laion/laion-voice-profiles-sft

LAION Voice Profiles — TTS supervised fine-tuning set

Authors: Christoph Schuhmann and LAION.

1,200,531 instruction-tuning samples for reference-conditioned TTS, drawn from 500 synthetic voice profiles: for each of the 842 acting conditions of each voice, the best 3 of its 48 candidate takes, each paired with a different clip of the same voice from another group as the reference, plus the exact conditioning prompt, MOSS audio codes for both target and reference, word-level forced alignment, and the full corrected annotation.

Every record carries two prompts — one conditioned on reference audio + caption, one on the speaker's name — so the same data trains voice cloning and voice memorisation.

• 1,200,531 samples · 500 voices · 2,401 per voice · 6.3 GB · en + de
• target and reference shipped as MOSS-Audio-Tokenizer-v2 codes, not waveforms
• DPO companion: laion/laion-voice-profiles-dpo

---

Read this first

1. This is codes, not audio. Targets and references are MOSS RVQ codes ((frames, 12) uint16). That is what the base model consumes and emits. Decode with the codec if you want to listen. uid joins to the waveform in laion/laion-voice-profiles-annotated.
2. The reference is deliberately never the profile's own reference clip, and never a clip from the same group. It is another take of the same voice, usually a different emotion, chosen fresh per record so the model cannot learn a fixed pairing.
3. WER was not used to select anything, on purpose — see §"Ranking".
4. Speaker identity against the nominal reference is weak corpus-wide, and that is a property of the source material, not of this selection. Per-voice mean spk_sim has median 0.445 (range 0.176 to 0.691), and the median voice has only ~20 % of its takes at spk_sim >= 0.60. The takes of one voice are much more like each other than like the profile's reference clip — which is why a within-voice reference at cosine 0.60 is easy to find while 0.60 against the nominal reference is not. Train on this and you get the voice the generator actually produced, which is consistently a voice but often not the reference clip's. spk_sim and ref_spk_sim are on every row.
5. Nobody has listened to this in a controlled study. Every quality number is a model output.

---

Ranking — how the 3 of 48 were chosen

score = norm(genuineness) + norm(blend) + 2 * norm(emo_strength)

Emotion strength counts double, by design: the point of this corpus is acting, and genuineness and burst blend are means to it.

norm is a per-voice quantile (rank) transform — rank(x) / (n - 1) over all valid takes of that voice, giving each term a uniform [0, 1] marginal before summing. Per-voice, because the three scores have visibly different scales and spreads from voice to voice (a per-voice mean spk_sim ranges from 0.20 to 0.86 across the 500, and blend is heavily right-skewed), so a global z-score would be dominated by the loudest voices and by blend's tail. The rank transform makes the "2x" weight mean exactly what it says: emotion strength gets twice the ordinal pull. The unnormalised inputs (genuineness, blend, emo_strength) and the normalised terms (n_genuineness, n_blend, n_emo_strength) are both shipped, so you can re-rank differently without re-reading the corpus.

WER is ignored. The corpus was re-annotated end to end, and the generation-time wer compares a whisper transcript against a prompt that deliberately contains vocal-burst tags and non-verbal direction; a take that hits the direction perfectly can score a terrible WER. It is shipped (wer) but was not consulted.

Takes are eligible if they are non-empty, at least 0.5 s, and present in the tokenised corpus.

---

Reference-clip selection

For each sample, the reference is another take of the same voice, from a different gid, required to satisfy

cos(ECAPA(sample), ECAPA(reference)) >= 0.60

spk_emb is the generator's own 192-d ECAPA embedding. It was verified against the corpus's spk_sim column before use: recovering the profile's reference embedding by least squares from 40k takes reproduces spk_sim with correlation 1.00000 and RMSE 1e-5, so this is exactly the space the project's speaker thresholds live in.

