publish dolphin-base 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-base
+pipeline_tag: automatic-speech-recognition
+library_name: openasr
+tags:
+  - automatic-speech-recognition
+  - speech-to-text
+  - openasr
+  - oasr
+  - dolphin-base
+---
+
+<div align="center">
+
+# Dolphin Base · OpenASR
+
+**Multilingual speech recognition across 40 languages, base tier -- a compact 140M WeNet/ESPnet E-Branchformer (CTC + attention)**
+
+[![License](https://img.shields.io/badge/license-Apache--2.0-2563eb.svg)](https://huggingface.co/DataoceanAI/dolphin-base/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--base-f59e0b.svg)](https://huggingface.co/DataoceanAI/dolphin-base)
+
+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, base tier** — the same multilingual E-Branchformer coverage as Dolphin Small (South Asian, Southeast Asian, Central Asian/Turkic, Chinese/Cantonese), at a fraction of the size
+- 🪶 **140M parameters** — roughly a third the width of the `small` checkpoint (512 vs 768 d_model, fewer 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
+- 🐬 **SentencePiece BPE vocab** — a shared subword vocabulary across all 40 languages (distinct from the cn-dialect family's fixed character vocab)
+- 🦀 **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-base:fp16
+
+# 3. Transcribe
+openasr transcribe audio.wav --model dolphin-base
+```
+
+All builds for this model:
+
+```bash
+openasr pull dolphin-base:fp16
+openasr pull dolphin-base:q8
+openasr pull dolphin-base:q4
+```
+
+## 📦 Available builds
+
+| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | ΔCER vs fp16 |
+|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
+| fp16 | `dolphin-base-fp16.oasr` | 287 MB | 1.92 GB | 0.15× | 0.14× | 0.0% |
+| q8_0 | `dolphin-base-q8_0.oasr` | 158 MB | 1.76 GB | 0.15× | 0.16× | 0.0% |
+| q4_k | `dolphin-base-q4_k.oasr` | 90 MB | 1.70 GB | 0.13× | 0.13× | 8.8% |
+
+<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 Base
+
+Dolphin Base is the **140M "base" tier** of DataoceanAI's **multilingual** Dolphin speech-
+recognition line, built on the same **Dolphin / ESPnet** recipe as the larger **Dolphin Small**:
+an **E-Branchformer encoder + Transformer decoder** trained with a **joint CTC + attention**
+objective over a shared SentencePiece BPE vocabulary spanning the card's advertised 40 languages
+(South Asian, Southeast Asian, Central Asian/Turkic, and Chinese including Cantonese as `yue`),
+at roughly a third of the small tier's encoder/decoder width -- a smaller RAM/CPU footprint for
+deployments where the small tier's accuracy headroom is not needed. Like `dolphin-small`, this
+checkpoint collapses this product's own Chinese-dialect granularity into a single `zh` (the
+dedicated `dolphin-cn-dialect-small`/`-base` packs cover per-dialect prompting). 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 at fp16/q8_0, with a small (~9% CER) drift at q4_k versus fp16 on the sanity clip;
+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-base](https://huggingface.co/DataoceanAI/dolphin-base) with the OpenASR importer:
+
+```bash
+openasr model-pack import dolphin <src> <out>.oasr \
+  --package-id dolphin-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](https://huggingface.co/DataoceanAI/dolphin-base/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 Base**, created and open-sourced by **DataoceanAI**
+([DataoceanAI/dolphin-base](https://huggingface.co/DataoceanAI/dolphin-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 **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-base](https://huggingface.co/DataoceanAI/dolphin-base)

dolphin-base-fp16.oasr

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dolphin-base-q4_k.oasr

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dolphin-base-q8_0.oasr

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