Update README.md

README.md

@@ -15,6 +15,7 @@ tags:
 - asr
 ---
 
+
 <div align="center">
 <h1>
 FireRedASR2S
@@ -24,23 +25,28 @@ A SOTA Industrial-Grade All-in-One ASR System
 
 </div>
 
-[[Paper]](https://arxiv.org/pdf/2501.14350)
-[[Model]](https://huggingface.co/FireRedTeam)
-[[Blog]](https://fireredteam.github.io/demos/firered_asr/)
+[[Code]](https://github.com/FireRedTeam/FireRedASR2S)
+[[Paper]](https://arxiv.org/pdf/2603.10420)
+[[Model🤗]](https://huggingface.co/collections/FireRedTeam/fireredasr2s)
+[[Model🤖]](https://www.modelscope.cn/collections/xukaituo/FireRedASR2S)
 [[Demo]](https://huggingface.co/spaces/FireRedTeam/FireRedASR)
 
 
 FireRedASR2S is a state-of-the-art (SOTA), industrial-grade, all-in-one ASR system with ASR, VAD, LID, and Punc modules. All modules achieve SOTA performance:
-- **FireRedASR2**: Automatic Speech Recognition (ASR) supporting Chinese (Mandarin, 20+ dialects/accents), English, code-switching, and singing lyrics recognition. 2.89% average CER on Mandarin (4 test sets), 11.55% on Chinese dialects (19 test sets), outperforming Doubao-ASR, Qwen3-ASR-1.7B, Fun-ASR, and Fun-ASR-Nano-2512. FireRedASR2-AED also supports word-level timestamps and confidence scores.
-- **FireRedVAD**: Voice Activity Detection (VAD) supporting speech/singing/music in 100+ languages. 97.57% F1, outperforming Silero-VAD, TEN-VAD, and FunASR-VAD. Supports non-streaming/streaming VAD and Audio Event Detection.
-- **FireRedLID**: Spoken Language Identification (LID) supporting 100+ languages and 20+ Chinese dialects/accents. 97.18% accuracy, outperforming Whisper and SpeechBrain-LID.
+- **FireRedASR2**: Automatic Speech Recognition (ASR) supporting peech and singing transcription for Chinese (Mandarin, 20+ dialects/accents), English, code-switching. 2.89% average CER on 4 public Mandarin benchmarks, 11.55% on 19 Chinese dialects and accents benchmarks, **outperforming Doubao-ASR, Qwen3-ASR-1.7B, Fun-ASR, and Fun-ASR-Nano-2512**. FireRedASR2-AED also supports word-level timestamps and confidence scores.
+- **FireRedVAD**: Voice Activity Detection (VAD) supporting speech/singing/music in 100+ languages. 97.57% F1, **outperforming Silero-VAD, TEN-VAD, FunASR-VAD and WebRTC-VAD**. Supports non-streaming/streaming VAD and Multi-label VAD (mVAD).
+- **FireRedLID**: Spoken Language Identification (LID) supporting 100+ languages and 20+ Chinese dialects/accents. 97.18% accuracy, **outperforming Whisper and SpeechBrain**.
 - **FireRedPunc**: Punctuation Prediction (Punc) for Chinese and English. 78.90% average F1, outperforming FunASR-Punc (62.77%).
 
 *`2S`: `2`nd-generation FireRedASR, now expanded to an all-in-one ASR `S`ystem*
 
 
 ## 🔥 News
-- [2026.02.12] We release FireRedASR2S (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with model weights and inference code. Download links below. Technical report and finetuning code coming soon.
+- [2026.03.12] 🔥 We release FireRedASR2S technical report. See [arXiv](https://arxiv.org/abs/2603.10420).
+- [2026.03.05] 🚀 [vLLM](https://github.com/vllm-project/vllm/pull/35727) supports FireRedASR2-LLM. See [vLLM Usage](https://github.com/FireRedTeam/FireRedASR2S?tab=readme-ov-file#vllm-usage) part.
+- [2026.02.25] 🔥 We release **FireRedASR2-LLM model weights**. [🤗](https://huggingface.co/FireRedTeam/FireRedASR2-LLM) [🤖](https://www.modelscope.cn/models/xukaituo/FireRedASR2-LLM/)
+- [2026.02.13] 🚀 Support TensorRT-LLM inference acceleration for FireRedASR2-AED (contributed by NVIDIA). Benchmark on AISHELL-1 test set shows **12.7x speedup** over PyTorch baseline (single H20).
+- [2026.02.12] 🔥 We release FireRedASR2S (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with **model weights and inference code**. Download links below. Technical report and finetuning code coming soon.
 