Why 0.60. It is the project's own MIN_SPK_TRAIN, and it is where the null distribution dies: measured over 44.2 million genuinely cross-voice pairs (24 voices, all pairs), P(cos >= 0.60) = 0.0042. So a reference admitted at this threshold has a 0.42 % chance of being what the corpus would call a different speaker. At 0.40 — the corpus's "not the same speaker" floor — that rises to 3.6 %; at 0.70 it falls to 0.09 % but coverage drops sharply.

There is deliberately no floor on the reference's own spk_sim. The requirement is similarity to the current sample, not to the profile's nominal reference. An early build gated the pool at spk_sim >= 0.60 as well and collapsed the low-identity voices — emolia_c0241 produced 6 samples instead of ~2,400, because 0.005 % of its takes reach 0.60 against its own reference — while changing the healthy voices by nothing at all. ref_spk_sim is shipped so you can re-impose that gate if you want it.

Varying the pairing. The pool is up to 3 takes per gid (highest spk_sim first), 2–25 s, capped at 1,500 per voice. Assignment walks the samples in random order and gives each the least-used eligible reference, ties broken at random, so the load spreads: about 1,297 distinct reference clips per voice serve 2,401 samples, and the mean realised cosine is 0.659. ref_emotion / ref_block differ from the target's on the large majority of rows — different emotion is the normal case, as intended.

Coverage. For each gid the top 8 candidates by score are considered and the best 3 that can be paired with a valid reference are kept, so a candidate that is too far off-identity for any reference to match is replaced by the next-best take rather than dropping the slot. Realised: 95.1 % on average (median 95.3 %, worst voice 86.8 %) of the nominal 842 x 3 = 2,526 per voice. The shortfall is concentrated in voices whose takes are mutually inconsistent; it is not uniform.

---

The material this is built from

500 synthetic voice profiles. Each profile is one reference speaker driven through the same fixed matrix of acting conditions in English and German, keeping every candidate take rather than only the winners. Generator laion/moss-tts-local-transformer-4.55b-voice-acting-v2, codec OpenMOSS-Team/MOSS-Audio-Tokenizer-v2, run tag PPILOT2. The full 20,125,736-take corpus with audio is laion/laion-voice-profiles-annotated; the per-voice LoRAs and reference clips are laion/moss-voice-profile-loras-500.

The group structure, measured

Per voice: 842 condition groups (gid), of which 832 have 48 takes and 10 have 32. Each gid splits into subset A = 32 takes and subset B = 16 takes.

Two things about this are easy to get wrong, and both were checked against the data rather than taken from prose:

• A gid is not one sentence. All 48 takes of a gid carry different text — 807 of 842 gids have 48 distinct sentences. text_slot is a slot label (char::granite-titan), not a sentence. The one-sentence-many-takes reading of a gid is wrong.
• The minimal pairs live in subset B, across gids. The same sentence is reused at (voice, parent_key, lang, sub_idx) — i.e. the same B-slot of every gid under one parent_key. That yields ~3,055 same-sentence quadruples per voice: the same line rendered at the 4 levels of one VoiceNet dimension, or under the 4 acting conditions (A/B/C/D) of one emotion. Nothing else in the corpus is a same-sentence set.

The "832" figure that circulated for this corpus is exactly right — it is the 832 full-size gids — and "16" and "32" are subsets B and A of each of them, not group sizes. 832 x 3 x 500 = 1,248,000 is the arithmetic the ~1.25 M target came from.

condition and level

Emotion cells are E|<Emotion>|<A|B|C|D>, where A/B/C/D is the 2x2 of intensity x containment:

code	intensity	containment	prompt gloss
A	intense	free	maximum emotion, freely let out
B	moderate	free	mild emotion, freely expressed
C	intense	contained	very strong but held back — it leaks through
D	moderate	contained	mild, quietly controlled

VoiceNet cells are V|<DIM>|<level> over the ladder extremely_low < moderately_low < moderately_high < very_high.

