publish firered-aed-l-v2 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: FireRedTeam/FireRedASR2-AED
+pipeline_tag: automatic-speech-recognition
+library_name: openasr
+tags:
+  - automatic-speech-recognition
+  - speech-to-text
+  - openasr
+  - oasr
+  - firered-aed-l-v2
+---
+
+<div align="center">
+
+# FireRedASR2 AED-L · OpenASR
+
+**FireRedTeam's Mandarin-first bilingual ASR — attention encoder-decoder tuned for state-of-the-art Chinese and dialect accuracy**
+
+[![License](https://img.shields.io/badge/license-Apache--2.0-2563eb.svg)](https://huggingface.co/FireRedTeam/FireRedASR2-AED/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-FireRedASR2--AED-f59e0b.svg)](https://huggingface.co/FireRedTeam/FireRedASR2-AED)
+
+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
+
+- 🥇 **State-of-the-art Mandarin accuracy** — 3.05% average CER across four public Mandarin benchmarks, with 0.57% CER on aishell1 (arXiv:2603.10420 Table 2)
+- 🗣️ **Strong dialect coverage** — 11.67% average CER across 19 public Chinese dialect/accent benchmarks, beating Doubao-ASR (15.39%) and matching Qwen3-ASR (11.85%) on the paper's own comparison table
+- 🇨🇳🇬🇧 **Bilingual Mandarin + English** — one 1.1B checkpoint decodes both languages plus code-switching, no language flag needed
+- 🎤 **Singing-lyrics robustness** — the paper reports 1.17% CER on a singing-lyrics benchmark, alongside the speech-recognition numbers above
+- 🏗️ **Conformer encoder + Transformer decoder** — an attention-based encoder-decoder, architecturally identical to FireRedASR-AED-L, retrained on a larger vocabulary
+- 🦀 **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 firered-aed-l-v2:q4
+
+# 3. Transcribe
+openasr transcribe audio.wav --model firered-aed-l-v2
+```
+
+All builds for this model:
+
+```bash
+openasr pull firered-aed-l-v2:fp16
+openasr pull firered-aed-l-v2:q8
+openasr pull firered-aed-l-v2:q4
+```
+
+## 📦 Available builds
+
+| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | JFK ΔWER vs fp16 |
+|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
+| fp16 | `firered-aed-l-v2-fp16.oasr` | 2.35 GB | 4.74 GB | 0.40× | 0.79× | 0.0% |
+| q8_0 | `firered-aed-l-v2-q8_0.oasr` | 1.28 GB | 4.35 GB | 0.36× | 0.35× | 0.0% |
+| q4_k | `firered-aed-l-v2-q4_k.oasr` | 709 MB | 4.10 GB | 0.38× | 0.31× | 0.0% |
+
+<sub>RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack
+in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's
+fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy.
+**q4_k** is the recommended default — near-reference quality at a fraction of the
+footprint.</sub>
+
+## 🧠 About FireRedASR2 AED-L
+
+FireRedASR2-AED is the attention-based encoder-decoder member of **FireRedASR2**, the successor
+to FireRedTeam's open-source industrial-grade **FireRedASR** speech-recognition family, released
+as part of the **FireRedASR2S** all-in-one ASR system. It pairs a **Conformer encoder** with a
+**Transformer decoder** at 1.1B parameters -- architecturally byte-identical to the original
+FireRedASR-AED-L -- retrained on a larger token vocabulary. The FireRedASR2S technical report
+(arXiv:2603.10420, Table 2) reports **3.05% average Character Error Rate** across four public
+Mandarin benchmarks (aishell1 at 0.57%), **11.67% average CER** across 19 public Chinese
+dialect/accent benchmarks, and **1.17% CER** on a singing-lyrics benchmark, outperforming
+Doubao-ASR and matching Qwen3-ASR on the paper's own comparison table. It is bilingual (Mandarin
+Chinese and English, including code-switching). This OpenASR repo repackages the weights as
+`.oasr` packs that run natively in the OpenASR runtime -- no Python at inference time, all
+decoding local. The **q4_k** build is the recommended default for everyday use; **q8_0** trades
+some size for closer fp16 fidelity and **fp16** is for maximum fidelity or verification. Note:
+FireRedTeam also publishes a larger **FireRedASR2-LLM** (Encoder-Adapter-LLM, 2.89%/11.55% avg
+CER) variant that this repo does not distribute -- the numbers above are for this AED checkpoint
+only, and should not be conflated with the original (v1) FireRedASR-AED-L's 3.18%/0.55% figures,
+which come from a different training run and benchmark protocol.
+
+## ⚙️ How these packs were made
+
+Converted from [FireRedTeam/FireRedASR2-AED](https://huggingface.co/FireRedTeam/FireRedASR2-AED) with the OpenASR importer:
+
+```bash
+openasr model-pack import firered-aed <src> <out>.oasr \
+  --package-id firered-aed-l-v2 --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/FireRedTeam/FireRedASR2-AED/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 **FireRedASR2-AED**, created and released by **FireRedTeam**
+([FireRedTeam/FireRedASR2-AED](https://huggingface.co/FireRedTeam/FireRedASR2-AED),
+[FireRedTeam/FireRedASR2S](https://github.com/FireRedTeam/FireRedASR2S)). All credit for the
+architecture, training, and weights belongs to FireRedTeam; the license is inherited from and
+identical to the upstream model (**Apache-2.0**, as declared on the upstream model card). Thank
+you to FireRedTeam 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** — [FireRedTeam/FireRedASR2-AED](https://huggingface.co/FireRedTeam/FireRedASR2-AED)

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