Dolphin Base · OpenASR
Multilingual speech recognition across 40 languages, base tier -- a compact 140M WeNet/ESPnet E-Branchformer (CTC + attention)
Native speech-to-text in the
OpenASR runtime —
engineered for peak performance on CPU & GPU,
no Python at inference time.
✨ 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
1# 1. Install the OpenASR CLI · https://openasr.org
2# 2. Pull a build (pick a quant — see the table below)
3openasr pull dolphin-base:fp16
4
5# 3. Transcribe
6openasr transcribe audio.wav --model dolphin-base
All builds for this model:
1openasr pull dolphin-base:fp16
2openasr pull dolphin-base:q8
3openasr 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% |
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.
🧠 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.
⚙️ How these packs were made
Converted from
DataoceanAI/dolphin-base with the OpenASR importer:
1openasr model-pack import dolphin <src> <out>.oasr \
2 --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). 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). 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
