Duplicate from netease-youdao/Confucius4-R2T2

Co-authored-by: heqi <heqigogo@users.noreply.huggingface.co>

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README.md

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+---
+tags:
+- confucius4
+- r2t2
+- asr
+- streaming
+- real-time
+- low-latency
+- speech-recognition
+- vllm
+- multilingual
+base_model: Qwen/Qwen3-ASR-1.7B
+pipeline_tag: automatic-speech-recognition
+license: other
+license_name: netease-model-use-license-agreement
+license_link: https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/MODEL_LICENSE
+---
+
+<div align="center">
+    <img src="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/resources/R2T2_logo.png" alt="Confucius4-R2T2" width="30%">
+    <h1>Confucius4-R2T2: A Low Latency and High Accuracy Real-Time Speech Recognition Model</h1>
+        <p>
+        <b>
+            Real
+            Real-Time
+            Transcription
+        </b>
+    </p>
+</div>
+
+<div align="center">
+    <a href="https://github.com/netease-youdao/Confucius4-R2T2"><img src="https://img.shields.io/badge/GitHub-Confucius4--R2T2-181717?logo=github" alt="GitHub repository"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://github.com/netease-youdao/Confucius4-R2T2/blob/master/README.zh.md"><img src="https://img.shields.io/badge/README-中文版本-red" alt="Chinese README"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/MODEL_LICENSE"><img src="https://img.shields.io/badge/model_license-NetEase-blue" alt="Model license: NetEase Model Use License Agreement"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://github.com/netease-youdao/Confucius4-R2T2/blob/master/LICENSE"><img src="https://img.shields.io/badge/code_license-Apache%202.0-blue" alt="Code license: Apache 2.0"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://r2t2.youdao.com/demo"><img src="https://img.shields.io/badge/Demo-在线体验-orange" alt="Online demo"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://huggingface.co/netease-youdao/Confucius4-R2T2"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Confucius4R2T2-yellow" alt="Hugging Face model"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://modelscope.cn/models/netease-youdao/Confucius4-R2T2"><img src="https://img.shields.io/badge/ModelScope-Confucius4R2T2-purple" alt="ModelScope model"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <a href="https://r2t2.ai/"><img src="https://img.shields.io/badge/Website-www.r2t2.ai-purple" alt="R2T2 website"></a>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+</div>
+<br>
+
+Confucius4-R2T2 is a low-latency and high-accuracy true streaming Automatic Speech Recognition (ASR) model that features fine-grained and configurable decoding chunks from 80 ms to 2 s. The model operates in append-only output mode: committing transcript text permanently without revising previous words, which is critical for applications where text must be processed or acted upon instantly. This results in a smoother user experience, avoiding disruptive text revisions and visual flickering in real-time applications, such as Real-Time Live Captioning & Subtitling, Downstream NLP Pipelines & LLM Agents, Simultaneous Speech Translation, etc.
+
+R2T2, short for Real Real-Time Transcription, is built upon the Qwen3-ASR model. And it is trained with a unique set of data construction techniques including stable-prefix data, forced time-alignment data, and token-level audio segmentation. Combined with a Longest Stable Prefix (LSP) learning paradigm (tech report will be released soon), R2T2 can dynamically determine when a stable prefix can be safely emitted and when additional audio context is needed. By exposing only stable prefixes, the model provides high-quality context that conditions subsequent predictions while guaranteeing that previously emitted text remains unchanged. Despite its streaming design, R2T2 maintains strong accuracy in offline recognition.
+
+- **Low-latency and high accuracy streaming recognition** — The model achieves accuracy close to that of offline  recognition, with only 200 to 600 milliseconds average latency.
+- **Stable streaming output** — Emitted text is committed as it arrives and remains unchanged.
+- **Configurable low-latency chunking** - Supports decoding chunks from 80 ms to 2 s for different latency/accuracy trade-offs.
+- **No loss in offline accuracy** — Adding streaming support does not degrade offline recognition accuracy.
+- **vLLM backend** — Provides high-throughput inference. A Hugging Face `transformers` backend is also available.
+- **Context and hotword prompts** — Natively supported.
+- **Multilingual support** — Optimized for **Chinese and English**, while also supporting a broad range of additional languages.
