laion/Humane-Voice-Large-Alpha-1.4

Humane Voice Large Alpha 1.4

A rank-128 reinforcement-learning adapter for expressive multilingual text-to-speech. This release adds the completed 1,452-update RL adapter to Humaneness Voice Large 1.3 Alpha. It is not a standalone model and does not duplicate the base weights.

Open the live Alpha comparison demo to hear the same 70 cases from the 1.2 SFT baseline, 1.3 SFT baseline and this completed RL adapter side by side, with aggregate quality and emotion tables. All 215 players, including five speaker references, are publicly accessible.

The legacy comparison is retained as a secondary link. Its historical “1.3 + RL” / green FINAL RL column refers to the adapter released here. The separately planned 20/40/40 continuation will be an additional comparison column after completion, not a silent replacement of these released weights.

What changed?

The frozen Large 1.3 model was trained with a new attention LoRA to favor pleasant speech, source-emotion fidelity and natural-sounding delivery. Rewards combine 33% Content Enjoyment, 33% matching the source audio's two strongest emotions, and 34% genuineness. Both reference-audio and prompt-only generations were trained. This version does not include the later requested 20/40/40 continuation; that is a separate experiment and checkpoint.

The policy is hierarchical: a roughly 4B semantic transformer advances once per 80-ms frame, and a local Talker predicts twelve codec indices within that frame. Twelve codebooks at 12.5 frames/s means 150 codec indices per second, not twelve indices per second. The external MOSS v2 codec reconstructs 48-kHz mono audio. See the pinned base model for the full architecture and SFT history.

Matched evaluation

All 70 fixed cases were retained: 35 scenes, five languages, with and without the same speaker reference, using the same literal text, directing prompts, seeds and frame budgets as the SFT baseline. No best-of-N selection, edited waveforms or discarded poor outputs. Scores are computed from the actual MP3s.

Model	Reference	N	Content Enjoyment ↑	Genuineness ↑	Mean per-clip WER ↓
Large 1.3 SFT	No	35	6.274	4.257	4.358%
Alpha 1.4 adapter	No	35	6.341	4.323	3.689%
Large 1.3 SFT	Yes	35	6.243	4.143	2.118%
Alpha 1.4 adapter	Yes	35	6.314	4.276	1.797%

These are small improvements in learned proxy scores, not human MOS or a statistical-significance claim. EARS Medium estimates CE and genuineness; Parakeet v3 supplies ASR for WER. WER is the arithmetic mean of per-clip error rates, not a corpus token-weighted rate. See aggregate statistics for sample standard deviations and run statistics for training cost and QA. Group learning curves and optimizer-step LR/gradient traces are included. No certified speaker-similarity or emotion-target ground truth is claimed on these scripted listening cases.

Install and generate

The tested stack used Python 3.13, PyTorch 2.9.1/CUDA 12.8 and Transformers 5.14.1 on GH200. A CUDA GPU is required for the tested path. On another server, install the appropriate CUDA PyTorch build first, then the remaining requirements. The model repository uses reviewed remote model code; trust_remote_code=True is necessary for the custom MOSS architecture. Keep caches outside a quota-limited home directory. The example downloads the pinned base revision, not its possibly-changing main branch.

hf download laion/Humane-Voice-Large-Alpha-1.4 --local-dir humane-alpha14
cd humane-alpha14
pip install -r requirements.txt
python infer.py --language en --seconds 7 --seed 777 \
  --text 'I really thought you had forgotten. This is such a wonderful surprise.' \
  --prompt 'GENERAL: warm, spontaneous, quietly delighted.\nSCRIPT:\n(warm relief, 3.0 seconds) I really thought you had forgotten.\n(brighter gratitude, 4.0 seconds) This is such a wonderful surprise.' \
  --output example.wav

For speaker conditioning add --reference-wav clean-reference.wav. Use a clean single-speaker recording at least 5.04 seconds long; the loader preserves up to 14.96 seconds, not the historical 2.96-second limit. Spoken words are supplied separately via --text; GENERAL/SCRIPT contains the performance directions and matching text, with round parentheses for inline directions and optional duration controls. Reference matching and exact timing are not guaranteed. Directions are usually English; spoken text can use any listed language. A registered fictional alias from the base catalog can be supplied via --voice-name instead of audio, but a good reference is preferred.

Important adapter format

This is not PEFT format. Do not feed adapter.safetensors directly to PeftModel.from_pretrained. Our loader validates every tensor name and shape, then inserts FP32 a/b residuals into semantic q/k/v/o and local-Talker c_attn/c_proj attention projections. Rank=alpha=128, residual scaling=1.0. The frozen base uses BF16 for deployment; adapter multiplication remains FP32. The published listening evaluation used this same unmerged deployment path. Deployment uses temperature 1.0, top-p 0.95, top-k 50 and repetition penalty 1, with a 625-frame ceiling in the fixed comparison. Training rollouts instead used full-support temperature 0.7 sampling, as required by likelihood replay. The adapter SHA-256 is c26031b9e1932ae0e76b29ec894b49bb6f719a9ff997cf733eae8bbf443f9fb2.

import infer
model, processor, base_api = infer.load(device='cuda:0')
base_api.load_codec(processor, 'OpenMOSS-Team/MOSS-Audio-Tokenizer-v2')
# base_api.build_user / generate / decode then work with the adapted model.

Training and reproducibility

See TRAINING.md for the objective, exact hyperparameters, prepared SQLite schema, source filtering, generic distributed launch and resume requirements. The portable trainer and numerical objective implementations are in training/. Run python -m unittest discover -s tests -v for CPU adapter-shape/integrity and objective checks; see VALIDATION.md for the allocated-GPU canary result.

The run used a curated mixture of real and synthetic speech across English, German, French, Dutch and Spanish, with equal language/source/conditioning representation. Provider-identifying metadata, local paths, raw data and input identifiers are intentionally omitted. Consequently the supplied code reproduces the method with compatible prepared inputs, not a byte-identical input map or optimizer/RNG resume of the original private run. No optimizer pickle is distributed. The complete original optimizer states remain preserved locally.

License, limitations and responsible use

New adapter weights and release materials: CC BY 4.0; see LICENSE and NOTICE.md. The base, codec, dependencies and source recordings retain their own upstream terms. This does not grant permission to impersonate people or to use someone else's voice without necessary consent.

This alpha release is intended primarily for research and responsibly licensed dataset augmentation. Assess content, identity, pronunciation, bias and audio quality before any downstream deployment. Do not use it for deceptive impersonation, fraud or misleading attributed recordings. LAION contributors do not endorse generated statements or assume responsibility for downstream use. These are usage recommendations and safety guidance, not additional restrictions that replace the CC BY 4.0 license.