laion/laion-voice-profiles-dpo-cfg

LAION Voice Profiles — contrastive DPO pairs (CFG + phase 2)

Authors: Christoph Schuhmann and LAION.

842,935 preference pairs in four families, built from the same 500 synthetic voice profiles as laion/laion-voice-profiles-sft and laion/laion-voice-profiles-dpo. These are the two pair families that the sister DPO set does not contain: they were built later, for two measured defects of the models trained on it, and they are the complete remainder of the project's preference material that is publishable under CC-BY-4.0.

config	family	pairs	contrast	words in the prompt?
cfg	cfg_high	237,209	same voice, matched length, the top 1 % of that voice's own range on one measured dimension against the bottom 1 %; the instruction asks for the dimension high, the high clip is chosen	no — replaced by ...
cfg	cfg_low	237,209	the same 237,209 pairs with the roles flipped: the instruction asks for the dimension low, the low clip is chosen	no
p2	p2_emox	309,128	same voice, matched length, intense on emotion A against intense on emotion B; the instruction names A	no
p2	p2_len	59,389	the same clip at its true length against itself cut to 50–75 % or run on to 125–150 %, with the emotion named	yes — the full timed script

Both sides of every pair ship as MOSS-Audio-Tokenizer-v2 codes (codes = chosen, rej_codes = rejected), together with the reference clip the chosen take was generated against (ref_codes). There is no audio in this repository; the waveforms of every source clip are in laion/laion-voice-profiles-annotated and the join is exact — see The join.

---

Read this first

1. "CFG" is the project's name for the construction, not a guidance scale. Nothing here was generated with classifier-free guidance and there is no numeric guidance weight anywhere in the data. The name comes from the shape of the pair: two clips of one voice that differ only in how strongly one dimension is expressed, put under one prompt that states the level, and emitted in both directions — the same conditional/unconditional contrast that guidance exploits at inference, moved into the preference data. cfg_high / cfg_low mean "the instruction asked for the dimension high / low", nothing else.
2. uid is a pair id, not a corpus utterance id. cfg<pair_id><h|l> and p2<pair_id><e|c|x>. The source clips are named in cfg/pairs_index.parquet and p2/pairs_index.parquet (and, for cfg, also in the row's own caption_tpl), and those ids are byte-identical to uid in laion/laion-voice-profiles-annotated.
3. Every row is src = 0, i.e. one of the 500 published synthetic voice profiles. No real recording, no podcast, no broadcast material contributes a clip, a transcript or a word timeline to this set. Checked on every row and every source-clip reference, as an allowlist.
4. The words are deliberately absent from three of the four families. A DPO pair shares one prompt; when chosen and rejected are different recordings with different words, the words would make the preference decidable from the transcript. cfg_free_text = true tells the prompt renderer to print ... for every speech chunk while keeping every pause, duration, burst and direction tag. The text column is still shipped — it is the chosen clip's text, for lookup — but it must not go into the prompt of those rows.
5. These rows are the training-corpus format, not the sister set's 86-column format. They are the exact files the two DPO runs below read, shipped unchanged (31 columns, zstd, 128 shards by uid hash). The per-clip annotation stack (40 emotion heads, 57 VoiceNet dimensions, captions, speaker embedding) is not repeated here; it is one join away.

---

Files

path	count	size	what
cfg/shard-000.parquet … shard-127.parquet	128	3,668,193,909 B	cfg_high + cfg_low, 474,418 rows, 31 columns
p2/shard-000.parquet … shard-127.parquet	128	2,822,830,198 B	p2_emox + p2_len, 368,517 rows, same 31 columns
cfg/pairs_index.parquet	1	8,533,671 B	237,209 base pairs: pair_id, hi_uid, lo_uid, measured values, percentiles, frames
p2/pairs_index.parquet	1	9,697,805 B	368,517 rows: pair_id, ch_uid, rj_uid, percentiles, frames, mode, cut_frames
code/build_cfg.py, code/build_pairs2.py	2	—	the two builders, verbatim, with their design notes in the docstrings
code/mix_cfg.py, code/mix_pairs2.py	2	—	how the families were concatenated onto the earlier corpora for training
verify.json	1	—	the verification run reproduced at the end of this card

Rows are bucketed by blake2b(uid) % 128, so every shard is a uniform random sample of its config and the two families of a config are interleaved. Shard b of cfg/ and shard b of p2/ are unrelated.

