FireRedPunc · OpenASR

Chinese punctuation restoration for OpenASR -- a BERT token classifier that adds full-width marks to unpunctuated transcripts, fully on-device

A capability-pack support model for the OpenASR runtime — pure-Rust inference, no Python at inference time. Not a standalone transcription model: it augments another OpenASR ASR model's own decode path.

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✨ Highlights

• 🈶 Chinese punctuation restoration -- restores full-width ，。？！ on an unpunctuated ASR transcript (e.g. Dolphin, FireRedASR2-AED) in a finalize-only post-processing pass
• 🧠 BERT token classifier -- a 12-layer chinese-lert-base encoder with a 5-class head predicting which of none/，。？！ follows each subword
• 🇨🇳 Chinese-only by construction -- the released label set has no English half-width marks, so only Chinese text is punctuated
• 🔌 Opt-in and auto-gated -- runs only when the ASR model is unpunctuated and this pack is installed, so punctuating families are never double-punctuated
• 🦀 Native in OpenASR — .oasr packs run with no Python at inference, engineered for peak performance on CPU & GPU

🚀 Quickstart

# 1. Install the OpenASR CLI  ·  https://openasr.org
# 2. Pull the pack
openasr pull firered-punc:fp16

# 3. Use it as an opt-in refinement for another model's transcribe call
openasr transcribe meeting.wav --model <asr-model> --word-timestamps=aligned

📦 Pack

🧠 About FireRedPunc

FireRedPunc is a punctuation-prediction model from FireRedTeam, part of the FireRedASR2S all-in-one ASR system. It adopts a BERT-style encoder initialized from chinese-lert-base (a 12-layer, 768-hidden, 12-head BERT) with a token-classification head that predicts, for each subword, which of five classes follows it: none, or one of the four Chinese full-width marks ，, 。, ？, ！. It is a text-in / labels-out post-processor, not an ASR model -- no audio frontend, no autoregressive decode.

In OpenASR it is packaged as an optional punctuation capability pack: an unpunctuated family's finished transcript (e.g. Dolphin or FireRedASR2-AED) is passed through the classifier once, and the predicted marks are re-inserted into the original characters. The stage is auto-gated on the ASR model's catalog emits_punctuation == false and only runs when this pack is installed, so punctuating families are never double-punctuated. Because the released label set is Chinese-only, the integration is Chinese-only by construction (the architecture cannot emit English half-width marks).

This OpenASR repo repackages the upstream weights as .oasr packs that run natively in the OpenASR runtime -- no Python at inference, all decoding local. It ships in fp16.

Not a standalone transcription model. This pack cannot transcribe audio by itself; it is an opt-in post-process applied to an unpunctuated ASR model's output.

Verification status: this is a brand-new catalog entry (no prior public listing). Local verification covers exact per-token label parity against the upstream PyTorch forward across a set of Chinese golden sentences (all four punctuation classes plus the no-mark class). This pack is staged in a private repo, not yet publicly listed.

⚙️ How this pack was made

Converted from FireRedTeam/FireRedPunc with the OpenASR local-source converter (convert_local_qwen_forced_aligner_source_to_runtime_pack, not yet wired to a public openasr model-pack import subcommand -- the pack format and tensor mapping are stable, but CLI/family-registry wiring is a separate stage).

The .oasr container is GGUF-backed; packs use zero-copy mmap weight binding.

⚖️ License

This pack inherits the upstream model's license: Apache-2.0 (source). OpenASR packaging retains the upstream copyright; the only modifications are format conversion and quantization.

🙏 Acknowledgements

This pack is a redistribution of FireRedPunc, created and open-sourced by FireRedTeam (FireRedTeam/FireRedPunc). 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). OpenASR only performs format conversion, runtime verification, and local-inference adaptation.

🔗 Links

• 🦀 OpenASR — https://github.com/QuintinShaw/openasr
• 🌐 Website — https://openasr.org
• 🤗 Upstream model — FireRedTeam/FireRedPunc