 
 
@@ -48,123 +54,33 @@ FireRedASR2S is a state-of-the-art (SOTA), industrial-grade, all-in-one ASR syst
 
 |Model|Supported Languages & Dialects|Download|
 |:-------------:|:---------------------------------:|:----------:|
-|FireRedASR2| Chinese (Mandarin and 20+ dialects/accents<sup>*</sup>), English, Code-Switching | [🤗](https://huggingface.co/FireRedTeam/FireRedASR2-AED) \| [🤖](https://modelscope.cn/collections/FireRedTeam/FireRedASR2S)|
-|FireRedVAD | 100+ languages, 20+ Chinese dialects/accents<sup>*</sup> | [🤗](https://huggingface.co/FireRedTeam/FireRedVAD) \| [🤖](https://modelscope.cn/collections/FireRedTeam/FireRedVAD)|
-|FireRedLID | 100+ languages, 20+ Chinese dialects/accents<sup>*</sup> | [🤗](https://huggingface.co/FireRedTeam/FireRedLID) \| [🤖](https://modelscope.cn/collections/FireRedTeam/FireRedLID)|
-|FireRedPunc| Chinese, English | [🤗](https://huggingface.co/FireRedTeam/FireRedPunc) \| [🤖](https://modelscope.cn/collections/FireRedTeam/FireRedPunc)|
+|FireRedASR2-LLM| Chinese (Mandarin and 20+ dialects/accents<sup>*</sup>), English, Code-Switching | [🤗](https://huggingface.co/FireRedTeam/FireRedASR2-LLM) \| [🤖](https://www.modelscope.cn/models/xukaituo/FireRedASR2-LLM/)|
+|FireRedASR2-AED| Chinese (Mandarin and 20+ dialects/accents<sup>*</sup>), English, Code-Switching | [🤗](https://huggingface.co/FireRedTeam/FireRedASR2-AED) \| [🤖](https://www.modelscope.cn/models/xukaituo/FireRedASR2-AED/)|
+|FireRedVAD | 100+ languages, 20+ Chinese dialects/accents<sup>*</sup> | [🤗](https://huggingface.co/FireRedTeam/FireRedVAD) \| [🤖](https://www.modelscope.cn/models/xukaituo/FireRedVAD/)|
+|FireRedLID | 100+ languages, 20+ Chinese dialects/accents<sup>*</sup> | [🤗](https://huggingface.co/FireRedTeam/FireRedLID) \| [🤖](https://www.modelscope.cn/models/xukaituo/FireRedLID/)|
+|FireRedPunc| Chinese, English | [🤗](https://huggingface.co/FireRedTeam/FireRedPunc) \| [🤖](https://www.modelscope.cn/models/xukaituo/FireRedPunc/)|
 
 <sup>*</sup>Supported Chinese dialects/accents: Cantonese (Hong Kong & Guangdong), Sichuan, Shanghai, Wu, Minnan, Anhui, Fujian, Gansu, Guizhou, Hebei, Henan, Hubei, Hunan, Jiangxi, Liaoning, Ningxia, Shaanxi, Shanxi, Shandong, Tianjin, Yunnan, etc.
 
 
 
 ## Method
+### FireRedASR2S: System Overview
+![Model](./assets/FireRedASR2S.png)
+
 ### FireRedASR2
 FireRedASR2 builds upon [FireRedASR](https://github.com/FireRedTeam/FireRedASR) with improved accuracy, designed to meet diverse requirements in superior performance and optimal efficiency across various applications. It comprises two variants:
 - **FireRedASR2-LLM**: Designed to achieve state-of-the-art performance and to enable seamless end-to-end speech interaction. It adopts an Encoder-Adapter-LLM framework leveraging large language model (LLM) capabilities.
 - **FireRedASR2-AED**: Designed to balance high performance and computational efficiency and to serve as an effective speech representation module in LLM-based speech models. It utilizes an Attention-based Encoder-Decoder (AED) architecture.
 
