Dolphin CN-Dialect Base
Chinese multi-dialect speech recognition, base tier -- a compact 140M WeNet E-Branchformer (CTC + attention) for Sichuan and 22 regional dialects
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let's line up this week's release plan,
the beta build can ship on Friday,
then loop in design and QA,
Why it fits
Dolphin CN-Dialect Base is the 140M "base" tier of DataoceanAI's Chinese multi-dialect
speech-recognition line, built on the same Dolphin / WeNet recipe as the larger
Dolphin CN-Dialect Small: an E-Branchformer encoder + Transformer decoder trained with a
joint CTC + attention objective over a mixed character/BPE vocabulary. It covers the same
Sichuan (川话)-forward set of 22 Chinese dialects (Wu, Cantonese, Minnan, Shanghainese and
more) as its small.cn sibling, but at roughly a third of the encoder/decoder width (512 vs 768
d_model, 6 vs 12 layers) -- a smaller RAM/CPU footprint for deployments where the small tier's
accuracy headroom is not needed. Unlike small.cn, this base.cn checkpoint does not ship a
trained hotword deep-biasing module. This OpenASR repo repackages the weights as .oasr packs
that run natively in the OpenASR runtime -- no Python at inference, all decoding local. It ships
in fp16 (maximum fidelity, recommended), q8_0, and q4_k builds.
Note: this model does not emit punctuation. Its upstream training corpus is transcribed
without punctuation marks, so the decoder never predicts a punctuation token -- there is no
setting to enable it. Transcripts are plain, unpunctuated text by design.
What you can do with it
- 🀄 22 Chinese dialects, base tier — the same WeNet E-Branchformer dialect coverage as Dolphin CN-Dialect Small (Sichuan/川话, Wu, Cantonese, Minnan, Shanghainese and more), at a fraction of the size
- 🪶 140M parameters — roughly a third the width of the
small.cncheckpoint (512 vs 768 d_model, 6 vs 12 layers), for tighter RAM and faster CPU decode when the small tier is overkill - 🧩 Joint CTC + attention — the same E-Branchformer encoder + Transformer decoder recipe with CTC/attention rescoring, verified against a shape-derived runtime contract shared with the rest of the Dolphin family
- 🐬 Chinese-focused, mixed char/BPE vocab — a character vocabulary for Chinese with SentencePiece word-piece tokens for code-switched English, purpose-built for zh audio including heavy accents
- 🦀 Native in OpenASR —
.oasrpacks run with no Python at inference, engineered for peak performance on CPU & GPU
Other models
Developer details CLI commands, file hashes, and per-quant downloads — for scripting and verification.
Model facts
Usage
These are CLI / local-server examples. The desktop app runs this model without typing a command — use Install above.
$ openasr pull dolphin-cn-dialect-base:fp16 ↓ dolphin-cn-dialect-base.oasr 213.9 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/dolphin-cn-dialect-base/fp16/dolphin-cn-dialect-base-fp16.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/dolphin-cn-dialect-base/fp16/dolphin-cn-dialect-base-fp16.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=dolphin-cn-dialect-base · 0 bytes will leave this host
from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8080/v1", api_key="local") audio = open("meeting.wav", "rb") text = client.audio.transcriptions.create(model="dolphin-cn-dialect-base", file=audio)