from datasets import load_dataset
cfg = load_dataset("laion/laion-voice-profiles-dpo-cfg", "cfg", split="train")
p2  = load_dataset("laion/laion-voice-profiles-dpo-cfg", "p2",  split="train")

---

What the pairs are, and why

Everything the project's models had been trained on described an emotion; nothing ever made two clips compete on how strongly one dimension is expressed. Asked for the 0.90–0.98 percentile of a named emotion, the round-3 model landed at 0.34. The two builds here are the two attempts to put intensity itself into the preference signal. Quoted numbers below are from the project protocol (§16, §I.13 of the technical report) and were re-derived from the shipped files where possible.

cfg — direction contrast on one measured dimension, both ways

For one voice and one dimension, the clip pool is that voice's own training clips (1,199,355 clips over 500 voices in the pool: src = 0, not held out, with codes), ranked by the value the annotation model measured on the finished audio — never by what the generating prompt asked for and never by the clip's intended condition.

• dimensions: all 40 emotion heads and 15 VoiceNet ordinal ladders that describe how something is spoken — AROU BRGT CLRT DFLU FULL RANG RESP ROUG STNC TEMP TENS VALN VOLT VULN WARM. Identity, recording-quality and content heads (AGEV GEND RCQL BKGN ESTH EXPL) are excluded: they are not things a performer can be asked to do.
• the two clips: top 1 % against bottom 1 % of that voice's distribution (floor of 24 clips per tail when 1 % is smaller). For emotion heads a separation of at least 0.25 corpus IQR is required; realised median corrected percentile of the high clip is 0.998, of the low clip 0.000, and the smallest high-minus-low gap in the set is 0.73. For VoiceNet ladders the two clips sit in different ladder buckets (high side mostly buckets 4–6, low side 0–1).
• same voice (voice_key), so speaker identity cannot decide the preference; frames within 10 % (|f_hi − f_lo| ≤ 0.10 · min, true on 237,209 / 237,209), so length cannot decide it; no words, so the transcript cannot decide it. Language is not matched — 54.6 % of pairs are same-language; the row's lang is the chosen clip's.
• nine base pairs per (voice, dimension) cell, 237,209 base pairs in all (173,116 on emotion heads, 64,093 on VoiceNet ladders), 408–495 per voice.
• both roles. Every base pair is emitted twice. cfg_high (uid ends in h): instruction asks for the dimension high, codes is the high clip, rej_codes the low one. cfg_low (l): instruction asks for it low, roles swap. Each clip is therefore chosen in one row and rejected in another; a policy that simply prefers louder or denser audio scores 50 % on this set, and the only way to win is to condition on the instruction. The two families are balanced to the row.

How the level is stated. For an emotion head the row carries the corpus's own caption form, A voice <band phrase> expressing <emotion>, <prose>; reads as <emotion>, and emo_strength is set so the prompt renderer picks the matching band:

role	emo_strength	band	band phrases	prose tail
high	0.95 or 0.99, alternating by uid hash	intense / extreme	intensely, powerfully, viscerally / to the extreme, utterly, overwhelmingly	the corpus's expressed-side prose for that emotion
low	the low clip's own percentile, capped at 0.60 (0.00 on 162,160 of 173,116 rows)	faint	mildly, slightly, faintly, a little	one of three generic "barely there" tails

The high role alternates between intense and extreme on purpose: both words are true of a clip at percentile 0.998, and a two-point contrast cannot calibrate between them. For a VoiceNet ladder there is no emotion to name; caption_general is A voice; <clause> is <ladder tag> and cfg_neutral carries the direction the renderer puts into the script — "<tag>, and hold it there; otherwise exactly as this voice normally speaks" — with emo_strength = null (128,186 rows).

p2 — the two phase-2 families

Built after the cfg run, for two defects it measured. The base pool and the matching rules are the same as above; the selection constants are HI_PCT = 0.90, LO_PCT = 0.50, FRAME_TOL = 0.10, 8 emotion-contrastive and 6 length pairs per cell, seed 4711.

p2_emox — intense against intense, for selectivity. Per-emotion adapters trained on intense-versus-mild contrasts raised their own emotion by +0.047 when asked for it — and by +0.033 when not. "A ratio of 1.4 : 1 is not control, it is tinting" (the builder's docstring). Every earlier contrast pits an intense clip against a mild one, so a model can win by being generically expressive. Here the chosen clip sits at percentile ≥ 0.90 on the head being asked for and has that head as its own top emotion; the partner is ≤ 0.50 on that head and ≥ 0.90 on its own top head. Both sides really are intense, on different emotions, same voice, frames within 10 % (true on 309,128 / 309,128), words removed. Each base pair is emitted twice, the mirror naming the partner's own top emotion with the roles swapped (154,564 base pairs, 19,884 (voice, emotion) cells, all 40 heads). Realised: chosen-side percentile median 0.986 (min 0.900), rejected-side median 0.215, median gap 0.76.