---

Provenance of every number, and which annotation tree was used

This corpus has a known defect: the annotation layer of the live release laion-tts-annotated-v1/index/vprof_base was computed on a half-speed decode — a stereo mp3 bug doubled the sample count. Every duration, every emotion intensity and all 57 VoiceNet dimensions in that tree are wrong. Nothing in this dataset comes from it. Two independent checks confirm which tree is which, both run for this build:

• dur_s in the live tree is exactly 2.000x the re-annotated dur_s on all 9,916 rows of shard 0 (std 0.000).
• the re-annotated dur_s equals the generator's own dur to the digit (ratio 1.000, std 0). The generator measured it on the in-memory waveform before mp3 encode, so it never saw the bug.

The MOSS codes were re-tokenised too, and that was checked on the bytes, not on the index. For the same six utterances, the .moss.npy member of the re-annotated tar has shape (199, 12), (170, 12), (186, 12), (400, 12), (186, 12), (159, 12) — exactly round(generator_dur * 12.5) in every case — while the live tree's tar has (398, 12), (340, 12), (372, 12), (800, 12), (372, 12), (318, 12), i.e. double. Across shard 0, moss_frames / (generator_dur * 12.5) is 1.000 (std 0) in the re-annotated tree and 2.000 (std 0) in the live one. The codes shipped here are the corrected ones. This matters more than any other check in this build: the earlier half-speed MOSS codes are unusable, and an index-only comparison could not have caught it — moss_frames == round(dur_s * 12.5) passed at 100 % on the broken data precisely because both terms were doubled.

Accordingly:

field group	source	why
genuineness, blend, emo_strength, spk_sim, spk_emb, dur, wer	the generator's own meta-*.parquet (shipped verbatim as index/vprof_base_scores)	measured pre-encode; unaffected by the decode bug
emotion, condition, dim, level, cond_key, parent_key, subset, sub_idx	same	these exist nowhere else
instruction_generation	the generator's caption column	the literal conditioning string the model was given
40 emo_*, 57 vn_*_reg, 4 qual_*, blend_0_10, genuineness_0_6, n_bursts, text, caption_general	the corrected re-annotation tree laion-tts-annotated-v1-reann	recomputed on correctly decoded audio
words_json	<uid>.json in the re-annotated tars — MMS_FA forced alignment of the prompt text
*_codes	<uid>.moss.npy in the re-annotated tars

---

The procedural caption is regenerated here, not copied

instruction_caption is rendered in this build from the numeric columns. The corpus's own caption column is deliberately not shipped, because it was being regenerated corpus-wide while this dataset was built and copying it would have baked a caption into a training set that disagrees with the corpus a downstream user reads. Two independent defects are fixed:

1. Polarity. The GEND and BKGN prose ladders ran backwards — high vn_GEND_reg is masculine and high vn_BKGN_reg is cleaner, and the ladders said the opposite. In the live corpus tree the mean vn_GEND_reg of clips captioned masculine is 0.81 against 2.49 for feminine. Captions here use the corrected ladders (caption_render.py, polarity="v2" — the same tables as caption2.py, md5 ec55b223bab60f9f1cfb7b9bedfdf76f). The numeric columns were always correct; only the words inverted. The polarity is now confirmed four independent ways: chest-resonance correlation (+0.87), head-resonance (−0.55), the corrected ladder's own derivation, and 89.2 % agreement between numeric vn_GEND_reg and each profile's independent design-spec card_gender — a figure that would be ~11 % if the polarity were inverted.

2. The emotion gate. Which emotions a caption names is decided here by a percentile gate rather than an absolute threshold: an emotion is named only if its value lands in the top 10 % of that emotion's own distribution, at most the top 3, and nothing at all if none clears the floor. The distribution comes from the tie-aware mid-rank ECDF in $NB/emonorm/out/capnorm.npz — 4,096-bin histograms over 132,833,726 rows pooled across 8 datasets and both languages. This matters because the 40 emotion heads sit on very different scales: measured on 120,659 rows of this corpus, emo_Interest has median 1.969 and no values at or below zero, while emo_Infatuation has median −0.015 and 81.7 % of its values at or below zero. One absolute threshold cannot be right for both.

