MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across 50+ languages, single-pass inference on audio recordings up to 90 minutes long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as [S01], [S02], and beyond. 2026-07-22: The subtitle Web UI now supports both Simplified Chinese and English. 2026-07-14: MOSS-Transcribe-Diarize won first place in the 2nd MLC-SLM Challenge…
Open weights
apache-2.0
909M parameters
131,072 tokens
transformers
A small DuplexJev connector: the Whisper-small encoder (frozen) feeds a frozen Qwen3-4B-2507, and every typed question is read as a single-token distribution over its options, with zero decode steps and many questions per forward pass. Trained with the same recipe as the Qwen3-32B connectors released with the paper. Leaderboard scores (see the GitHub README): main language (mean of qa100, ZJU-ML, Easy-Turn) 53.5, paralinguistics (mean of gender, emotion) 43.0. Paper protocol: the evaluation prompts of the paper (single option order, one H200). duplexjev defaults: the package's own prompts with lang matched to the clip. Spoken QA with a small LLM is bounded by the LLM itself (see the…
Open weights
apache-2.0
30M parameters
transformers
A small DuplexJev connector: the Whisper-small encoder (frozen) feeds a frozen SmolLM3-3B, and every typed question is read as a single-token distribution over its options, with zero decode steps and many questions per forward pass. Trained with the same recipe as the Qwen3-32B connectors released with the paper. The connector was trained with SmolLM3's no-think format (an empty block before the answer). The tokenizer in this repository carries that chat template; SmolLM3's default template adds a long system prompt with today's date and would not match. Leaderboard scores (see the GitHub README): main language (mean of qa100, ZJU-ML, Easy-Turn) 37.4, paralinguistics (mean of gender…
Open weights
apache-2.0
29M parameters
transformers
Native audio in, structured decisions out. Aural One adapts Gemma 4 E2B to score supplied choices from a recording, a written state, and named questions. One model handles the audio and the decision; its inference path does not require speech-to-text or a separate sound classifier. This v0.1.0 research preview publishes a frozen adapter and 54.3 million updated acoustic/projection weights. The pinned Gemma 4 E2B base is downloaded separately. The language model was frozen during this final training stage. These numbers have different test scopes. The full evaluation report covers the frozen reference, listener-vote cross-entropy and calibration, same-words contrasts, sound events…
Open weights
apache-2.0
transformers
Paper: arXiv:2610.02638 · Code: github.com/adventists-ai/duplexjev · Research note: teaching a speech-decision LLM to hear stress DuplexJev-32B-Turn that also hears stress and pauses. "I didn't say she took the money" and "I didn't say she took the money" are the same words. This model can tell them apart: it answers which word is stressed, is any word stressed at all, and where the speaker pauses, in Mandarin and English. Spoken QA, gender, emotion and turn-taking stay at the 32B-Turn level. Same size, same serving stack, same prompt format as the other DuplexJev models. DuplexJev reads typed, closed-set decisions from speech as a single-token distribution of the LLM: zero decode steps…
Open weights
cc-by-nc-4.0
vllm
Paper: arXiv:2610.02638 · Code: github.com/adventists-ai/duplexjev · Research note: teaching a speech-decision LLM to hear stress The DuplexJev model that hears the most beyond the words. On top of everything DuplexJev-32B-Turn and DuplexJev-32B-Stress do (spoken QA, gender, emotion, turn-taking, stress and pauses), it also hears laughter, breathing, coughs and sighs, notices how someone is talking (whispering, shouting, very fast or slow), and can tell, roughly as a person would, whether two clips are the same speaker. Mandarin and English. DuplexJev reads typed, closed-set decisions from speech as a single-token distribution of the LLM: zero decode steps, many questions about the same…
Open weights
cc-by-nc-4.0
vllm