One honest detail about the mirror: the ≤ 0.50 bound was enforced on the original direction only. In the mirror row the rejected clip is the original's chosen clip, whose percentile on the partner's emotion was never constrained. Across all 309,128 rows the rejected side is ≤ 0.50 on the named head on 247,324 (80.0 %), above 0.50 on 61,804, and ≥ 0.90 on 14,281 (4.6 %) — those last are pairs where one clip is intense on both emotions — and on 3,372 rows (1.1 %) the rejected side is actually higher on the named head than the chosen one (worst gap −0.095). p2/pairs_index.parquet carries ch_pct and rj_pct per row, so you can filter on the gap you want.

p2_len — the right length, conditioned on the emotion. The sister set's truncation and continuation families know nothing about emotion and teach "the right length is better" in the abstract. Here the speaker, the text and the emotion stay fixed and only the length moves: chosen is a clip whose top emotion is at percentile ≥ 0.90, rejected is the same clip cut to 50–75 % of its frames (mode = cut, 29,564 rows; realised keep fraction 0.49–0.75, median 0.62) or run on to 125–150 % by appending the opening frames of another clip of the same voice (mode = ext, 29,825 rows; realised 1.23–1.50, median 1.37; the donor is rj_uid, same language on 54 %). Because the words are shared, the prompt carries the full timed script — the timing tags are exactly what the rejected side violates. Clips shorter than 40 frames (3.2 s) were not used.

What they trained, and what happened

adapter	corpus	pairs seen	reward	WER	emotion pct	quality	burst	burst hit rate
SFT-3 base, no adapter	—	—	0.4584	0.0987	0.3494	0.9127	0.3564	0.666
previous best DPO (sister families only)	1,853,486 pairs	step 3216	0.4687	0.1094	0.3401	0.9211	0.3929	0.709
laion/moss-va-sft3-dpo-lora	sister families + cfg (2,327,904; 20.4 % cfg)	step 4912	0.4708	0.0950	0.3373	0.9235	0.4271	0.772
laion/moss-va-sft3-dpo-lora-p2	the above + p2 (2,696,421; 13.7 % p2)	step 5022	0.4757	0.0977	0.3541	0.9208	0.4180	0.762

Both runs: LoRA rank 64 on laion/moss-tts-local-transformer-4.55b-voice-acting-v2-sft3, β 30, lr 1e-6, 256 pairs per step, 8 nodes, stopped by a 6-hour wall clock at roughly half an epoch; 80-prompt standard evaluation, 320 clips per row.

Read plainly: the cfg families made the best general checkpoint the project had measured — word error rate better than the supervised base, the highest quality and burst realisation — and did not move the thing they were built for; emotion percentile fell. Preference accuracy on the cfg rows went 0.56 → 0.98, so the model learned to recognise the level without gaining the ability to reach it — the high side of a pair is only as intense as the corpus gets. The p2 families then produced the first preference-tuned model above the supervised baseline on emotion percentile (0.3541 against 0.3494, requested band 0.90–0.98), with emotion-contrastive preference accuracy 0.951 post-warmup, while p2_len saturated at accuracy 1.000 in the second half of the run — that family is solved and could be dropped from a future mix. The remaining half epoch of each run was never trained.

---

The prompt

The rows carry the ingredients of the prompt, not the rendered string, because the renderer is stochastic (direction placement, timed/untimed script, burst dropout, reference-versus-name slot, all drawn per step). The training code rendered every row into the base model's <user_inst> template with prompt_lib2.render_prompt (format hash 073aeb09dc923376); the renderer itself is part of the training stack and is not shipped here. What it does with the columns of this set:

• caption_general → the GENERAL: line (the level statement for emotion rows);
• text + words_json + burst_* + dur_s → the timed SCRIPT: — segment durations, pauses, detected bursts with their lengths, and the delivery direction drawn from emo_strength's band (or cfg_neutral for VoiceNet rows);
• cfg_free_text = true → every speech chunk of that script becomes ...;
• frames → the - Tokens: budget; lang → - Language:; ref_codes → the <|audio|> reference slot, or Speaker: <speaker_name> in name mode.

