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README.md

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+---
+license: cc-by-4.0
+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]
+---
+
+# LAION Emotional-Trajectory Speech — tier T≥0.80
+
+**319,765 crossfaded speech trajectories · 4,482 audio-hours · 1,598,825 source clips**
+
+A *trajectory* is a short sequence of 5 consecutive utterances **by one
+speaker** whose measured emotion or voice character moves monotonically from one end of
+the corpus distribution to the other. The clips are joined into one continuous audio file
+with equal-power crossfades, the joined audio is **re-tokenized** with MOSS-Audio-
+Tokenizer-v2, and every chain carries a two-level caption.
+
+This is the **T≥0.80** rung: every chain crosses at least **80 % of the corpus range**
+on its named dimension while never stepping more than 25 % between adjacent clips.
+
+## Why a trajectory dataset
+
+Most expressive-TTS corpora label a clip with *one* emotion. That teaches a model to hit
+a target but not to *travel* — to start guarded and end furious, to slide from amusement
+into contempt. These chains are examples of the travel itself, with the change measured
+rather than asserted.
+
+## The tier ladder — strictly nested
+
+| tier | chains | hours | en % | de % | other % |
+|---|--:|--:|--:|--:|--:|
+| T≥0.20 | 4,381,192 | 44,999 | 62.8 | 21.4 | 15.8 |
+| T≥0.25 | 2,881,179 | 33,590 | 62.2 | 26.5 | 11.3 |
+| T≥0.40 | 1,594,365 | 19,603 | 61.9 | 34.2 | 3.9 |
+| T≥0.50 | 1,007,755 | 13,371 | 61.0 | 37.8 | 1.2 |
+| T≥0.60 | 783,747 | 10,423 | 60.6 | 39.2 | 0.3 |
+| T≥0.70 | 615,517 | 8,194 | 59.7 | 40.3 | 0.0 |
+| T≥0.80 **(this release)** | 319,765 | 4,531 | 59.6 | 40.4 | 0.0 |
+
+Higher T is a **strict subset** of every lower T (verified by set containment, all four
+rules × all seven rungs). So you can train on T≥0.20 and evaluate on T≥0.80 knowing the
+harder set is contained in the easier one, and a curriculum needs no re-download.
+
+## How a chain qualifies
+
+Scores are mapped to a **tie-aware mid-rank ECDF** over all 132,833,726 annotated
+utterances, so "moved 0.25" means "crossed 25 % of the whole corpus" identically on every
+dimension. Writing `u_e(i)` for that percentile of dimension `e` at clip `i`, with
+`A = argmax_e u_e(first)` and `B = argmax_e u_e(last)`:
+
+| rule | family | requires |
+|---|---|---|
+| `B1` | `emotion` | one-sided: `\|Δu_B\| ≥ T`, per-step ≤ C on axis B |
+| `AB2` | `emotion_twosided` | both named axes move by ≥ T, per-step ≤ C on both |
+| `PXR` | `proxy_spearman` | as AB2, but an endpoint that cannot ramp certifies smoothness on a correlated proxy axis |
+| `VN1` | `voicenet` | one of 57 VoiceNet voice descriptors sweeps by ≥ T |
+
+`C = 0.25` throughout. A chain may satisfy several rules; the `rules` column lists all of
+them and the row appears **once**.
+
+> **PXR caveat.** Only the *Spearman* proxy family is present in the source chain table.
+> A tail-lift proxy family exists upstream (it covers 1,553 of 1,560 ordered emotion pairs
+> against Spearman's 1,475) but has no rows here, so every `PXR` chain in this release is
+> Spearman-derived and labelled `proxy_map = "spearman"`.
+
+## The speaker filter — strict, and no voice conversion
+
+Mined chains must satisfy **both** conditions on `Orange/Speaker-wavLM` cosines:
+
+```
+min_cos_consec  >= 0.80     every adjacent pair
+min_cos_anchor  >= 0.80     every clip against the first
+```
+
+Neighbour-only similarity does not chain — A can resemble B and B resemble C while A and
+C are plainly different people — so the anchored condition is what catches drift, and
+requiring both is the strict reading. **Chains with no measurement are dropped, not
+kept.** No voice conversion is applied anywhere: a chain is one real speaker or it is not
+in the dataset.
+
+`vprof_vc` chains are exempt from this filter **and only from this filter**: one voice
+profile is one cloned voice by construction, so there is no identity to verify. They
+carry `cos_source = "vprof-cloned-voice"`.
+
+## Rendering
+
+* Every clip levelled to **exactly −20.0 dBFS RMS** before joining. MOSS's own loudness
+  normaliser targets −20 dBFS but clamps gain to ±3 dB, and 59 % of corpus clips hit that
+  clamp; pre-scaling makes its gain zero, so the seam step is zero *by construction*.
+* **150 ms equal-power (cos/sin) crossfade** at every join, shortened to 100 ms on a hot
+  onset or tail, never more than 25 % of either segment. A linear crossfade sums to less
+  than unit energy at its midpoint and dips audibly.
+* Peak guard applied **once** to the finished chain.
