publish dolphin-small OpenASR packs

.gitattributes

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+*.oasr filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: DataoceanAI/dolphin-small
+pipeline_tag: automatic-speech-recognition
+library_name: openasr
+tags:
+  - automatic-speech-recognition
+  - speech-to-text
+  - openasr
+  - oasr
+  - dolphin-small
+---
+
+<div align="center">
+
+# Dolphin Small · OpenASR
+
+**Multilingual speech recognition across 40 languages -- a WeNet/ESPnet E-Branchformer (CTC + attention) covering South/Southeast/Central Asian and Chinese-dialect speech**
+
+[![License](https://img.shields.io/badge/license-Apache--2.0-2563eb.svg)](https://huggingface.co/DataoceanAI/dolphin-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-dolphin--small-f59e0b.svg)](https://huggingface.co/DataoceanAI/dolphin-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
+
+- 🌏 **40 languages, one checkpoint** — a WeNet/ESPnet E-Branchformer spanning South Asian (Hindi, Bengali, Urdu...), Southeast Asian (Vietnamese, Thai, Indonesian...), Central Asian/Turkic (Kazakh, Uzbek, Azerbaijani...), and Chinese/Cantonese speech, with per-utterance `<lang><region>` prompting
+- 🧩 **Joint CTC + attention** — an E-Branchformer encoder with a Transformer decoder and CTC/attention rescoring, verified against a shape-derived runtime contract shared with the rest of the Dolphin family
+- 🐬 **SentencePiece BPE vocab** — a shared subword vocabulary across all 40 languages (distinct from the cn-dialect family's fixed character vocab), suited to code-mixed and cross-lingual speech
+- 🪶 **372M parameters, `small` tier** — the larger of the two multilingual Dolphin sizes (paired with the more compact `dolphin-base`)
+- 🦀 **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-small:fp16
+
+# 3. Transcribe
+openasr transcribe audio.wav --model dolphin-small
+```
+
+All builds for this model:
+
+```bash
+openasr pull dolphin-small:fp16
+openasr pull dolphin-small:q8
+openasr pull dolphin-small:q4
+```
+
+## 📦 Available builds
+
+| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | ΔCER vs fp16 |
+|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
+| fp16 | `dolphin-small-fp16.oasr` | 754 MB | 3.86 GB | 0.35× | 0.59× | 0.0% |
+| q8_0 | `dolphin-small-q8_0.oasr` | 412 MB | 2.68 GB | 0.37× | 0.80× | 0.0% |
+| q4_k | `dolphin-small-q4_k.oasr` | 229 MB | 3.56 GB | 0.43× | 0.33× | 0.0% |
+
+<sub>RTF = real-time factor on the shared 11s JFK clip (out-of-distribution, drift signal only) plus an in-language Mandarin sanity clip (**lower is faster**); RAM peak measured per pack
+in an isolated subprocess. ΔCER 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.</sub>
+
+## 🧠 About Dolphin Small
+
+Dolphin Small is the **372M "small" tier** of DataoceanAI's **multilingual** Dolphin speech-
+recognition line, built on the **Dolphin / ESPnet** recipe as an **E-Branchformer encoder +
+Transformer decoder** trained with a **joint CTC + attention** objective over a shared
+SentencePiece BPE vocabulary. Unlike the dedicated `dolphin-cn-dialect-*` checkpoints (fixed
+`<zh>` language token, Chinese-only char vocab), this multilingual checkpoint varies **both**
+the language and region prompt slots across the card's advertised 40 languages -- South Asian,
+Southeast Asian, Central Asian/Turkic, and Chinese (including Cantonese, listed separately as
+`yue`) -- while collapsing this product's own Chinese-dialect granularity into a single `zh`
+(the dedicated `dolphin-cn-dialect-small`/`-base` packs cover per-dialect prompting; this
+checkpoint does not). 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.
+
+**Verification status:** this pack is staged in a private repo, not yet publicly listed. Local
+verification so far covers Mandarin (`zh`) sanity-checked against the upstream architecture and
+bit-stable across fp16/q8_0/q4_k quants; Japanese (`ja`), one of the 40 advertised languages, has
+not yet had a native-speaker listening review and must get one before this model is made public.
+
+## ⚙️ How these packs were made
+
+Converted from [DataoceanAI/dolphin-small](https://huggingface.co/DataoceanAI/dolphin-small) with the OpenASR importer:
+
+```bash
+openasr model-pack import dolphin <src> <out>.oasr \
+  --package-id dolphin-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/DataoceanAI/dolphin-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 Small**, created and open-sourced by **DataoceanAI**
+([DataoceanAI/dolphin-small](https://huggingface.co/DataoceanAI/dolphin-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 **ESPnet** E-Branchformer / joint CTC-attention recipe -- thank
+you to the Dolphin and ESPnet 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** — [DataoceanAI/dolphin-small](https://huggingface.co/DataoceanAI/dolphin-small)

dolphin-small-fp16.oasr

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dolphin-small-q4_k.oasr

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dolphin-small-q8_0.oasr

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