Tie-awareness is what makes this work rather than a plain 0.90-quantile value threshold: several heads have a majority point mass at zero, so a value threshold would sit at zero and admit everything, while mid-rank gives the whole tied group a percentile near 0.5 and correctly declines to name it. Implementation and validation runs are in code/capgate.py.

One claim about this that I could not reproduce, stated because it shaped the design. The percentile gate was introduced on the premise that the absolute gate named Interest on ~95 % of captions. On this corpus that is not what happens: the shipped caption_general of the re-annotated tree names "interest" on 2.3 % of 120,659 sampled rows. So the gate here is a principled normalisation across incommensurable heads — which the median/zero figures above do justify — and not a fix for a runaway-Interest bug, which this corpus does not exhibit. If you prefer the corpus's own wording, regenerate it with caption_render.render(row, polarity="v2"); every column it needs is shipped.

Realised on the captions actually shipped here, measured on 95,680 of them: Interest 4.4 % of clips, all 40/40 emotion heads appear, 2.93 emotions named per clip, and 0.00 % carry "no dominant emotion". That last figure is essentially zero because of selection, not because of the gate: every row here is one of the top-3 takes of its cell ranked with emotion strength weighted double, so essentially every clip clears a top-10 % floor on something. Do not read these numbers as corpus statistics — they are statistics of a deliberately emotional subset. instruction_caption is nullable and is null on 2 of the sampled rows where the renderer could not build a caption from the row's numbers.

The prompts were never affected by any of this. prompt_reference and prompt_name embed instruction_generation — the generator's own GENERAL:/SCRIPT: conditioning string, which is what the model was actually given at generation time and is not a procedural caption at all. Swap instruction_caption into the - Instruction: slot to train caption-following instead.

---

MOSS codes — layout, and the "32 tokens" question

target_codes / chosen_codes / rejected_codes / ref_codes / donor_codes are raw little-endian uint16, shape (frames, 12) after reshape.

import numpy as np
codes = np.frombuffer(row["target_codes"], dtype="<u2").reshape(row["target_frames"], row["n_vq"])

• 12 codebooks x 1024 entries, 12.5 fps. frames == round(dur_s * 12.5) holds on every row.
• The atomic unit is one frame = 12 tokens = 80 ms. There is no 32-token block, no delay pattern and no interleaving: the model emits all 12 channels of one frame sequentially inside a 1-layer local ("depth") transformer with n_positions = n_vq + 1 = 13, RQ-Transformer style.
• The 32 associated with this stack is the codec's num_quantizers: 32. The codec ships 32 residual quantizers; the TTS model consumes only the first 12 (codes[:, :n_vq]). It is a depth, not a block size. Every cut in this dataset is therefore aligned to a 12-token frame boundary, which is the only alignment the format actually has.

---

The two conditioning modes

Every record carries both, as complete strings, in the base model's own <user_inst> template (processing_moss_tts.py::UserMessage), and also as the components so you can re-template:

prompt_reference — reference audio + caption. <|audio|> is where the reference clip's MOSS codes are spliced in as a prefix (ref_codes, ref_frames):

<user_inst>
- Reference(s):
<|audio|>
- Instruction:
GENERAL: A voice intensely expressing affection, ... impossible to hide.
SCRIPT:
(letting it out / not hiding it, warm and open, unguarded) "..."
- Tokens:
19
- Quality:
None
- Sound Event:
None
- Ambient Sound:
None
- Language:
German
- Text:
Für die letzten zwei Tage dieser langen Reise ...
</user_inst>

prompt_name — the speaker's name in place of the reference audio, so the model can memorise the voice. Byte-identical to prompt_reference except the Reference(s): slot:

- Reference(s):
Speaker: Katrin

- Quality:, - Sound Event: and - Ambient Sound: are None because the generator never passed them — that is the literal prompt shape the model saw. - Tokens: is a reconstruction: the generator passed a length budget derived from the word count, raised for non-verbal carriers like screams and groans; the value here is the aligned n_words of the same text, which agrees with it on ordinary lines but not necessarily on the burst-heavy ones. It is the one field of the prompt that is rebuilt rather than lifted. Everything else in prompt_reference is either the generator's own string (instruction_generation) or a corpus column.

