Dolphin CN-Dialect Base · OpenASR
Chinese multi-dialect speech recognition, base tier -- a compact 140M WeNet E-Branchformer (CTC + attention) for Sichuan and 22 regional dialects
Native speech-to-text in the
OpenASR runtime —
engineered for peak performance on CPU & GPU,
no Python at inference time.
✨ Highlights
- 🀄 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.cn checkpoint (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 —
.oasr packs run with no Python at inference, engineered for peak performance on CPU & GPU
🚀 Quickstart
1# 1. Install the OpenASR CLI · https://openasr.org
2# 2. Pull a build (pick a quant — see the table below)
3openasr pull dolphin-cn-dialect-base:fp16
4
5# 3. Transcribe
6openasr transcribe audio.wav --model dolphin-cn-dialect-base
All builds for this model:
1openasr pull dolphin-cn-dialect-base:fp16
2openasr pull dolphin-cn-dialect-base:q8
3openasr pull dolphin-cn-dialect-base:q4
📦 Available builds
| Quant | File (.oasr) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | ΔWER vs fp16 |
|---|
| fp16 | dolphin-cn-dialect-base-fp16.oasr | 224 MB | 1.04 GB | 0.13× | 0.05× | 0.0% |
| q8_0 | dolphin-cn-dialect-base-q8_0.oasr | 127 MB | 1.06 GB | 0.09× | 0.05× | 4.5% |
| q4_k | dolphin-cn-dialect-base-q4_k.oasr | 101 MB | 1.03 GB | n/a | n/a | n/a |
RTF = real-time factor on the shared 11s JFK clip (out-of-distribution English, drift signal only) plus an in-language Mandarin sanity clip (lower is faster); RAM peak measured per pack
in an isolated subprocess. ΔWER compares each quantized build's JFK + zh sanity clip transcript to this model's
fp16 JFK + zh sanity clip transcript, so it measures quantization drift rather than absolute recognition accuracy.
fp16 is the recommended default — near-reference quality at a fraction of the
footprint.
🧠 About Dolphin CN-Dialect Base
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.
⚙️ How these packs were made
Converted from
DataoceanAI/dolphin-cn-dialect-base with the OpenASR importer:
1openasr model-pack import dolphin <src> <out>.oasr \
2 --package-id dolphin-cn-dialect-base --quantization {fp16,q8-0,q4-k}
The .oasr container is GGUF-backed; packs use zero-copy mmap weight binding and graph
buffer reuse to keep peak memory low.
⚖️ License
These packs
inherit the upstream model's license: Apache-2.0
(
source). OpenASR packaging retains the upstream copyright and
NOTICE; the only modifications are format conversion and quantization.
🙏 Acknowledgements
This pack is a redistribution of
Dolphin CN-Dialect Base (
base.cn), created and
open-sourced by
DataoceanAI
(
DataoceanAI/dolphin-cn-dialect-base).
All credit for the original architecture, training, and weights belongs to the authors; the
license is inherited from and identical to the upstream model (Apache-2.0). The model builds on
the
Dolphin multilingual ASR project and the
WeNet E-Branchformer / joint CTC-attention
recipe -- thank you to the Dolphin and WeNet teams and to DataoceanAI for releasing their work
openly. OpenASR only performs format conversion, quantization, runtime verification, and
local-inference adaptation.
🔗 Links
