samples/README.md

# VIPBench reviewer-friendly sample subset

This directory holds a 5-speaker slice of the full release so reviewers can inspect the dataset without downloading the full ~5.8 GB bundle. Bundle size: **~115 MB** (audio + subsetted embeddings).

The format is identical to the full release; analyses that work on `data/` work on `samples/` by changing one path.

## How this sample was created

Selection criterion: **one speaker per sociophonetic group**, gender-balanced as far as possible at 5 cells (3M + 2F). Within each of the 5 sociophonetic groups, the first speaker (by speaker ID order in `data/speakers.csv`) of the chosen target gender was selected. The 5 chosen speakers are listed in the table below; their integer codes (1-5) cover the full 5-group stratification scheme of the full benchmark.

For each chosen speaker, the sample includes:
- Their reference audio clip (1 file per speaker, e.g. `M01R.wav`).
- All 98 comparison clips paired with that speaker as the reference (covers stimulus types 1, 2, 3, and Type 6 morphs anchored on this speaker; Types 4 and 5 different-speaker pairs that *use* one of the 5 speakers as a comparison are also retained because their `reference` field is one of the 5).
- All listener judgments on those 490 pairs (6,401 judgments).

Filtering logic (reproducible from the full release):
1. `samples/speakers.csv` = rows of `data/speakers.csv` where `id ∈ {M01, F06, M11, F16, M21}`.
2. `samples/stimuli.csv` = rows of `data/stimuli.csv` where `reference ∈ {M01, F06, M11, F16, M21}`. Yields 490 rows.
3. `samples/participant_responses.csv` = rows of `data/participant_responses.csv` where `stimuli_id` appears in (2). Yields 6,401 rows.
4. `samples/audio/reference/` and `samples/audio/comparison/` = audio files corresponding to the IDs in (1) and (2).
5. `samples/embeddings/<model>.npz` = same 10 main + 5 layer-bundle embeddings as the full release, but with each `.npz` reduced to the 495 keys (5 references + 490 comparisons) corresponding to the sample. Embedding dimensions are unchanged.

The sample was constructed by deterministic filtering of the released CSVs and embedding files (no re-extraction; `code/run_all_extractions.sh` is not used here). All sampled audio and embeddings are bit-identical to their counterparts in `data/`.

## What's included

| Item | Count |
|---|---|
| Speakers | 5 (M01, F06, M11, F16, M21; one per sociophonetic group, 3M+2F) |
| Reference audio (R.wav) | 5 |
| Comparison audio | 490 (98 per speaker, all stimulus types) |
| Listener judgments | 6,401 |
| Pre-extracted embeddings | 10 models, subsetted to 495 keys each |
| Per-layer SSL embeddings | 5 models, subsetted to 495 keys each |
| Stimulus types covered | All 6 |

## Layout

```
samples/
  README.md                        # this file
  speakers.csv                     # 5 rows (subset of data/speakers.csv)
  stimuli.csv                      # 490 rows (subset of data/stimuli.csv)
  participant_responses.csv        # 6,401 rows (subset of data/participant_responses.csv)
  audio/
    reference/                     # 5 *R.wav (symlinks to ../../../exp_2)
    comparison/                    # 490 *.wav (symlinks to ../../../output)
  embeddings/
    rawnet3.npz, ecapa_tdnn.npz, ... (10 models)
    layers/wav2vec2.npz, ... (5 SSL models)
```

## Quickstart

```python
import numpy as np, pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

stim = pd.read_csv('samples/stimuli.csv')
emb = dict(np.load('samples/embeddings/ecapa_tdnn.npz'))

cos = []
for _, row in stim.iterrows():
    ref, cmp = emb[f'{row.reference}R'], emb[row.id]
    cos.append(cosine_similarity([ref], [cmp])[0][0])
stim['cos_ecapa'] = cos

# P(same) target
stim['p_same'] = stim['same_vote'] / stim['num_response']

# Pearson r
print(stim[['cos_ecapa', 'p_same']].corr())
```

## Speaker subset

| ID | Group | Gender | Age bracket |
|---|---|---|---|
| M01 | 1 (New York City English) | M | 1 (under 45) |
| F06 | 2 (Southern American English) | F | 1 (under 45) |
| M11 | 3 (African American English) | M | 1 (under 45) |
| F16 | 4 (Latino English) | F | 1 (under 45) |
| M21 | 5 (Asian American English) | M | 1 (under 45) |

See `docs/data_dictionary.md` for the full integer-to-group mapping and column schemas.

## License

CC-BY-NC 4.0 for audio + judgments + embeddings; same as the full release. See `../LICENSE`.