+![Model](./assets/FireRedASR2.png)
 
 ### Other Modules
-- **FireRedVAD**: DFSMN-based non-streaming/streaming Voice Activity Detection and Audio Event Detection.
-- **FireRedLID**: FireRedASR2-based Spoken Language Identification. See [FireRedLID README](./fireredasr2s/fireredlid/README.md) for language details.
+- **FireRedVAD**: DFSMN-based non-streaming/streaming Voice Activity Detection and Multi-label VAD (mVAD). mVAD can be viewed as a lightweight Audio Event Detection (AED) system specialized for a small set of sound categories (speech/singing/music).
+- **FireRedLID**: Encoder-Decoder-based Spoken Language Identification. See [FireRedLID README](./fireredasr2s/fireredlid/README.md) for language details.
 - **FireRedPunc**: BERT-based Punctuation Prediction.
 
 
-## Evaluation
-### FireRedASR2
-Metrics: Character Error Rate (CER%) for Chinese and Word Error Rate (WER%) for English. Lower is better.
-
-We evaluate FireRedASR2 on 24 public test sets covering Mandarin, 20+ Chinese dialects/accents, and singing.
-
-- **Mandarin (4 test sets)**: 2.89% (LLM) / 3.05% (AED) average CER, outperforming Doubao-ASR (3.69%), Qwen3-ASR-1.7B (3.76%), Fun-ASR (4.16%) and Fun-ASR-Nano-2512 (4.55%).
-- **Dialects (19 test sets)**: 11.55% (LLM) / 11.67% (AED) average CER, outperforming Doubao-ASR (15.39%), Qwen3-ASR-1.7B (11.85%), Fun-ASR (12.76%) and Fun-ASR-Nano-2512 (15.07%).
-
-*Note: ws=WenetSpeech, md=MagicData, conv=Conversational, daily=Daily-use.*
-
-|ID|Testset\Model|FireRedASR2-LLM|FireRedASR2-AED|Doubao-ASR|Qwen3-ASR|Fun-ASR|Fun-ASR-Nano|
-|:--:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
-|  |**Average CER<br>(All, 1-24)**   |**9.67** |**9.80** |12.98 |10.12 |10.92 |12.81 |
-|  |**Average CER<br>(Mandarin, 1-4)**   |**2.89** |**3.05** |3.69 |3.76 |4.16 |4.55 |
-|  |**Average CER<br>(Dialects, 5-23)**  |**11.55**|**11.67**|15.39|11.85|12.76|15.07|
-|1 |aishell1          |0.64 |0.57 |1.52 |1.48 |1.64 |1.96 |
-|2 |aishell2          |2.15 |2.51 |2.77 |2.71 |2.38 |3.02 |
-|3 |ws-net            |4.44 |4.57 |5.73 |4.97 |6.85 |6.93 |
-|4 |ws-meeting        |4.32 |4.53 |4.74 |5.88 |5.78 |6.29 |
-|5 |kespeech          |3.08 |3.60 |5.38 |5.10 |5.36 |7.66 |
-|6 |ws-yue-short      |5.14 |5.15 |10.51|5.82 |7.34 |8.82 |
-|7 |ws-yue-long       |8.71 |8.54 |11.39|8.85 |10.14|11.36|
-|8 |ws-chuan-easy     |10.90|10.60|11.33|11.99|12.46|14.05|
-|9 |ws-chuan-hard     |20.71|21.35|20.77|21.63|22.49|25.32|
-|10|md-heavy          |7.42 |7.43 |7.69 |8.02 |9.13 |9.97 |
-|11|md-yue-conv       |12.23|11.66|26.25|9.76 |33.71|15.68|
-|12|md-yue-daily      |3.61 |3.35 |12.82|3.66 |2.69 |5.67 |
-|13|md-yue-vehicle    |4.50 |4.83 |8.66 |4.28 |6.00 |7.04 |
-|14|md-chuan-conv     |13.18|13.07|11.77|14.35|14.01|17.11|
-|15|md-chuan-daily    |4.90 |5.17 |3.90 |4.93 |3.98 |5.95 |
-|16|md-shanghai-conv  |28.70|27.02|45.15|29.77|25.49|37.08|
-|17|md-shanghai-daily |24.94|24.18|44.06|23.93|12.55|28.77|
-|18|md-wu             |7.15 |7.14 |7.70 |7.57 |10.63|10.56|
-|19|md-zhengzhou-conv |10.20|10.65|9.83 |9.55 |10.85|13.09|
-|20|md-zhengzhou-daily|5.80 |6.26 |5.77 |5.88 |6.29 |8.18 |
-|21|md-wuhan          |9.60 |10.81|9.94 |10.22|4.34 |8.70 |
-|22|md-tianjin        |15.45|15.30|15.79|16.16|19.27|22.03|
-|23|md-changsha       |23.18|25.64|23.76|23.70|25.66|29.23|
-|24|opencpop          |1.12 |1.17 |4.36 |2.57 |3.05 |2.95 |
-
-Doubao-ASR (volc.seedasr.auc) tested in early February 2026, and Fun-ASR tested in late November 2025. Our ASR training data does not include any Chinese dialect or accented speech data from MagicData.
-- Doubao-ASR (API): https://www.volcengine.com/docs/6561/1354868
-- Qwen3-ASR (1.7B): https://github.com/QwenLM/Qwen3-ASR
-- Fun-ASR (API): https://help.aliyun.com/zh/model-studio/recording-file-recognition
-- Fun-ASR-Nano-2512: https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512
-
-
-### FireRedVAD
-We evaluate FireRedVAD on FLEURS-VAD-102, a multilingual VAD benchmark covering 102 languages.
-
-FireRedVAD achieves SOTA performance, outperforming Silero-VAD, TEN-VAD, FunASR-VAD, and WebRTC-VAD.
-