Train on either mode, or mix them per step; speaker_name and ref_* are both present on every row. instruction_generation is the generator's own conditioning string; instruction_caption is an independently rendered description of the delivered audio, drawn from the corrected numeric annotation using one of the 16 procedural templates (caption_template says which — it is rotated per record so no template dominates). Swap one for the other in the - Instruction: slot to train caption-following rather than direction-following.

---

Names

Each profile has one unique given name, and an acoustic gender class measured from vn_GEND_reg over that profile's own takes (high = masculine, verified: +0.87 with chest resonance, −0.55 with head resonance across the 500 per-voice means). Pooled over 1,003,974 clips the distribution is bimodal, modes at 1.587 and 4.867, 50/50 boundary at 3.2103; a voice is male when >= 85 % of its takes sit above it, female when <= 15 % do, uncertain otherwise — 209 / 170 / 121. Uncertain voices get names that read as unisex in Germany or the US. 125 German names, 375 English, following card_language where it exists.

names.csv / names.json ship in this repo, and the same mapping is in laion/moss-voice-profile-loras-500 (name in manifest.parquet, voice.json, profile.json).

The pre-existing card_gender label — a VLM's read of the reference clip and the voice's design spec, not of the generated takes — agrees on 89.2 % of the 379 confidently classified voices and disagrees on 41, which names.csv flags. The disagreements track speaker-identity failure: their rate falls from 15.8 % in the weakest quartile of frac_ge06 (share of takes reaching spk_sim >= 0.60) to 3.2 % in the strongest, monotonically. Note also that the uncertain bucket is mostly measured ambiguity, not designed: only 12 of 121 carry card_gender = Androgynous.