Two rows of this set, rendered by that code (reference-audio mode):

cfg_high, emotion head Intoxication, uid cfgd0ac06bc0eea0a5f4dcch:

<user_inst>
- Reference(s):
<|audio|>
- Instruction:
GENERAL: A voice utterly expressing intoxication, a woozy, slurring voice, intoxicated and untethered, in an altered haze, impossible to hide; reads as intoxication altered states of consciousness
SCRIPT:
(not holding it together at all, breath shallow and fast, the fear plain in the voice, unguarded; completely intoxicated) [4.3 seconds duration] ... [0.5 seconds pause] (keep it overwhelmingly intoxicated) [2.7 seconds duration] ...
- Tokens:
94
- Quality:
None
- Sound Event:
None
- Ambient Sound:
None
- Language:
English
- Text:
(not holding it together at all, breath shallow and fast, the fear plain in the voice, unguarded; completely intoxicated) [4.3 seconds duration] ... [0.5 seconds pause] (keep it overwhelmingly intoxicated) [2.7 seconds duration] ...
</user_inst>

cfg_low, VoiceNet ladder WARM (timbre), uid cfga19aa3876e64686f91c1l — no GENERAL: line, the level sits in the script as cfg_neutral:

- Instruction:
SCRIPT:
[5.5 seconds pause] (slightly cool, and hold it there; otherwise exactly as this voice normally speaks) [1.4 seconds duration] ... [0.8 seconds pause] [3.2 seconds duration] ... [0.3 seconds pause]
- Tokens:
140

A p2_len row renders the same way with the words present in the script. The [n seconds duration] and [n seconds pause] tags come from words_json; the (burst, n seconds) tags from burst_starts / burst_ends / burst_labels; the parenthesised directions are drawn from the band and are not stored in the row.

---

Columns — the 31 columns of cfg/shard-* and p2/shard-*

column	type	meaning
uid	string	pair-row id. cfg + 20-hex pair_id + h/l (role), or p2 + 9-digit pair_id + e/c/x (emox / cut / ext). Unique across the whole set.
family	string	cfg_high, cfg_low, p2_emox, p2_len
src	int8	source group of the training corpus; 0 on every row = synthetic voice profile
voice_key	string	voice-profile id, one of the 500 (anime_000, emolia_c0123, mediathek_…, refvoice_…, k<n>_age<n>_bg<n>); both clips of the pair share it
lang	string	en / de — of the chosen clip; the rejected clip of a cfg / p2_emox pair may be in the other language
text	string	the chosen clip's generation text, verbatim (may contain the generator's own parenthesised burst directions). Not in the prompt when cfg_free_text is true.
text_bursts	string	text with the detected vocal bursts inserted inline as (label); equals text when nothing was detected (73 % of cfg rows, 75 % of p2)
caption_general	string	the level statement. Emotion rows: A voice <band> expressing <emotion>, <prose>; reads as <emotion>. VoiceNet rows: A voice; <clause> is <tag>.
caption_script	string	always empty in this set
caption_tpl_text	string	always empty in this set
caption_tpl	string	provenance key. cfg: cfg|<emo|vn>|<dimension>|<hi_uid>|<lo_uid>. p2: p2|<emox|len>|<emox|cut|ext>|<dimension>
speaker_name	string	the voice's assigned given name (the same mapping as names.csv in the sister set)
spoken_dur_s	float32	end of the last aligned word of the chosen clip, seconds
dur_s	float32	duration of the chosen clip, seconds (generator's pre-encode measurement; frames == round(dur_s · 12.5))
words_json	string	JSON list [{"w","s","e"}, …] — MMS_FA forced alignment of the chosen clip's text, seconds
burst_starts / burst_ends	list<float32>	detected vocal-burst spans in the chosen clip, seconds
burst_labels	list<string>	their class labels (Contented Sigh, Breathy Giggle, …)
in_extreme	bool	legacy flag of the older corpus; always false here (emo_strength carries the band instead)
frames	int32	MOSS frames of the chosen clip
codes	binary	chosen MOSS codes, little-endian uint16, frames × 12 values
rej_frames	int32	MOSS frames of the rejected side
rej_codes	binary	rejected MOSS codes, same layout. cfg / p2_emox: the other clip; cut: a frame-aligned prefix of codes; ext: codes followed by cut_frames frames of the donor
ref_frames	int32	MOSS frames of the reference clip
ref_codes	binary	the reference clip the chosen take was paired with in the training corpus — another take of the same voice; its uid is not carried in these rows
has_ref	bool	always true here
is_val	bool	blake2b(uid) % 1000 == 0 — the training runs' held-out rows (490 in cfg, 373 in p2); shipped, not excluded
cfg_free_text	bool	render the script without words. True on every cfg row and every p2_emox row, false on p2_len
cfg_neutral	string	VoiceNet rows only: the direction stating the ladder tag ("very tense, and hold it there; otherwise exactly as this voice normally speaks"); empty elsewhere
emo_strength	float32, nullable	the emotion band the prompt states, as a percentile: 0.95 / 0.99 for the high or named side, ≤ 0.06 for cfg_low emotion rows, null on VoiceNet rows

