Humaneness Voice 1.1 Alpha

Publication status

• Small: COMPLETE; step 6,109/6,109; validation loss 4.204355222838266
• Large: COMPLETE; step 3,055/3,055; validation loss 4.164423295429775

1656 / 1656 predictions published. All predictions and scores are available.

Small weights and code · Large weights and code · Audio, exact prompts and metrics · Previous Small grid.

What the models do and how they work

A semantic transformer plans speech one 80-ms frame at a time. A local Talker predicts 12 residual codec indices inside each frame; MOSS Audio Tokenizer v2 turns those indices into sound. That is 12.5 frames/s × 12 codes/frame = 150 indices/s, not 12 tokens per second. Small has 596.05M semantic parameters, 112.76M active Talker/bridge parameters (708.81M active total), a 1,024-dimensional semantic state and a 2,560-dimensional local state. An additional stored 6.67M score conditioner remains inactive. Large has about 4.13B parameters: 4.02B semantic and 107.52M local Talker/audio parameters, both with 2,560-dimensional states.

This is full-parameter continuation, not LoRA training or a fresh pretraining run. Small starts at Real + Gemini before its Rank-64 adapter; Large starts at the SFT4 Gemini continuation. Both see the same shuffled mix: original Gemini DE/EN once, five-language extension twice, Gaston five times and ElevenLabs six times. This is 390,914 presentations, about 3,285 presentation-hours. Half use distinct same-speaker 5–15-second references, half learned names. Three GENERAL/SCRIPT views and 10% caption dropout preserve flexible prompting; Gaston always keeps French Accent in GENERAL.

Matched comparison and interpretation

The original 70 cases and 35 natural/organic variants preserve the previous exact prompts, transcripts, seeds and 12-second reference excerpts. The earlier 40-emotion, 20-casual and 10-assistant cases are also retained. New Gaston/ElevenLabs scripts are separate. Their complete transcripts were checked against training targets, but this is not a speaker-disjoint benchmark. Some reference recordings come from training material. No best-of-N or take cherry-picking is used. Historical Real+Gemini/Ref15 predictions are reused only where the weights and input match; the rounded merged previous default is regenerated.

Parakeet v3 WER counts transcript errors (lower is better). Orange 128-D timbre cosine estimates reference similarity (higher is better, not verified identity). Frozen Humaneness Ears Medium predicts emotion intensity, genuineness, burst blending, Audiobox Content Enjoyment and Production Quality. These are model-estimated proxies, not human MOS or direct original Audiobox evaluations. Mean ± standard deviation is across clips; WER is word-weighted. Emotion is blank for cases without a single taxonomy target. No-reference cases never secretly receive an audio clip or a name. Compare the same row and conditioning subset; the 14-case checkpoint loss does not establish universal quality. Sentence-level timing alignment and exact burst realization are not claimed here. Gaston and Eleven-prefixed names are new to Alpha: name-only older-model comparisons are therefore out of distribution, and reference-conditioned rows are the fairer cloning comparison. The original Small S10 used at most 2.96-second references in training: its 12-second-reference column is out of its training range. Ref15 is the fairer long-reference control.

Prompting and new voice examples

Only literal spoken words go into Text. GENERAL/SCRIPT directions, round-parenthesis delivery/burst cues and square-bracket durations/pauses go into Instruction. A name is a separate speaker condition, not a spoken word. Gaston has friendly French-accented English/German compliments, each with name-only and reference-conditioned takes. ElevenLabs cases use four voice slots with new anger, fear and elation scenes. Listen to the shared reference to judge voice match. For name-only rows the audition reference below is NOT supplied to the model; for old no-identity rows neither a reference nor a name is supplied.