+
+Experimental results show that R2T2 achieves state-of-the-art (SOTA) performance in both latency and recognition quality among a range of open-source models, while remaining competitive with leading closed-source systems. The [GitHub repository](https://github.com/netease-youdao/Confucius4-R2T2) provides inference code, a minimal usage example, and a vLLM-based backend supporting both offline and real-time streaming inference.
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Demo](#demo)
+  - [Side-by-side comparison with GPT-Live-Transcribe](#side-by-side-comparison-with-gpt-live-transcribe)
+  - [Additional resources](#additional-resources)
+- [Evaluation](#evaluation)
+  - [Streaming performance](#streaming-performance)
+  - [Accuracy](#accuracy)
+    - [English](#english)
+    - [Chinese](#chinese)
+- [Installation](#installation)
+  - [Clone the repository](#clone-the-repository)
+  - [Option 1: Conda](#option-1-conda)
+  - [Option 2: uv](#option-2-uv)
+- [Docker (recommended)](#docker-recommended)
+  - [1. Start a container](#1-start-a-container)
+  - [2. Run the example inside the container](#2-run-the-example-inside-the-container)
+  - [3. Manage the container](#3-manage-the-container)
+- [Quick Start](#quick-start)
+  - [Configuration](#configuration)
+- [Python API](#python-api)
+  - [Offline transcription (vLLM backend)](#offline-transcription-vllm-backend)
+  - [Streaming transcription (vLLM backend)](#streaming-transcription-vllm-backend)
+- [WebSocket Server](#websocket-server)
+  - [Start and stop the server](#start-and-stop-the-server)
+  - [WebSocket endpoint](#websocket-endpoint)
+  - [Message format](#message-format)
+  - [Example client](#example-client)
+- [Supported Languages](#supported-languages)
+- [Community & Contact](#community--contact)
+  - [WeChat Group](#wechat-group)
+  - [Discord Server](#discord-server)
+  - [Business contact](#business-contact)
+  - [GitHub Issues](#github-issues)
+- [Acknowledgements](#acknowledgements)
+- [Citation](#citation)
+- [License](#license)
+
+---
+
+## Overview
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/resources/R2T2_framework.png" alt="Confucius4-R2T2 framework" width="70%">
+  <p><i>Figure 1. Overall framework of R2T2.</i></p>
+</div>
+
+## Demo
+
+### Side-by-side comparison with GPT-Live-Transcribe
+
+<div align="center">
+  <video controls playsinline preload="metadata" width="90%" src="https://github.com/user-attachments/assets/1b21c04a-766a-434f-96dc-580376b305f1" title="GPT-Live-Transcribe and R2T2 processing the same audio together in real time — a side-by-side comparison.">
+    Your browser does not support embedded video.
+  </video>
+  <p><a href="https://github.com/user-attachments/assets/1b21c04a-766a-434f-96dc-580376b305f1">Watch the comparison video</a></p>
+  <p><i>Figure 2. GPT-Live-Transcribe and R2T2 processing the same audio, shown together in real time — a side-by-side comparison.</i></p>
+</div>
+
+### Additional resources
+
+More demonstrations, comparisons, and supporting resources will be added here.
+
+## Evaluation
+
+> If you are an author or maintainer of a model included in these comparisons and have questions or concerns about the results, please feel free to contact us through the [GitHub issue tracker](https://github.com/netease-youdao/Confucius4-R2T2/issues). We are happy to share evaluation details and work with you to verify or correct them.
+
+### Streaming performance
+
+The streaming API supports decoding chunks from 80 ms to 2 s; the figures below show representative WER/latency trade-offs at 160 ms.
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/resources/asr_en_wer_latency.svg" alt="English WER and retrospective chunk-wise latency comparison across ASR models and configurations" width="80%">
+  <p><i>Figure 3. English WER and retrospective chunk-wise latency across model and configuration settings.</i></p>
+</div>
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/resources/asr_cn_wer_latency.svg" alt="Chinese CER and retrospective chunk-wise latency comparison across ASR models and configurations" width="80%">
+  <p><i>Figure 4. Chinese CER and retrospective chunk-wise latency across model and configuration settings.</i></p>
+</div>
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/resources/asr_pareto_wer_latency.svg" alt="English and Chinese accuracy-latency Pareto frontier for representative streaming ASR configurations" width="96%">
+  <p><i>Figure 5. Accuracy-latency Pareto frontier. Lower-left is better; the frontier uses retrospective chunk-wise mean fuzzy latency.</i></p>
+</div>
+
+### Accuracy
+
+English results use WER (%), and Chinese results use CER (%); lower is better.
