cstr/dolphin-cn-dialect-small-streaming-GGUF

Dolphin CN-Dialect small (streaming) — GGUF

GGUF conversions of `DataoceanAI1/dolphin-cn-dialect-small-streaming` for [CrispASR](https://github.com/CrispStrobe/CrispASR) (--backend dolphin, or -m dolphin to auto-download).

Dolphin CN-Dialect, from Dataocean AI and Tsinghua University, recognises Mandarin plus Chinese dialects. It is a WeNet model: an E-Branchformer encoder, a Transformer attention decoder and a CTC head. CrispASR decodes it the way upstream dolphin/transcribe.py does. It runs a CTC prefix beam search (beam 10) and rescores the n-best with the attention decoder under the prompt [sos, <lang>, <region>, <asr>, <notimestamp>]. The decoder predicts the language and region, or -l zh / -l zh-SICHUAN forces them.

Files

Verification

The F16 file was checked stage by stage against the upstream dolphin package (float32, CPU, dither 0) with crispasr-diff dolphin. The checked stages are the fbank, the subsampling output, every encoder block, the encoder output, the CTC log-probs and the final text. Every stage passed, and the transcripts are identical to upstream on a Mandarin clip and on jfk.wav.

Quantised transcripts against F16 on 15 in-domain clips: Mandarin, Sichuan, Tianjin and Henan dialects, Cantonese, and zh-en code-switching.

Encoder-output cosines are less smooth than the transcripts suggest. A handful of frames sit near decision boundaries, so the worst-frame cosine moves a lot under quantisation while the mean stays at ≥ 0.996 for Q80. English (out of domain for this model) drifts more at Q4K.

Licence

Apache-2.0, as upstream.