+* 48 kHz mono, MP3 96 kbps CBR.
+
+## Re-tokenization — and why it was necessary
+
+The crossfaded concatenation is **new audio**. The constituent clips' existing MOSS codes
+describe the clips, not the chain, and MOSS's codec blocks are causal — decoding clip B
+with clip A in context changes B by about −7.5 dB relative error over its whole length.
+So the chain is re-encoded from the rendered waveform:
+
+`OpenMOSS-Team/MOSS-Audio-Tokenizer-v2`, **12 codebooks × 1024, 12.5 fps**. One frame is
+12 tokens = 80 ms. (The codec ships 32 quantizers; the model consumes 12. 32 is depth,
+not a block size.)
+
+Stored as `uint16 [T, 12]` in `<chain>.moss.npy`, with `moss_frames == floor(dur_s ×
+12.5)`.
+
+## Captions — two levels, both shipped
+
+### Inline, per segment
+A screenplay: `(emotions · voice descriptors) the words spoken`, one line per clip.
+Descriptors identical across the whole chain are hoisted out into
+`constant_descriptors`, because a value that never changes says nothing about a
+trajectory.
+
+### General, for the whole concatenation
+Derived by **scoring the rendered audio**, not the source clips. Two variants:
+
+* **`caption_general_a`** — the top 3–5 VoiceNet dimensions, **no emotion terms at all**.
+  Emotion comes solely from the inline tags.
+* **`caption_general_b`** — the same, plus the top 2–3 emotions.
+
+**The >30 s split.** The scorer pads/truncates every input to exactly 30 s, so scoring a
+45 s chain would silently describe only its first 30 s. Chains longer than 30 s are split
+at **segment boundaries** into ≤30 s parts, each part scored, and the parts combined by
+duration weight (VoiceNet regressions and emotion scores averaged; ordinal buckets taken
+from the longest part, so bucket and label stay consistent). `n_score_parts` records how
+many. In this tier **96.7 %** of chains are split — the normal case, not an edge
+case.
+
+**The emotion gate** is a tie-aware mid-rank ECDF, top 10 % *for that emotion*, max 3
+named. This matters: the 40 emotion heads sit on different scales, and `emo_Awe` is at or
+below zero for ~92 % of clips, so its p90 *value* is −0.0 and a naive `score >= p90_value`
+test would name Awe on about a third of all clips. The mid-rank ECDF maps that tie block
+to 0.47 and it correctly fails the gate. When nothing clears, **the clause is simply
+absent** — there is no "no dominant emotion" string. `emotion_clause_present` records it.
+
+Every ranking breaks ties **explicitly on the dimension or emotion name**, so captions are
+byte-identical across runs.
+
+## ⚠ Burst annotations mean two different things — read this
+
+A `(parenthetical)` inside the transcript is **not** one claim. Measured across the corpus:
+
+| source | paren & `n_bursts`==0 | paren & `n_bursts`>0 |
+|---|--:|--:|
+| **`vprof_vc`** | **37.7 %** | 11.4 % |
+| `emolia` | 0.0 % | 28.5 % |
+| `podcast` | 0.0 % | 44.0 % |
+| `kartoffelphon` | 0.0 % | 24.5 % |
+| `eurospeech` | 1.5 % | 46.2 % |
+| `mls` | 0.0 % | 0.4 % |
+
+In the **mined** corpora a parenthetical always coincides with a detected burst — it is
+detector output, an observed event. In **`vprof_vc`** roughly two-fifths of clips carry a
+parenthetical the detector never confirmed: those are burst *directions from the synthesis
+prompt*, not observations.
+
+Because `vprof_vc` is **100.0 %** of this tier, most parentheses here are the
+unconfirmed kind. So each segment carries the two claims **separately**:
+
+* `burst_detected` — `n_bursts > 0`, a real detection
+* `burst_scripted` — a parenthetical with `n_bursts == 0`, requested but unconfirmed
+* `burst_note` — `"detected"` / `"scripted-unconfirmed"` / `"none"`
+
+**Do not treat them as the same signal.**
+
+## ⚠ Language: the chain label is the FIRST clip only
+
+`lang_iso` on a chain is taken from its first clip. That is **not** the language of the
+whole chain. Measured on this tier by two independent methods (per-clip `lang` from the
+index, and stopword detection on the segment text):
+
+| | share of chains |
+|---|--:|
+| single-language | **7.4 %** |
+| **mixes two languages** (de+en) | **92.6 %** |
+
+The cause is structural, not a bug: a `vprof_vc` chain is five takes **by one cloned
+voice of five different texts**, and those texts are not all in one language. The voice
+identity is constant; the language is not.
+
+So each chain carries the honest fields alongside the label:
+
+* `langs` — sorted distinct languages actually present, e.g. `["de","en"]`
+* `lang_mixed` — true when more than one
+* `n_langs`
+* per segment, `segments[i].lang`
+
+**Filter on `langs` / `lang_mixed`, not on `lang_iso`**, unless you specifically want
+"whatever the first clip was". The per-tier `en %` / `de %` tables above are computed on
+the chain label and inherit exactly this caveat.