+
+※ Pseudo-streaming model: its partial transcript may revise previously emitted text; unmarked models use true streaming, append-only output.
+
+#### English
+
+<div align="center">
+<table>
+<thead><tr>
+<th rowspan="2" scope="col" align="left">Dataset</th>
+<th colspan="2" scope="colgroup" align="center">Qwen</th>
+<th rowspan="2" scope="col" align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>R2T2 (Ours)</strong><br><sub>160ms</sub></th>
+<th colspan="4" scope="colgroup" align="center">Open-source</th>
+<th colspan="3" scope="colgroup" align="center">Proprietary</th>
+</tr><tr>
+<th scope="col" align="center">Qwen3-ASR※<br><sub>2s/u2/t5</sub></th>
+<th scope="col" align="center">Qwen3-ASR base<br><sub>160ms</sub></th>
+<th scope="col" align="center">X-ASR<br><sub>160ms</sub></th>
+<th scope="col" align="center">WhisperRT※<br><sub>200ms</sub></th>
+<th scope="col" align="center">Nemotron<br><sub>160ms</sub></th>
+<th scope="col" align="center">Voxtral<br><sub>160ms</sub></th>
+<th scope="col" align="center">AssemblyAI※<br><sub>min_latency</sub></th>
+<th scope="col" align="center">Commercial A※</th>
+<th scope="col" align="center">Commercial B※</th>
+</tr></thead><tbody>
+<tr>
+<th scope="row" align="left">AMI</th>
+<td align="center">9.25</td>
+<td align="center">24.79</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>11.37</strong></td>
+<td align="center">14.41</td>
+<td align="center">24.19</td>
+<td align="center">18.11</td>
+<td align="center">15.94</td>
+<td align="center">12.00</td>
+<td align="center">13.27</td>
+<td align="center">8.44</td>
+</tr>
+<tr>
+<th scope="row" align="left">Giga-clean</th>
+<td align="center">8.61</td>
+<td align="center">24.37</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>9.60</strong></td>
+<td align="center">10.26</td>
+<td align="center">13.81</td>
+<td align="center">12.67</td>
+<td align="center">11.13</td>
+<td align="center">9.21</td>
+<td align="center">8.84</td>
+<td align="center">9.46</td>
+</tr>
+<tr>
+<th scope="row" align="left">LS-clean</th>
+<td align="center">1.67</td>
+<td align="center">22.30</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>2.13</strong></td>
+<td align="center">3.86</td>
+<td align="center">4.70</td>
+<td align="center">3.71</td>
+<td align="center">2.49</td>
+<td align="center">1.89</td>
+<td align="center">1.73</td>
+<td align="center">1.25</td>
+</tr>
+<tr>
+<th scope="row" align="left">LS-other</th>
+<td align="center">3.54</td>
+<td align="center">25.74</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>4.88</strong></td>
+<td align="center">9.64</td>
+<td align="center">9.86</td>
+<td align="center">8.27</td>
+<td align="center">7.15</td>
+<td align="center">3.37</td>
+<td align="center">3.57</td>
+<td align="center">2.48</td>
+</tr>
+<tr>
+<th scope="row" align="left">SPGI</th>
+<td align="center">2.90</td>
+<td align="center">22.25</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>3.00</strong></td>
+<td align="center">5.14</td>
+<td align="center">8.66</td>
+<td align="center">3.93</td>
+<td align="center">3.06</td>
+<td align="center">2.14</td>
+<td align="center">3.06</td>
+<td align="center">1.74</td>
+</tr>
+<tr>
+<th scope="row" align="left">VoxPopuli</th>
+<td align="center">3.02</td>
+<td align="center">20.71</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>3.07</strong></td>
+<td align="center">5.68</td>
+<td align="center">8.28</td>
+<td align="center">5.69</td>
+<td align="center">6.30</td>
+<td align="center">4.75</td>
+<td align="center">3.17</td>
+<td align="center">3.14</td>
+</tr>
+<tr>
+<th scope="row" align="left">Earnings22</th>
+<td align="center">6.68</td>
+<td align="center">29.72</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>9.36</strong></td>
+<td align="center">15.95</td>
+<td align="center">35.08</td>
+<td align="center">17.22</td>
+<td align="center">11.66</td>
+<td align="center">7.47</td>
+<td align="center">10.32</td>
+<td align="center">8.96</td>
+</tr>
+<tr>
+<th scope="row" align="left">TED-LIUM</th>
+<td align="center">2.33</td>
+<td align="center">19.18</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>3.34</strong></td>