Column dictionary

sft columns

column	type	meaning
uid	large_string	utterance id; joins to laion/laion-voice-profiles-annotated and to the score index
voice	large_string	voice-profile id (one of 500)
speaker_name	large_string	the assigned unique given name for this voice
speaker_gender	large_string	acoustic gender class: male / female / uncertain (from vn_GEND_reg)
shard	large_string	source shard of the corpus (vprof_base-NNNNN)
audio_key	large_string	<gid>.cNNN.mp3 — the key inside the corpus tar
gid	large_string	condition group: `
cand	int64	candidate index 0..47 within the gid
subset	large_string	A (32 takes, own sentences) or B (16 takes, the minimal-pair slots)
sub_idx	int64	index within the subset; with parent_key and lang it identifies the sentence in B
block	large_string	emotion / voicenet / edge / character / burst_isolated / explicit / sports
lang	large_string	en or de
cond_key	large_string	the (emotion, condition) or (dim, level) cell, without language
parent_key	large_string	the emotion or dimension the cell belongs to; groups the 4 conditions/levels
text_slot	large_string	slot label of the sentence pool, NOT the sentence
emotion	large_string	requested emotion (emotion block only)
condition	large_string	requested acting condition A/B/C/D (emotion block only)
intensity	large_string	intense / moderate — decoded from condition
containment	large_string	free / contained — decoded from condition
dim	large_string	requested VoiceNet dimension (voicenet block only)
level	large_string	requested level: extremely_low / moderately_low / moderately_high / very_high
edge	large_string	edge-case id (edge block only)
character	large_string	character id (character block only)
burst_class	large_string	vocal-burst class the generator asked for, if any
is_holdout	bool	the gid was a validation gid in the LoRA work; not excluded here
text	large_string	the prompt text, verbatim
text_with_bursts	large_string	the same text with detected vocal bursts marked inline
n_words	int64	word count; also the - Tokens: budget in the prompt
align_status	large_string	forced-alignment status from the re-annotation (ok or an error string)
dur_s	double	duration in seconds, from the generator's pre-encode measurement
instruction_generation	large_string	the literal GENERAL:/SCRIPT: string the generator was conditioned on
instruction_caption	large_string	an independently rendered description of the delivered audio, from the corrected numeric annotation
caption_template	large_string	which of the 16 procedural templates instruction_caption uses
spk_sim	double	ECAPA cosine of this take to the PROFILE's reference embedding (generation time)
genuineness	double	generation-time genuineness, 0-6
blend	double	generation-time vocal-burst blend, 0-10
emo_strength	double	generation-time emotion strength
wer	double	generation-time WER — shipped, not used
n_genuineness	double	per-voice quantile rank of genuineness
n_blend	double	per-voice quantile rank of blend
n_emo_strength	double	per-voice quantile rank of emo_strength
genuineness_0_6	double	re-annotated genuineness
blend_0_10	double	re-annotated vocal-burst blend
qual_overall	double	re-annotated overall quality head
qual_speech	double	re-annotated speech quality head
qual_background	double	re-annotated background quality head
n_bursts	int64	re-annotated vocal-burst count
burst_labels	list<element: string>	re-annotated vocal-burst labels
top_emotion	large_string	argmax of the 40 re-annotated emotion intensities
top_emotion_value	double	its value
sft_score	double	n_genuineness + n_blend + 2*n_emo_strength
sft_rank	int64	1..3 within the gid
target_frames	int64	MOSS frames of the target
n_vq	int64	codebooks per frame — always 12
target_codes	binary	target MOSS codes, little-endian uint16, reshape (target_frames, 12)
words_json	large_string	MMS_FA word alignment: [{w, s, e}, ...], seconds
ref_cos	double	ECAPA cosine between this sample and its reference clip (>= 0.60 by construction)
ref_same_parent	bool	whether the reference came from the same parent_key
ref_uid	large_string	uid of the reference clip
ref_gid	large_string	its gid — always different from gid
ref_parent_key	large_string	its parent_key
ref_lang	large_string	its language
ref_block	large_string	its block
ref_emotion	large_string	its emotion, if any — usually different from the target's
ref_dur_s	double	its duration
ref_spk_sim	double	its own ECAPA cosine to the profile reference
ref_frames	int64	its MOSS frames
ref_codes	binary	its MOSS codes, same encoding as target_codes
prompt_reference	large_string	complete prompt, reference-audio mode (`<
prompt_name	large_string	complete prompt, name mode (Speaker: <speaker_name>, no audio)
emo_vector	binary	the 40 re-annotated emotion intensities, float32 little-endian, alphabetical
vn_reg_vector	binary	the 57 re-annotated VoiceNet regressions, float32 little-endian, alphabetical

