nurgali-Kadyrbek/frozen-speech-llm-stress}
}
```