cfg/pairs_index.parquet — 237,209 rows, one per base pair

column	type	meaning
pair_id	string	20 hex characters; the row uids are cfg{pair_id}h and cfg{pair_id}l
voice_key	string	the voice
dim	string	emotion head name or VoiceNet code
kind	string	emo (173,116) or vn (64,093)
hi_uid / lo_uid	string	the high and the low clip — uids of laion/laion-voice-profiles-annotated
hi_val / lo_val	float32	the measured value of dim on each clip (emotion intensity, or VoiceNet regression)
hi_frames / lo_frames	int32	their MOSS frames
hi_pct / lo_pct	float32	emo: tie-aware corpus ECDF percentile of the value; vn: the ladder bucket (0–6) as a float

p2/pairs_index.parquet — 368,517 rows, one per shipped row

column	type	meaning
pair_id	string	9 digits; the row uid is p2{pair_id}{mode[0]}
family	string	emox or len (the row's family is p2_ + this)
voice_key, dim	string	the voice; the emotion head the instruction names
ch_uid	string	the chosen clip — uid in laion/laion-voice-profiles-annotated
rj_uid	string	emox: the rejected clip; cut: equal to ch_uid; ext: the donor whose opening frames are appended
ch_pct / rj_pct	float32	percentile of dim on the chosen / rejected clip (len: both the chosen clip's own top-head percentile)
ch_frames / rj_frames	int32	emox: frames of the two clips; cut: rj_frames is the kept prefix length; ext: rj_frames is the number of donor frames appended
mode	string	emox / cut / ext
cut_frames	int32	cut: frames kept; ext: frames appended; emox: 0

---

The join

hi_uid, lo_uid, ch_uid, rj_uid are the uid column of laion/laion-voice-profiles-annotated (index/origin=original/, and the tar member stem under data/origin=original/), of the form <voice>__<block>__<cell>__<lang>.cNNN — e.g. anime_000__E__Affection__D__de.c037. Verified byte-for-byte on the released index (see below). From there you have the MP3, the 205-column annotation, the speaker embedding, and — via the same uid — the rows of laion/laion-voice-profiles-sft and -dpo.

import numpy as np, pyarrow.parquet as pq, pyarrow.dataset as ds

def codes(b, frames, n_vq=12):
    return np.frombuffer(b, dtype="<u2").reshape(frames, n_vq)

row = pq.read_table("cfg/shard-000.parquet").slice(0, 1).to_pylist()[0]
chosen, rejected = codes(row["codes"], row["frames"]), codes(row["rej_codes"], row["rej_frames"])

# which clips are these?  (cfg: also readable from caption_tpl.split("|")[3:5])
idx = pq.read_table("cfg/pairs_index.parquet").to_pandas().set_index("pair_id")
p   = idx.loc[row["uid"][3:-1]]
chosen_uid, rejected_uid = (p.hi_uid, p.lo_uid) if row["uid"].endswith("h") else (p.lo_uid, p.hi_uid)

# the same for p2
row = pq.read_table("p2/shard-000.parquet").slice(0, 1).to_pylist()[0]
p   = pq.read_table("p2/pairs_index.parquet").to_pandas().set_index("pair_id").loc[row["uid"][2:-1]]
chosen_uid, rejected_uid, mode = p.ch_uid, p.rj_uid, p.mode     # rj_uid == ch_uid when mode == "cut"

# audio + full annotation, from the annotated corpus (local snapshot)
ann = ds.dataset("laion-voice-profiles-annotated/index", partitioning="hive", format="parquet")
a   = ann.to_table(filter=ds.field("uid") == chosen_uid).to_pylist()[0]
# tar: data/origin=original/{a['shard']}.tar, members {chosen_uid}.mp3 / .json / .moss.npy / .vclap.npy

uid in the annotated corpus contains dots — do not split on the first one. Its audio_key is not unique across runs; join on uid.