+
+## Composition
+| dataset | chains | licence |
+|---|--:|---|
+| `vprof_vc` | 319,650 | generated (see note) |
+| `emolia` | 115 | CC-BY-4.0 |
+
+This is the **CLEAN** release: `podcast` and `evasnippets` are removed for provenance, and `kartoffelphon` is held pending a licensing review.
+
+## Files
+
+```
+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
+```
+
+## Loading
+
+```python
+import glob, json, io, tarfile
+import numpy as np, soundfile as sf, pyarrow.parquet as pq
+
+# metadata for the whole tier
+meta = pq.read_table(sorted(glob.glob("traj-t80-*.parquet"))).to_pandas()
+print(len(meta), "chains")
+
+# the nested ladder is a filter, not a re-download
+hard = meta[meta.tier_max >= 0.80]
+
+# one chain, audio + codes + captions
+with tarfile.open("traj-t80-00000.tar") as tf:
+    names = [m.name for m in tf.getmembers() if m.name.endswith(".mp3")]
+    stem  = names[0][:-4]
+    wav, sr   = sf.read(io.BytesIO(tf.extractfile(stem + ".mp3").read()))
+    codes     = np.load(io.BytesIO(tf.extractfile(stem + ".moss.npy").read()))
+    info      = json.loads(tf.extractfile(stem + ".json").read())
+
+print(codes.shape, sr)                      # (frames, 12) uint16, 48000
+assert codes.shape[0] == int(len(wav)/sr*12.5)
+print(info["caption_general_a"])            # voice only
+print(info["caption_general_b"])            # voice + emotions
+print(info["script"])                       # inline screenplay
+for s in info["segments"]:
+    print(s["start"], s["burst_note"], s["tag"], s["text"][:60])
+```
+
+## Two nesting selectors, both shipped
+
+`tNN` columns (**the default**) select `qmax >= T` under each row's own rule. Nested by
+construction for both arms.
+
+`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**.
+
+> **`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.
+
+## Source datasets and licences
+
+| source | what it is | licence |
+|---|---|---|
+| `emolia` | YODAS-derived emotional speech | CC-BY-4.0 |
+| `kartoffelphon` | German speech (~51 % librivox) | CC-BY-4.0 · **held pending review** |
+| `mls` | Multilingual LibriSpeech | CC-BY-4.0 |
+| `eurospeech` | European parliamentary speech | CC-BY-4.0 |
+| `vprof_vc` | synthetic voice profiles, Chatterbox-VC + SIDON | see note below |
+| `snippets` | short reference snippets | CC-BY-4.0 |
+
+`podcast` and `evasnippets` are **excluded from every public release** — their per-item
+provenance is unknown or unclear and they are not openly redistributable.
+
+`kartoffelphon` is excluded from the CLEAN releases **pending a licensing review, not for
+any data defect**: ~51 % is librivox (public domain), the remainder is podcast-provenance
+material. Its coverage is excellent (100 % `-id` speaker embeddings, the best of any
+source). Every tier records `kartoffelphon_contribution` so the effect of clearing it is
+visible without a rebuild.
+
+Annotations are CC-BY-4.0. The generated voice-profile audio derives in part from EmoLia.
+
+## Honest limitations
+
+* **`vprof_vc` trajectories are constructed, not observed.** Takes are independent
+  renditions of *different texts* by one cloned voice; the ordering is imposed by the
+  packer. They carry no speaker-identity risk and no natural time axis. At the top of the
+  ladder they are almost the entire corpus.
+* **Five emotions cannot be trajectory endpoints under the two-sided rules** — Awe,
+  Distress, Sadness, Disappointment, Helplessness. Their raw scores are so zero-inflated
+  that the normalised value is effectively two-valued, so no per-step cap ≤0.25 can be
+  satisfied. This is arithmetic, not missing data. `PXR` recovers four of the five.
+* **EmoLia identity uses a timbre embedding**, not the verification model. Where
+  `cos_source == "orange-tbr"` the 0.80 threshold is applied to
+  `Orange/Speaker-wavLM-tbr`; store-to-store the equivalent of `-id >= 0.80` is
+  `-tbr >= 0.788`, so this is fractionally *stricter*, but it is a different space and its
+  pass rate reads ~7 points optimistic.
+* **The emotion scorer is weak in absolute terms** — 8.0 % top-1 against the requested
+  label on the voice profiles (chance 2.5 %). Selection is always on measured scores,
+  never on a requested-emotion label.
+* **Emotion clauses are common at chain level.** With 40 heads each gated at its own top
+  decile, P(none clears) ≈ 0.9^40 ≈ 1.5 %, so ~98 % of chains name at least one emotion.
+  That is the arithmetic of a 40-way gate, not evidence every chain is emotive.
+* Language is `lang_iso_fixed`, post-correction. `other %` is dominated by the
+  multilingual sources.
+
+## Citation
+
+```bibtex
+@misc{laion_traj_t80,
+  title  = {LAION Emotional-Trajectory Speech, tier T>=0.80},
+  author = {LAION},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/laion/laion-emotional-trajectory-t80}
+}
+```