Card: files/audio/format section, verified join, 212-column reference (COLUMNS.md), parquet config, tNN/pcNN correction

README.md

@@ -4,6 +4,11 @@ task_categories: [text-to-speech, audio-classification]
 language: [en, de, fr, es, it, nl, pl, pt]
 tags: [speech, emotion, voice, trajectory, tts, moss, webdataset]
 size_categories: [100K<n<1M]
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: traj-t80-*.parquet
 ---
 
 # LAION Emotional-Trajectory Speech — tier T≥0.80
@@ -202,13 +207,68 @@ the chain label and inherit exactly this caveat.
 
 This is the **CLEAN** release: `podcast` and `evasnippets` are removed for provenance, and `kartoffelphon` is held pending a licensing review.
 
-## Files
+## Files, audio and formats
 
+**2,402 files at the repository root, in 800 numbered triples plus this card.**
+
+| path | count | what it is |
+|---|--:|---|
+| `traj-t80-NNNNN.tar` | 800 | **the audio**, plus the per-chain JSON and the MOSS tokens |
+| `traj-t80-NNNNN.parquet` | 800 | **the metadata**: one row per chain, 212 columns |
+| `traj-t80-NNNNN.done` | 800 | per-shard verification counts written after the shard was sealed |
+
+`NNNNN` runs `00000`..`00799`. Shard *n* of one kind always describes shard *n* of the other; the
+last shard is short (165 chains, the rest hold 400) — 799 x 400 + 165 = **319,765**.
+
+### Inside a tar — three members per chain, one stem
+
+| member | format | detail |
+|---|---|---|
+| `<stem>.mp3` | **MPEG-1 Layer III, 48 kHz, 96 kbps CBR, mono** | the crossfaded chain, read out of the shipped bytes |
+| `<stem>.json` | JSON | `chain_id`, both general captions, `script`, `segments`, `constant_descriptors`, `speaker_clause`, `tier_max`, `rules`, `dur_s`, `moss_frames`, `n_score_parts` |
+| `<stem>.moss.npy` | `numpy` `uint16`, shape `[T, 12]` | MOSS-Audio-Tokenizer-v2 codes of the **rendered chain**, 12.5 fps, one frame = 12 tokens = 80 ms |
+
+There is no `.vclap.npy` member here and no waveform format other than MP3.
+
+### The join — parquet row to tar member
+
+`shard` is the tar basename **without** the `.tar` extension, and the member stem is `chain_id`
+with `|` replaced by `__`:
+
+```python
+tar_path    = row["shard"] + ".tar"              # "traj-t80-00000"  -> traj-t80-00000.tar
+member_stem = row["chain_id"].replace("|", "__")  # "emolia|DE_x_W0,DE_x_W1,..." -> "emolia__DE_x_W0,DE_x_W1,..."
 ```
-traj-t80-NNNNN.tar        WebDataset: <chain>.mp3, <chain>.json, <chain>.moss.npy
-traj-t80-NNNNN.parquet    one row per chain, all columns below
-traj-t80-NNNNN.done       per-shard verification counts
-```
+
+Verified on `traj-t80-00000`: the parquet's first three `chain_id` values map to the tar's first
+three member groups, member for member.
+
+Member names are long (a chain names all five of its source uids) and exceed the 100-byte POSIX
+tar name field, so the tars use GNU long-name records. Every standard tar reader — Python's
+`tarfile`, GNU `tar`, `webdataset` — handles this; a hand-rolled 512-byte header parser does not,
+and will silently hand you truncated names.
+
+## Columns
+
+**212 columns per row.** The full generated reference, with a one-line meaning for every column, is
+in **[`COLUMNS.md`](COLUMNS.md)**. The groups:
+
+| group | columns | what it is |
+|---|--:|---|
+| identity and provenance | 9 | `chain_id`, `chain_key`, `shard`, `dataset`, `arm`, `uids`, `speaker`, `track`, `k` |
+| language | 4 | `lang_iso` (**first clip only**), `langs`, `lang_mixed`, `n_langs` |
+| audio and rendering | 5 | `dur_s`, `dur_s_soundfile`, `peak_clipped`, `lvl_spread_db`, `fades_ms` |
+| MOSS tokens | 3 | `moss_frames`, `moss_n_vq`, `moss_frames_expected` |
+| text | 3 | `script_text`, `segments` (the per-clip records), `constant_descriptors` |
+| captions | 9 | `caption_general_a`/`_b` and the parts they were built from |
+| emotion | 40 | `emo_*`, Empathic-Insight intensities **of the rendered chain** |
+| voice character | 114 | `vn_<CODE>_reg` + `vn_<CODE>_bucket`, 57 VoiceNet dimensions |
+| quality | 6 | 4 `qual_*` heads, `genuineness_0_6`, `blend_0_10` |
+| vocal bursts | 2 | `n_burst_detected`, `n_burst_scripted` — **two different claims**, see below |
+| trajectory qualification | 17 | `tier_max`, `qmax`, `cmax`, `C`, `rules`, `dim_a`/`dim_b`, the speaker cosines, the scoring-split fields |
+
+**The 40 `emo_*`, 114 `vn_*` and 4 `qual_*` columns describe the RENDERED CHAIN**, scored on the
+crossfaded audio, not averaged from the source clips. Per-clip values are inside `segments`.
 
 ## Loading
 
@@ -240,18 +300,21 @@ for s in info["segments"]:
     print(s["start"], s["burst_note"], s["tag"], s["text"][:60])
 ```
 
-## Two nesting selectors, both shipped
+## Selecting a tier
 
-`tNN` columns (**the default**) select `qmax >= T` under each row's own rule. Nested by
-construction for both arms.
+Every row of this release already satisfies `tier_max >= 0.80` — that is what the tier is. Within
+it, `tier_max` (equivalently `qmax`, equal to it on every row here) is the selector:
 
-`pcNN` columns select the packer's own per-cell population (`T == that tier's own T`).
-For `vprof_vc` these were packed **independently per cell** with a fresh `used` mask, so
-each packing had re-packing freedom a nested subset cannot have. That is why the per-cell
-high-T counts look larger — they are **not subsets of anything**.
+```python
+harder = meta[meta["tier_max"] >= 0.90]
+```
 
-> **`pcNN` must never be summed with, differenced against, or nested inside `tNN`.** They
-> are two different selections of the same underlying chains, not two parts of one.
+**A correction to earlier prose.** Descriptions of this dataset have mentioned `tNN` and `pcNN`
+selector columns. **They are not in this release.** Checked on the first, middle and last shard:
+212 columns each, none of them `tNN` or `pcNN`. Those selectors belong to the upstream chain table
+the tiers were packed from, where the distinction mattered — `tNN` nested by construction, `pcNN`
+a per-cell packing that is *not* a subset of anything and must never be summed with or nested
+inside `tNN`. Downstream of the packer only `tier_max` survives, and it is the nested one.
 
 ## Source datasets and licences