-|Metric\Model|FireRedVAD|[Silero-VAD](https://github.com/snakers4/silero-vad)|[TEN-VAD](https://github.com/TEN-framework/ten-vad)|[FunASR-VAD](https://modelscope.cn/models/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch)|[WebRTC-VAD](https://github.com/wiseman/py-webrtcvad)|
-|:-------:|:-----:|:------:|:------:|:------:|:------:|
-|AUC-ROC↑  |**99.60**|97.99|97.81|-    |-    |
-|F1 score↑ |**97.57**|95.95|95.19|90.91|52.30|
-|False Alarm Rate↓  |**2.69** |9.41 |15.47|44.03|2.83 |
-|Miss Rate↓|3.62     |3.95 |2.95 |0.42 |64.15|
-
-<sup>*</sup>FLEURS-VAD-102: We randomly selected ~100 audio files per language from [FLEURS test set](https://huggingface.co/datasets/google/fleurs), resulting in 9,443 audio files with manually annotated binary VAD labels (speech=1, silence=0). This VAD testset will be open sourced (coming soon).
-
-Note: FunASR-VAD achieves low Miss Rate but at the cost of high False Alarm Rate (44.03%), indicating over-prediction of speech segments.
-
-
-### FireRedLID
-Metric: Utterance-level LID Accuracy (%). Higher is better.
-
-We evaluate FireRedLID on multilingual and Chinese dialect benchmarks.
-
-FireRedLID achieves SOTA performance, outperforming Whisper, SpeechBrain-LID, and Dolphin.
-
-|Testset\Model|Languages|FireRedLID|[Whisper](https://github.com/openai/whisper)|[SpeechBrain](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa)|[Dolphin](https://github.com/DataoceanAI/Dolphin)|
-|:-----------------:|:---------:|:---------:|:-----:|:---------:|:-----:|
-|FLEURS test         |82 languages |**97.18**     |79.41  |92.91      |-|
-|CommonVoice test    |74 languages |**92.07**    |80.81  |78.75      |-|
-|KeSpeech + MagicData|20+ Chinese dialects/accents |**88.47**     |-|-|69.01|
-
-
-### FireRedPunc
-Metric: Precision/Recall/F1 Score (%). Higher is better.
-
-We evaluate FireRedPunc on multi-domain Chinese and English benchmarks.
-
-FireRedPunc achieves SOTA performance, outperforming FunASR-Punc (CT-Transformer).
-
-|Testset\Model|#Sentences|FireRedPunc|[FunASR-Punc](https://www.modelscope.cn/models/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch)|
-|:------------------:|:---------:|:--------------:|:-----------------:|
-|Multi-domain Chinese| 88,644    |**82.84 / 83.08 / 82.96** | 77.27 / 74.03 / 75.62 |
-|Multi-domain English| 28,641    |**78.40 / 71.57 / 74.83** | 55.79 / 45.15 / 49.91 |
-|Average F1 Score    | -         |**78.90** | 62.77 |
-
-
-
-
 ## Quick Start
 ### Setup
 1. Create a clean Python environment:
@@ -186,10 +102,11 @@ $ export PYTHONPATH=$PWD/:$PYTHONPATH
 ```bash
 # Download via ModelScope (recommended for users in China)
 pip install -U modelscope
-modelscope download --model FireRedTeam/FireRedASR2-AED --local_dir ./pretrained_models/FireRedASR2-AED
-modelscope download --model FireRedTeam/FireRedVAD --local_dir ./pretrained_models/FireRedVAD
-modelscope download --model FireRedTeam/FireRedLID --local_dir ./pretrained_models/FireRedLID
-modelscope download --model FireRedTeam/FireRedPunc --local_dir ./pretrained_models/FireRedPunc
+modelscope download --model xukaituo/FireRedASR2-AED --local_dir ./pretrained_models/FireRedASR2-AED
+modelscope download --model xukaituo/FireRedVAD --local_dir ./pretrained_models/FireRedVAD
+modelscope download --model xukaituo/FireRedLID --local_dir ./pretrained_models/FireRedLID
+modelscope download --model xukaituo/FireRedPunc --local_dir ./pretrained_models/FireRedPunc
+modelscope download --model xukaituo/FireRedASR2-LLM --local_dir ./pretrained_models/FireRedASR2-LLM
 
 # Download via Hugging Face
 pip install -U "huggingface_hub[cli]"
@@ -197,6 +114,7 @@ huggingface-cli download FireRedTeam/FireRedASR2-AED --local-dir ./pretrained_mo
 huggingface-cli download FireRedTeam/FireRedVAD --local-dir ./pretrained_models/FireRedVAD
 huggingface-cli download FireRedTeam/FireRedLID --local-dir ./pretrained_models/FireRedLID
 huggingface-cli download FireRedTeam/FireRedPunc --local-dir ./pretrained_models/FireRedPunc
+huggingface-cli download FireRedTeam/FireRedASR2-LLM --local-dir ./pretrained_models/FireRedASR2-LLM
 ```
 