---

MOSS codes

codes, rej_codes, ref_codes are raw little-endian uint16, (frames, 12) after reshape: 12 codebooks × 1024 entries at 12.5 fps, one frame = 12 tokens = 80 ms. There is no 32-token block and no interleaving; the 32 associated with this stack is the codec's num_quantizers, of which the TTS model consumes the first 12. Every cut boundary is a whole frame. The p2 builder drops rejected sides longer than 620 frames (49.6 s); the training corpus caps chosen takes the same way.

These codes are from the corrected tokenisation. The voice-profile corpus once carried codes computed on a half-speed decode (a stereo-to-mono bug that doubled every duration and frame count in the first annotation tree). The frame counts here were compared, clip by clip, with moss_frames of the released annotated index for 1,322 clip references of voice anime_000 (its first three index parts): ratio 1.00 on every one, and dur_s likewise. Nothing in this set comes from the broken tree.

---

The material this is built from

500 synthetic voice profiles, each one reference speaker driven through a fixed matrix of 842 named acting conditions in English and German, keeping every candidate take. Generator laion/moss-tts-local-transformer-4.55b-voice-acting-v2, codec OpenMOSS-Team/MOSS-Audio-Tokenizer-v2, run vprof_base. The full corpus with audio and the complete annotation stack is laion/laion-voice-profiles-annotated; the per-voice LoRAs and reference clips are laion/moss-voice-profile-loras-500. The clip pool for both builds is the voice-profile part of the project's second training corpus (src = 0, 1,199,355 clips) — the same top-3-per-cell takes that make up laion/laion-voice-profiles-sft (1,200,531 rows), minus the corpus's 1-in-1000 held-out rows, joined with the corrected re-annotation. The source clips span every block of the matrix — for the cfg high side, 115,656 come from emotion cells, 88,904 from VoiceNet cells, 22,562 from edge cases, 6,532 from character voices, 2,957 from isolated bursts, 380 sports and 218 explicit cells — because the ranking is by the measured value, not by the cell.

The measured values that rank the clips are the 40 Empathic-Insight emotion intensities and the 57 VoiceNet regressions of the corrected re-annotation (laion/voiceclap-commercial → laion/voicenet-dimension-predictors-commercial), with the percentiles taken from the project's tie-aware ECDF over 132.8 M rows pooled across its corpora. The same caveat as on the sister cards applies: these are model outputs, not human ratings, and the VoiceNet heads were trained on Gemini perceptual estimates (mean r 0.79).

---

Limitations, honestly

• No human has listened to any pair in a controlled study. "High" and "low" are model measurements on model output.
• The high side is only as intense as the corpus gets. These pairs teach the direction of a dimension within a voice's own range; they cannot teach a level the generator never produced. That is exactly the result the cfg run showed.
• Language is not matched within a cfg / p2_emox pair (54.6 % / 59.1 % same-language). The prompt states the chosen clip's language and the words are absent, so the mismatch is not visible in the prompt — but the rejected side may be a German clip against an English prompt.
• The p2_emox mirror rows are looser than the original direction (20.0 % of rows with the rejected side above 0.50 on the named head; 4.6 % at or above 0.90). Filter on rj_pct if you want the strict version.
• p2_len negatives are constructed, not observed, and the ext splice has no crossfade. Its preference accuracy saturated at 1.000 during training; it carries little signal for a model that already handles timing tags.
• The rendered prompts are not shipped, only their ingredients; reproducing them exactly needs the project's renderer. The example strings above are what the trainer saw.
• Speaker identity is weak against the nominal reference, everywhere, as inherited from the generation run (per-voice mean spk_sim 0.18–0.69 in the sister set). Same voice within a pair is guaranteed by voice_key, not by a similarity threshold.