+<td align="center">3.75</td>
+<td align="center">6.67</td>
+<td align="center">5.11</td>
+<td align="center">4.60</td>
+<td align="center">3.23</td>
+<td align="center">3.08</td>
+<td align="center">3.30</td>
+</tr>
+<tr>
+<th scope="row" align="left">EN-RealSI</th>
+<td align="center">6.54</td>
+<td align="center">13.75</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>8.40</strong></td>
+<td align="center">8.97</td>
+<td align="center">35.36</td>
+<td align="center">10.69</td>
+<td align="center">14.75</td>
+<td align="center">9.73</td>
+<td align="center">8.73</td>
+<td align="center">17.05</td>
+</tr>
+</tbody></table></div>
+
+#### Chinese
+
+<div align="center">
+<table>
+<thead><tr>
+<th rowspan="2" scope="col" align="left">Dataset</th>
+<th colspan="2" scope="colgroup" align="center">Qwen</th>
+<th rowspan="2" scope="col" align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>R2T2 (Ours)</strong><br><sub>160ms</sub></th>
+<th colspan="4" scope="colgroup" align="center">Open-source</th>
+<th colspan="3" scope="colgroup" align="center">Proprietary</th>
+</tr><tr>
+<th scope="col" align="center">Qwen3-ASR※<br><sub>2s/u2/t5</sub></th>
+<th scope="col" align="center">Qwen3-ASR base<br><sub>160ms</sub></th>
+<th scope="col" align="center">X-ASR<br><sub>160ms</sub></th>
+<th scope="col" align="center">WhisperRT※<br><sub>200ms</sub></th>
+<th scope="col" align="center">Nemotron<br><sub>160ms</sub></th>
+<th scope="col" align="center">Voxtral<br><sub>160ms</sub></th>
+<th scope="col" align="center">AssemblyAI※<br><sub>min_latency</sub></th>
+<th scope="col" align="center">Commercial A※</th>
+<th scope="col" align="center">Commercial B※</th>
+</tr></thead><tbody>
+<tr>
+<th scope="row" align="left">Wenet-net</th>
+<td align="center">4.94</td>
+<td align="center">19.79</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>5.87</strong></td>
+<td align="center">8.81</td>
+<td align="center">U</td>
+<td align="center">24.70</td>
+<td align="center">23.53</td>
+<td align="center">12.91</td>
+<td align="center">5.13</td>
+<td align="center">4.79</td>
+</tr>
+<tr>
+<th scope="row" align="left">Wenet-meeting</th>
+<td align="center">5.97</td>
+<td align="center">20.38</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>7.27</strong></td>
+<td align="center">11.33</td>
+<td align="center">U</td>
+<td align="center">20.18</td>
+<td align="center">60.54</td>
+<td align="center">11.84</td>
+<td align="center">7.07</td>
+<td align="center">3.75</td>
+</tr>
+<tr>
+<th scope="row" align="left">SPEECHIO-06</th>
+<td align="center">6.10</td>
+<td align="center">24.50</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>7.30</strong></td>
+<td align="center">7.86</td>
+<td align="center">U</td>
+<td align="center">22.52</td>
+<td align="center">32.16</td>
+<td align="center">15.08</td>
+<td align="center">5.67</td>
+<td align="center">5.34</td>
+</tr>
+<tr>
+<th scope="row" align="left">SPEECHIO-07</th>
+<td align="center">6.19</td>
+<td align="center">21.16</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>8.20</strong></td>
+<td align="center">11.22</td>
+<td align="center">U</td>
+<td align="center">24.28</td>
+<td align="center">22.97</td>
+<td align="center">10.84</td>
+<td align="center">6.45</td>
+<td align="center">6.46</td>
+</tr>
+<tr>
+<th scope="row" align="left">CN-RealSI</th>
+<td align="center">3.34</td>
+<td align="center">39.72</td>
+<td align="center" style="background-color: rgba(79, 140, 255, 0.14); border-left: 2px solid #4F8CFF; border-right: 2px solid #4F8CFF;"><strong>3.48</strong></td>
+<td align="center">4.92</td>
+<td align="center">U</td>
+<td align="center">11.52</td>
+<td align="center">8.74</td>
+<td align="center">5.15</td>
+<td align="center">3.99</td>
+<td align="center">3.64</td>
+</tr>
+</tbody></table></div>
+
+## Installation
+
+We recommend using a **fresh, isolated environment**. For local development and
+source installation, use the **Conda** or **uv** environment below. **Docker** is
+recommended for quickly running the project with a preconfigured CUDA and runtime
+environment — see [Docker](#docker-recommended).