 4. Convert your audio to **16kHz 16-bit mono PCM** format if needed:
@@ -247,7 +165,7 @@ $ cd examples_infer/asr
 $ bash inference_asr_aed.sh
 $ bash inference_asr_llm.sh
 
-# VAD & AED (Audio Event Detection)
+# VAD & mVAD (mVAD=Audio Event Detection, AED)
 $ cd examples_infer/vad
 $ bash inference_vad.sh
 $ bash inference_streamvad.sh
@@ -262,6 +180,13 @@ $ cd examples_infer/punc
 $ bash inference_punc.sh
 ```
 
+### vLLM Usage
+```shell
+# Serving FireRedASR2-LLM with latest vLLM for the highest performance.
+# For more details, see https://github.com/vllm-project/vllm/pull/35727.
+$ vllm serve allendou/FireRedASR2-LLM-vllm -tp=2 --dtype=float32
+$ python3 examples/online_serving/openai_transcription_client.py --repetition_penalty=1.0 --audio_path=/root/hello_zh.wav
+```
 
 ### Python API Usage
 Set up `PYTHONPATH` first: `export PYTHONPATH=$PWD/:$PYTHONPATH`
@@ -354,7 +279,7 @@ print(result)
 </details>
 
 
-#### Audio Event Detection (AED)
+#### mVAD (Audio Event Detection, AED)
 <details>
 <summary>Click to expand</summary>
 
@@ -474,6 +399,7 @@ for wav_path, uttid in zip(batch_wav_path, batch_uttid):
 # {'uttid': 'hello_en', 'text': 'Hello speech.', 'sentences': [{'start_ms': 260, 'end_ms': 1820, 'text': 'Hello speech.', 'asr_confidence': 0.933, 'lang': 'en', 'lang_confidence': 0.993}], 'vad_segments_ms': [(260, 1820)], 'dur_s': 2.24, 'words': [{'start_ms': 400, 'end_ms': 960, 'text': 'hello'}, {'start_ms': 960, 'end_ms': 1666, 'text': 'speech'}], 'wav_path': 'assets/hello_en.wav'}
 ```
 
+**Note:** `FireRedASR2S` code has only been tested on Linux Ubuntu 22.04. Behavior on other Linux distributions or Windows has not been tested.
 
