README.md

---
license: apache-2.0
base_model: DataoceanAI1/dolphi-cn-dialect-small
pipeline_tag: automatic-speech-recognition
library_name: openasr
tags:
  - automatic-speech-recognition
  - speech-to-text
  - openasr
  - oasr
  - dolphin
---

<div align="center">

# Dolphin CN-Dialect Small · OpenASR

**Chinese multi-dialect speech recognition -- a WeNet E-Branchformer (CTC + attention) tuned for Sichuan and 22 regional dialects**

[![License](https://img.shields.io/badge/license-Apache--2.0-2563eb.svg)](https://huggingface.co/DataoceanAI1/dolphi-cn-dialect-small/blob/main/README.md)
[![Format](https://img.shields.io/badge/format-.oasr-7c3aed.svg)](https://github.com/QuintinShaw/openasr)
[![Runtime](https://img.shields.io/badge/runtime-OpenASR-111827.svg)](https://openasr.org)
[![Base model](https://img.shields.io/badge/base-dolphi--cn--dialect--small-f59e0b.svg)](https://huggingface.co/DataoceanAI1/dolphi-cn-dialect-small)

Native speech-to-text in the **[OpenASR](https://github.com/QuintinShaw/openasr)** runtime —
engineered for peak performance on CPU & GPU, **no Python at inference time**.

</div>

---

## ✨ Highlights

- 🀄 **22 Chinese dialects** — a WeNet E-Branchformer tuned for regional Mandarin, with a standout Sichuan (川话) strength across Wu, Cantonese, Minnan, Shanghainese and more
- 🎯 **Dialect-first accuracy** — a reported ~38% relative gain on dialect recognition and ~16% lower CER versus the base Dolphin, without giving up standard Mandarin
- 🧩 **Joint CTC + attention** — an E-Branchformer encoder with a Transformer decoder and CTC/attention rescoring that OpenASR runs bit-exact against its golden reference
- 🐬 **Chinese-focused, char-level** — a compact `small.cn` checkpoint over a character vocabulary, purpose-built for zh audio including heavy accents and code-mixed speech
- 🦀 **Native in OpenASR** — `.oasr` packs run with no Python at inference, engineered for peak performance on CPU & GPU

## 🚀 Quickstart

```bash
# 1. Install the OpenASR CLI  ·  https://openasr.org
# 2. Pull a build (pick a quant — see the table below)
openasr pull dolphin-cn-dialect-small:fp16

# 3. Transcribe
openasr transcribe audio.wav --model dolphin-cn-dialect-small
```

All builds for this model:

```bash
openasr pull dolphin-cn-dialect-small:fp16
openasr pull dolphin-cn-dialect-small:q8
openasr pull dolphin-cn-dialect-small:q4
```

## 📦 Available builds

| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | ΔCER vs fp16 |
|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
| fp16 | `dolphin-cn-dialect-small-fp16.oasr` | 860 MB | 2.37 GB | 0.32× | 0.26× | 0.0% |
| q8_0 | `dolphin-cn-dialect-small-q8_0.oasr` | 494 MB | 2.88 GB | 0.24× | 0.12× | 0.0% |
| q4_k | `dolphin-cn-dialect-small-q4_k.oasr` | 298 MB | 2.65 GB | 0.26× | 0.11× | 4.5% |

<sub>RTF = real-time factor on a 2.38s in-language Sichuan-dialect (川话) clip (**lower is faster**); RAM peak measured per pack
in an isolated subprocess. ΔCER compares each quantized build's Sichuan-clip transcript to this model's
fp16 Sichuan-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.</sub>

## 🧠 About Dolphin CN-Dialect Small

Dolphin CN-Dialect Small is a **Chinese multi-dialect** speech-recognition model from
**DataoceanAI**, built on the **Dolphin / WeNet** recipe as an **E-Branchformer encoder +
Transformer decoder** trained with a **joint CTC + attention** objective (the `small.cn`
checkpoint over a character vocabulary). It specializes in regional Mandarin — a standout
**Sichuan (川话)** capability alongside 22 Chinese dialects such as Wu, Cantonese, Minnan and
Shanghainese — while keeping strong standard-Mandarin transcription (the card reports a large
relative gain on dialect recognition and a meaningful CER reduction over the base Dolphin).
This OpenASR repo repackages the weights as `.oasr` packs that run natively in the OpenASR
runtime — no Python at inference, all decoding local. OpenASR decodes it with CTC beam search
plus attention rescoring and verified the transcript bit-exact against a golden reference on a
Sichuan-dialect clip. It ships in **fp16** (maximum fidelity, recommended), **q8_0**, and **q4_k** builds.

## ⚙️ How these packs were made

Converted from [DataoceanAI1/dolphi-cn-dialect-small](https://huggingface.co/DataoceanAI1/dolphi-cn-dialect-small) with the OpenASR importer:

```bash
openasr model-pack import dolphin <src> <out>.oasr \
  --package-id dolphin-cn-dialect-small --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](https://huggingface.co/DataoceanAI1/dolphi-cn-dialect-small/blob/main/README.md)). 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 Small**, created and open-sourced by
**DataoceanAI** ([DataoceanAI1/dolphi-cn-dialect-small](https://huggingface.co/DataoceanAI1/dolphi-cn-dialect-small)).
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

- 🦀 **OpenASR** — <https://github.com/QuintinShaw/openasr>
- 🌐 **Website** — <https://openasr.org>
- 🤗 **Upstream model** — [DataoceanAI1/dolphi-cn-dialect-small](https://huggingface.co/DataoceanAI1/dolphi-cn-dialect-small)