---

What is, and is not, in the sister sets

repository	families	relation to this set
laion/laion-voice-profiles-dpo	emotion, truncation, continuation — 3,451,531 pairs	the vp_* families of the training corpora; none of the four families here
laion/tts-realspeech-dpo-en-de	too_short, too_long on real speech — 3,959,192 pairs	the rs_* families; real recordings, disjoint by construction (src ≠ 0)
this set	cfg_high, cfg_low, p2_emox, p2_len — 842,935 pairs	the remainder

The training corpora were nested unions: dpo_corpus2 (1,853,486, sister families) ⊂ dpo_corpus_cfg (+ cfg, 2,327,904) ⊂ dpo_corpus_p2 (+ p2, 2,696,421). code/mix_cfg.py and code/mix_pairs2.py are the two concatenations. Overlap was checked, not assumed: every uid, chosen_uid, rejected_uid, ref_uid and donor_uid of all 1,922 parquet files of both sister sets (7,410,723 rows) was read, and none of the 237,209 cfg base pairs and none of the 154,564 p2_emox base pairs is a (chosen, rejected) tuple of the sister emotion family (1,064,594 tuples), in either order. What is shared is the pool: all 332,576 / 342,284 source clips appear in the sister set as a chosen take, because both are built from the same top-3 selection, and every p2_len chosen clip also has a truncation and a continuation negative there — cut at a sentence boundary in the 30–85 % window rather than at 50–75 % of the frames, so the pairs differ. No row of this set is a copy of a row there.

---

Verification run on the shipped files

check	result
rows shipped	cfg 474,418 (cfg_high 237,209 · cfg_low 237,209) · p2 368,517 (p2_emox 309,128 · p2_len 59,389) — counted with pyarrow over all 256 shards
languages (of the chosen clip)	cfg en 227,068 / de 247,350 · p2 en 179,649 / de 188,868
uid unique across each config	474,418 / 474,418 · 368,517 / 368,517
src == 0	474,418 / 474,418 · 368,517 / 368,517
voice_key is one of the 500 published profiles	474,418 / 474,418 · 368,517 / 368,517 (500 voices in each config)
every source-clip reference is <voice>__<block>__<cell>__<lang>.cNNN with voice in the 500	1,423,254 / 1,423,254 (cfg, index + caption_tpl) · 737,034 / 737,034 (p2); 0 matches to any real-speech or broadcast uid pattern
every row has an entry in its pairs_index.parquet	474,418 / 474,418 · 368,517 / 368,517
sample join, 200 rows per family: role, voice_key, frames, rej_frames agree with the index	200 / 200 on cfg_high, cfg_low, p2_emox; 82 / 82 on p2_len
code buffer length == frames × 12 × 2 for codes, rej_codes, ref_codes	682 / 682 sampled rows
all codes in 0..1023	682 / 682
abs(frames − round(dur_s × 12.5)) ≤ 1	682 / 682
cut: rej_codes is a frame-aligned prefix of codes	41 / 41 sampled
ext: rej_codes begins with all of codes	41 / 41 sampled
frame tolerance `	f_hi − f_lo
frames and dur_s against the released annotated index (moss_frames, dur_s)	1,322 / 1,322 clip references at ratio 1.00 — the corrected tokenisation
has_ref	true on every row
in_extreme, caption_script, caption_tpl_text	always false / empty / empty, all rows
overlap with laion/laion-voice-profiles-dpo and laion/tts-realspeech-dpo-en-de	7,410,723 sister rows read; 0 identical pairs; 0 / 800 sampled row uids present there
files on the Hub vs. local, HfApi.repo_info(files_metadata=True)	258 / 258 files, size and LFS sha256 identical, 6,509,255,583 bytes

Each check re-derives a property from a quantity other than the one the writer used — frames against dur_s, code length against the declared frame count, the row's frames against the pair index, the pair index against the released annotated index — rather than re-running the builders.

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Licence and attribution

CC-BY-4.0. Credit Christoph Schuhmann and LAION, and the upstream sources listed in laion/laion-voice-profiles-annotated. Every clip referenced here is model output from one of the 500 synthetic profiles; no take is a recording of a person, and no clip, transcript or timeline from the project's real-speech or broadcast corpora is included.

@misc{schuhmann2026voiceprofilesdpocfg,
  title  = {LAION Voice Profiles -- contrastive DPO pairs (CFG + phase 2)},
  author = {Schuhmann, Christoph and LAION},
  year   = {2026},
  url    = {https://huggingface.co/datasets/laion/laion-voice-profiles-dpo-cfg}
}