 
 ## FAQ
@@ -483,8 +409,107 @@ for wav_path, uttid in zip(batch_wav_path, batch_uttid):
 
 **Q: What are the input length limitations of ASR models?**
 
-- FireRedASR2-AED supports audio input up to 60s. Input longer than 60s may cause hallucination issues, and input exceeding 200s will trigger positional encoding errors.
-- FireRedASR2-LLM supports audio input up to 30s. The behavior for longer input is untested.
+- **FireRedASR2-AED** supports audio input **up to 60s**. Input longer than 60s may cause hallucination issues, and input exceeding 200s will trigger positional encoding errors.
+- **FireRedASR2-LLM** supports audio input **up to 40s**. The behavior for longer input is untested.
+- **FireRedASR2-LLM Batch Beam Search**: When performing batch beam search with FireRedASR2-LLM, even though attention masks are applied, it is recommended to ensure that the input lengths of the utterances are similar. If there are significant differences in utterance lengths, shorter utterances may experience **repetition issues**. You can either sort your dataset by length or set `batch_size` to 1 to avoid the repetition issue.
+
+
+
+## Evaluation
+### FireRedASR2
+Metrics: Character Error Rate (CER%) for Chinese and Word Error Rate (WER%) for English. Lower is better.
+
+We evaluate FireRedASR2 on 24 public test sets covering Mandarin, 20+ Chinese dialects/accents, and singing.
+
+- **Mandarin (4 test sets)**: 2.89% (LLM) / 3.05% (AED) average CER, outperforming Doubao-ASR (3.69%), Qwen3-ASR-1.7B (3.76%), Fun-ASR (4.16%) and Fun-ASR-Nano-2512 (4.55%).
+- **Dialects (19 test sets)**: 11.55% (LLM) / 11.67% (AED) average CER, outperforming Doubao-ASR (15.39%), Qwen3-ASR-1.7B (11.85%), Fun-ASR (12.76%) and Fun-ASR-Nano-2512 (15.07%).
+
+*Note: FRASR2=FireRedASR2, ws=WenetSpeech, md=MagicData, conv=Conversational, daily=Daily-use.*
+
+|ID|Testset\CER\Model|FRASR2-LLM|FRASR2-AED|Doubao-ASR|Qwen3-ASR|Fun-ASR|
+|:--:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+|Avg|**All(1-24)**   |**9.67** |**9.80** |12.98 |10.12 |10.92 |
+|Avg|**Mandarin(1-4)**   |**2.89** |**3.05** |3.69 |3.76 |4.16 |
+|Avg|**Dialect(5-23)**  |**11.55**|**11.67**|15.39|11.85|12.76|
+|1 |aishell1          |0.64 |0.57 |1.52 |1.48 |1.64 |
+|2 |aishell2          |2.15 |2.51 |2.77 |2.71 |2.38 |
+|3 |ws-net            |4.44 |4.57 |5.73 |4.97 |6.85 |
+|4 |ws-meeting        |4.32 |4.53 |4.74 |5.88 |5.78 |
+|5 |kespeech          |3.08 |3.60 |5.38 |5.10 |5.36 |
+|6 |ws-yue-short      |5.14 |5.15 |10.51|5.82 |7.34 |
+|7 |ws-yue-long       |8.71 |8.54 |11.39|8.85 |10.14|
+|8 |ws-chuan-easy     |10.90|10.60|11.33|11.99|12.46|
+|9 |ws-chuan-hard     |20.71|21.35|20.77|21.63|22.49|
+|10|md-heavy          |7.42 |7.43 |7.69 |8.02 |9.13 |
+|11|md-yue-conv       |12.23|11.66|26.25|9.76 |33.71|
+|12|md-yue-daily      |3.61 |3.35 |12.82|3.66 |2.69 |
+|13|md-yue-vehicle    |4.50 |4.83 |8.66 |4.28 |6.00 |
+|14|md-chuan-conv     |13.18|13.07|11.77|14.35|14.01|
+|15|md-chuan-daily    |4.90 |5.17 |3.90 |4.93 |3.98 |
+|16|md-shanghai-conv  |28.70|27.02|45.15|29.77|25.49|
+|17|md-shanghai-daily |24.94|24.18|44.06|23.93|12.55|
+|18|md-wu             |7.15 |7.14 |7.70 |7.57 |10.63|
+|19|md-zhengzhou-conv |10.20|10.65|9.83 |9.55 |10.85|
