README.md

metadata

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
configs:
  - config_name: default
    data_files:
      - split: train
        path: traj-t80-*.parquet

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, 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 __:

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. 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

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:

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

@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}
}