Audio-Text-to-Text
Transformers
Safetensors
English
qwen3_omni_moe
text-to-audio
audio
speech
multimodal
reasoning
Instructions to use brgroup/BR-Voice-Reasoner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brgroup/BR-Voice-Reasoner with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("brgroup/BR-Voice-Reasoner") model = AutoModelForMultimodalLM.from_pretrained("brgroup/BR-Voice-Reasoner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download evaluation_protocol.json from brgroup/BR-Voice-Reasoner: direct link, hf CLI and curl.
- Browser
- Download file 2.78 kB
-
https://huggingface.co/brgroup/BR-Voice-Reasoner/resolve/main/evaluation_protocol.json
- Command line
-
hf download hf://brgroup/BR-Voice-Reasoner/evaluation_protocol.json
-
curl -L -o evaluation_protocol.json https://huggingface.co/brgroup/BR-Voice-Reasoner/resolve/main/evaluation_protocol.json
2.78 kB
| { | |
| "schema": "br-voice-reasoner-evaluation-protocol.v1", | |
| "evaluation_date_utc": "2026-09-02", | |
| "benchmark": { | |
| "name": "VoiceBench", | |
| "upstream_repository": "https://github.com/matthewcym/VoiceBench", | |
| "snapshot_identity": "SHA256 manifest", | |
| "git_revision": null, | |
| "revision_note": "The evaluated local snapshot did not retain Git metadata; release-relevant files are identified by SHA256." | |
| }, | |
| "subset_samples": { | |
| "wildvoice": 1000, | |
| "bbh": 1000, | |
| "alpacaeval_full": 636, | |
| "mmsu": 3074, | |
| "openbookqa": 455, | |
| "ifeval": 345, | |
| "advbench": 520, | |
| "commoneval": 200, | |
| "sdqa_usa": 553, | |
| "total": 7783 | |
| }, | |
| "generation": { | |
| "reasoning_enabled": true, | |
| "temperature": 0.6, | |
| "top_p": 0.95, | |
| "top_k": 20, | |
| "maximum_tokens": 16384, | |
| "seed_policy": "A deterministic per-sample primary seed is derived from the input and base seed 1234.", | |
| "empty_final_policy": { | |
| "primary_attempts": 3, | |
| "maximum_consecutive_fallback_seeds": 8, | |
| "selection": "First non-empty visible final answer; no ranking or best-of selection.", | |
| "samples_entering_seed_fallback": 5, | |
| "samples_recovered": 4, | |
| "samples_exhausted": 1, | |
| "exhausted_record": "No answer.", | |
| "exhausted_scoring": "incorrect" | |
| } | |
| }, | |
| "model_judge": { | |
| "requested_model": "openai/gpt-4o-mini", | |
| "model_kind": "alias", | |
| "accessed_utc": "2026-09-02", | |
| "gateway": "OpenRouter", | |
| "provider_only": "OpenAI", | |
| "allow_provider_fallbacks": false, | |
| "judgments_per_sample": 3, | |
| "vote_mode": "three separate requests without a seed", | |
| "temperature": 0.5, | |
| "top_p": 0.95, | |
| "maximum_tokens": 1024 | |
| }, | |
| "aggregation": "Open-ended ratings are normalized to 0-100. Overall is the arithmetic mean of nine 0-100 subset scores.", | |
| "sha256": { | |
| "main.py": "cb7c86d7149b76d50e3316d09a377fb7cfa6549855ad2922f86b7dd980416f3e", | |
| "evaluate.py": "0b610247b1373f2c256a4f183c2dfcbe7c562b576823cf4ce1396bf036ba1f42", | |
| "src/models/qwen3_omni_thinking.py": "f44bcbc3c9b866c13b4db33ff156026dcf136f7fcfbd223ea006e600986543d8", | |
| "qwen3omni_thinking/run_voicebench.sh": "4f040e0f695a12ee6a9fe4e1164c6fe3e57582160d5afca99d975f6ce7a7226c", | |
| "qwen3omni_thinking/audit_outputs.py": "3ae9335dff1d568d19dc395bdf2b352b6a81685a9128edccdeca3882bdee7fb0", | |
| "qwen3omni_thinking/score_local.sh": "332d701640ee1d76b3d7566e7250c55055546b3aa8186cd443531886635511ac", | |
| "qwen3omni_thinking/score_thinking_mcq.py": "505018a7ae166f71f1a1d19b99887f69adc7fc802f91ceca930dbf2349f858eb", | |
| "qwen3omni_thinking/api_judge_openrouter.py": "5b721566c1095e5dcf83b909f6ea667d127aed2533315b73f0819d168071ec33", | |
| "qwen3omni_thinking/repro_utils.py": "9b36582a40c858f9ae6f5c64f7230dd7cf7db2e868d4f2bd77c446e70ea1a046" | |
| } | |
| } | |