+|20|md-zhengzhou-daily|5.80 |6.26 |5.77 |5.88 |6.29 |
+|21|md-wuhan          |9.60 |10.81|9.94 |10.22|4.34 |
+|22|md-tianjin        |15.45|15.30|15.79|16.16|19.27|
+|23|md-changsha       |23.18|25.64|23.76|23.70|25.66|
+|24|opencpop          |1.12 |1.17 |4.36 |2.57 |3.05 |
+
+
+### FireRedVAD
+<details>
+<summary>Click to expand</summary>
+We evaluate FireRedVAD on FLEURS-VAD-102, a multilingual VAD benchmark covering 102 languages.
+
+FireRedVAD achieves SOTA performance, outperforming Silero-VAD, TEN-VAD, FunASR-VAD, and WebRTC-VAD.
+
+|Metric\Model|FireRedVAD|Silero-VAD|TEN-VAD|FunASR-VAD|WebRTC-VAD|
+|:-------:|:-----:|:------:|:------:|:------:|:------:|
+|AUC-ROC↑  |**99.60**|97.99|97.81|-    |-    |
+|F1 score↑ |**97.57**|95.95|95.19|90.91|52.30|
+|False Alarm Rate↓  |**2.69** |9.41 |15.47|44.03|2.83 |
+|Miss Rate↓|3.62     |3.95 |2.95 |0.42 |64.15|
+
+FLEURS-VAD-102: We randomly selected ~100 audio files per language from [FLEURS test set](https://huggingface.co/datasets/google/fleurs), resulting in 9,443 audio files with manually annotated binary VAD labels (speech=1, silence=0). This VAD testset will be open sourced (coming soon).
+
+Note: FunASR-VAD achieves low Miss Rate but at the cost of high False Alarm Rate (44.03%), indicating over-prediction of speech segments.
+</details>
+
+
+### FireRedLID
+<details>
+<summary>Click to expand</summary>
+Metric: Utterance-level LID Accuracy (%). Higher is better.
+
+We evaluate FireRedLID on multilingual and Chinese dialect benchmarks.
+
+FireRedLID achieves SOTA performance, outperforming Whisper, SpeechBrain-LID, and Dolphin.
+
+|Testset\Model|Languages|FireRedLID|Whisper|SpeechBrain|Dolphin|
+|:-----------------:|:---------:|:---------:|:-----:|:---------:|:-----:|
+|FLEURS test         |82 languages |**97.18**     |79.41  |92.91      |-|
+|CommonVoice test    |74 languages |**92.07**    |80.81  |78.75      |-|
+|KeSpeech + MagicData|20+ Chinese dialects/accents |**88.47**     |-|-|69.01|
+</details>
+
+
+### FireRedPunc
+<details>
+<summary>Click to expand</summary>
+Metric: Precision/Recall/F1 Score (%). Higher is better.
+
+We evaluate FireRedPunc on multi-domain Chinese and English benchmarks.
+
+FireRedPunc achieves SOTA performance, outperforming FunASR-Punc (CT-Transformer).
+
+|Testset\Model|#Sentences|FireRedPunc|FunASR-Punc|
+|:------------------:|:---------:|:--------------:|:-----------------:|
+|Multi-domain Chinese| 88,644    |**82.84 / 83.08 / 82.96** | 77.27 / 74.03 / 75.62 |
+|Multi-domain English| 28,641    |**78.40 / 71.57 / 74.83** | 55.79 / 45.15 / 49.91 |
+|Average F1 Score    | -         |**78.90** | 62.77 |
+
+</details>
 
 
 ## Acknowledgements
@@ -492,3 +517,14 @@ Thanks to the following open-source works:
 - [Qwen](https://huggingface.co/Qwen)
 - [WenetSpeech-Yue](https://github.com/ASLP-lab/WenetSpeech-Yue)
 - [WenetSpeech-Chuan](https://github.com/ASLP-lab/WenetSpeech-Chuan)
+
+
+## Citation
+```bibtex
+@article{xu2026fireredasr2s,
+  title={FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System},
+  author={Xu, Kaituo and Jia, Yan and Huang, Kai and Chen, Junjie and Li, Wenpeng and Liu, Kun and Xie, Feng-Long and Tang, Xu and Hu, Yao},
+  journal={arXiv preprint arXiv:2603.10420},
+  year={2026}
+}
+```