--- 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 **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 `.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, 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 | |---|---|---| | `.mp3` | **MPEG-1 Layer III, 48 kHz, 96 kbps CBR, mono** | the crossfaded chain, read out of the shipped bytes | | `.json` | JSON | `chain_id`, both general captions, `script`, `segments`, `constant_descriptors`, `speaker_clause`, `tier_max`, `rules`, `dur_s`, `moss_frames`, `n_score_parts` | | `.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,..." ``` 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__reg` + `vn__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 ```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]) ``` ## Selecting a tier 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: ```python harder = meta[meta["tier_max"] >= 0.90] ``` **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 | 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} } ```