---

Limitations, honestly

• No human has listened to any of this in a controlled study. Every selection criterion here is a model output — VoiceNet dimensions, EmoNet intensities, an ECAPA cosine, a genuineness head. "Best 3 of 48" means best by those proxies.
• The emotion DPO family is a same-sentence, different-condition comparison, not a same-prompt resample. The corpus contains no repeated generations of one prompt, so the negative was generated under a different instruction. It is a legitimate "this line, wrong reading" negative, and it is not an "identical prompt, worse sample" negative. Anyone expecting the latter should know they are not getting it.
• vocal_burst and blend in the family-(a) formula are the same physical quantity measured twice (re-annotation vs generation time). The terms are correlated; the ranking is effectively ~2x burst blend + 2x target emotion. Kept as specified rather than silently changed.
• Truncation and continuation negatives are constructed, not observed. A real model failure looks different from a clean prefix or a hard splice. These teach the shape of the failure, not its texture. The continuation seam has no crossfade.
• Speaker identity is weak against the nominal reference, everywhere. Per-voice mean spk_sim spans 0.176 to 0.691, median 0.445; the median voice has ~20 % of its takes at 0.60 or above. 138 voices sit below 0.40. The takes are mutually consistent — which is why every voice still yields references at cosine 0.60 — but "sounds like this profile's reference clip" is a much weaker claim than "sounds like one consistent speaker". This is inherited from the generation run; nothing in this build could fix it. Filter on spk_sim / ref_spk_sim if voice fidelity to the reference matters more than coverage.
• The name-conditioned prompt inherits that. For a drifted voice, Speaker: <name> will teach the drifted voice, not the reference clip.
• is_holdout marks the gids reserved as the per-voice validation split in the LoRA work. They are included here — exclude them yourself if you intend to compare against those numbers.
• Forced alignment is imperfect. align_status is ok on the large majority but not all rows; where alignment failed there are no words, so no truncation pair was emitted for that take.
• German and English are balanced by construction (one gid per language per cell), but the text pools differ in origin and the German is partly translated.
• Everything here is model output, including the reference clips. No take is a recording of a person. Only the emolia_* family traces back to real recorded speech upstream, and that upstream dataset is itself CC-BY-4.0.

---

Reproducing

Build code is in code/ in this repo: build_voice.py (selection, reference assignment, truncation points), emit.py (record assembly), run_seq.py / run_all.sbatch (500-way fan-out), aggregate.py + make_cards.py (the verification below and this card), namepools.py, quickstart.py. It is CPU-only and cost zero GPU-hours — every model score it consumes already existed in the corpus. Actual compute is ~11.1 core-hours of work; it was run as 4 booster nodes for about half an hour, which the site bills at 288 core-h per node-hour whether or not the GPUs are used.

Verification run on the shipped files

check	result
rows shipped	SFT 1,200,531 · emotion 1,064,594 · truncation 1,186,406 · continuation 1,200,531
every parquet footer opens, no stray temp files	yes, all 2000
files	500 + 500 + 500 + 500 parquet, no loose files
size	6.3 GB + 19.1 GB
target_frames == round(dur_s * 12.5)	95,680 / 95,680 rows
code buffer length == frames * 12	1,600 / 1,600 sampled
all codes in 0..1023	1,600 / 1,600 sampled
reference is from another gid	95,680 / 95,680
ref_cos >= 0.60	95,680 / 95,680
last word end <= dur_s	1,600 / 1,600 sampled
duplicate uid within a voice	0
distinct speaker names in 40 sampled voices	40
truncation cut is a whole frame, inside the clip	23,578 / 23,578
DPO emotion: text == rejected_text	21,118 / 21,118
DPO emotion: condition or level differs	21,118 / 21,118
DPO emotion: target_gap >= 0.15	21,118 / 21,118
caption gender agrees with the voice's measured class	40 / 40 voices
...share of captions saying masculine for male voices	97.2 %
...share saying feminine for female voices	97.6 %
DPO continuation: donor from another gid	23,825 / 23,825
DPO continuation: rejected_frames == chosen + donor	23,825 / 23,825
DPO continuation: donor <= 3 s	23,825 / 23,825 (mumble 16062, near_silent 7507, short_failed_take 256)

Each check reads the released parquet and re-derives the property from a different quantity than the writer used — frames against dur_s * 12.5, code length against the declared frame count, ref_gid against gid, text against rejected_text — rather than re-running the builder.

---

Licence and attribution

CC-BY-4.0. Credit Christoph Schuhmann and LAION, and the upstream sources listed in laion/laion-voice-profiles-annotated. The 500 profiles are synthetic; 385 of them are model re-interpretations whose source identifiers are deliberately not reconstructible from any release.

@misc{laion_voice_profiles_tts_2026,
  title  = {LAION Voice Profiles — TTS fine-tuning sets (SFT and DPO)},
  author = {Schuhmann, Christoph and {LAION}},
  year   = {2026},
  url    = {https://huggingface.co/datasets/laion/laion-voice-profiles-sft}
}