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).