---

license: apache-2.0

base_model: OpenMOSS-Team/MOSS-Transcribe-Diarize

language:

• zh
• en

tags:

• automatic-speech-recognition
• speaker-diarization
• timestamps
• zh-tw
• traditional-chinese
• ggml
• gguf
• rapidspeech
• on-device

pipeline_tag: automatic-speech-recognition

---

MOSS-Transcribe-Diarize GGUF weights

Status (2026-07-23): this repo serves a purification-first pipeline — the

base OpenMOSS-Team/MOSS-Transcribe-Diarize

(0.9B, Whisper-medium encoder + Qwen3-0.6B decoder, Apache-2.0), **not

fine-tuned**, ported to C++/ggml and byte-verified against the genuine PyTorch

reference before any optimization. Full writeup:

vieenrose/distil-vibevoice-asr.

Files

| file | role |

|---|---|

| moss-transcribe-base-q4mix-v2.gguf | deployed weights (759 MB) — mixed q4: every encoder/adaptor/decoder linear at Q4_K, token_embd at f16, norms/biases f32. Decoder tensors come from a silence-robust QAT checkpoint (see below); encoder/adaptor/token_embd are numerically identical to the plain base conversion |

| moss-transcribe-base-q8mix.gguf | higher-fidelity option (1.55 GB) — token_embd + full Qwen3 decoder at f16 (both measured Q8_0-collapse-sensitive), encoder + adaptor Q8_0. No silence QAT: its behavior on fully silent input has not been validated the way v2 has |

| moss-transcribe-base-f32.gguf | the byte-identity GATE reference (3.64 GB) — converted from the official checkpoint, f32, no fine-tuning |

| campplus.gguf | CAM++ speaker embedding model, used for cross-window speaker linking in the demo |

Why q4mix-v2 (silence robustness)

At f32 the model's EOS-on-silence decision has a thin margin (~+1.35 logits vs

15–17 on speech), so any q4 quantization noise could flip it: the previous

q4mix free-ran marker loops ([0.00][S01][0.06]…) to the token budget on

silent / unvoiced / near-silent audio. v2 fixes this in the weights — a

minimal-perturbation QAT that trains only the silence decision class (silence

negatives, garbage-recovery prefixes, quiet-lead-in patience composites,

attenuated quiet-speech positives) with an L2 trust region to the base

weights. Release validation vs the previous q4mix, identical harness:

• silent input → empty transcript in seconds (this is the correct

no-speech result, not an error);

• zh 5-min golden agreement 89.3 → 96.7;
• 5-meeting WER and speaker accuracy statistically unchanged;
• quiet-speech sensitivity preserved (−40 dB attenuated speech still

transcribed);

• en single-pass golden −1.5 pt (97.2 vs 98.7) is the one known cost.

Engine and usage

Engine: vieenrose/RapidSpeech.cpp

branch moss-pure, vendoring localai-org/moss-transcribe.cpp

(MIT) unmodified. Live demo:

Luigi/moss-transcribe-diarize-cpp.

Recommended flags: MT_KV_F16=1 MT_KV_EVICT_S=45 (near-lossless, large decode

speedup on long audio). Integration details:

integration note.

Removed files (2026-07-23)

The abandoned fine-tuned zh-TW lineage (moss-td-zhtw-v5kl…v71-*), the

early base conversions (moss-td-base-*), the silence-fragile q4mix v1, and

the unused campplus-cn-common.gguf were deleted from the tip of this repo.

The fine-tuned line was measured to be over-specialised to its training domain

and structurally fragile relative to the base model (structural-token logit

margin 0.98 vs the base's 4.90 — quantization noise could flip marker

decisions), which is why the project pivoted to the purified base. All removed

files remain downloadable from this repo's git history (Files → History →

pick a pre-2026-07-23 revision).