+
+### Clone the repository
+
+```bash
+git clone https://github.com/netease-youdao/Confucius4-R2T2.git
+cd Confucius4-R2T2
+```
+
+### Option 1: Conda
+
+```bash
+conda create -n confucius4-r2t2 python=3.12 -y
+conda activate confucius4-r2t2
+
+# Install the package with the vLLM backend
+pip install -e .
+```
+
+### Option 2: uv
+
+```bash
+uv venv --python 3.12
+source .venv/bin/activate
+
+# Install the package with the vLLM backend
+uv pip install -e .
+```
+
+Python 3.10+ is supported. Python 3.12 is the version we test against.
+
+vLLM has strict CUDA / PyTorch compatibility requirements. If the install
+fails to resolve, check the version matrix on the [vLLM website](https://docs.vllm.ai/)
+and pin a combination that matches your CUDA runtime.
+
+## Docker (recommended)
+
+R2T2 runs out of the box on the official **Qwen3-ASR** Docker image, which already ships every runtime library we need.
+
+Pre-built image: [qwenllm/qwen3-asr](https://hub.docker.com/r/qwenllm/qwen3-asr).
+
+Before you begin, install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) to enable GPU access from Docker. If Docker Hub access is slow or unreliable in your region, you may need to configure a registry mirror.
+
+### 1. Start a container
+
+```bash
+LOCAL_WORKDIR=/path/to/your/workspace   # host path that will be mounted into the container
+HOST_PORT=8000
+CONTAINER_PORT=80
+
+docker run --gpus all --name confucius4-r2t2 \
+    -v /var/run/docker.sock:/var/run/docker.sock \
+    -p $HOST_PORT:$CONTAINER_PORT \
+    --mount type=bind,source=$LOCAL_WORKDIR,target=/data/shared/confucius4-r2t2 \
+    --shm-size=4gb \
+    -it qwenllm/qwen3-asr:latest
+```
+
+Your local workspace (`$LOCAL_WORKDIR`) — including a checkout of this repository and the R2T2 checkpoint — will be mounted inside the container at `/data/shared/confucius4-r2t2`. Host port `8000` is mapped to container port `80`; services running inside the container must bind to `0.0.0.0` (not `127.0.0.1`) for port forwarding to work.
+
+### 2. Run the example inside the container
+
+Once inside the container's shell:
+
+```bash
+cd /data/shared/confucius4-r2t2/Confucius4-R2T2
+MODEL_PATH=/data/shared/confucius4-r2t2/Confucius4-R2T2 \
+    ./run_example.sh /path/to/audio.wav
+```
+
+### 3. Manage the container
+
+```bash
+# re-enter after exiting
+docker start confucius4-r2t2
+docker exec -it confucius4-r2t2 bash
+
+# remove completely
+docker rm -f confucius4-r2t2
+```
+
+## Quick Start
+
+Grab any audio file (mono or stereo, any sample rate — it is resampled to 16 kHz internally) and run:
+
+```bash
+./run_example.sh /path/to/audio.wav \
+    --model_path /path/to/Confucius4-R2T2 \
+    --infer_mode stream_vllm \
+    --language Chinese \
+    --chunk_size_ms 160
+```
+
+Logs are written to `run_example.log` by default. Run `./run_example.sh --help` to see the full flag list.
+
+### Configuration
+
+`run_example.sh` reads the following environment variables (all optional):
+
+| Variable              | Default            | Description                                            |
+| --------------------- | ------------------ | ------------------------------------------------------ |
+| `MODEL_PATH`          | (required)         | Path or HF repo id of the R2T2 checkpoint              |
+| `AUDIO`               | first CLI argument | Path to the input audio file                           |
+| `INFER_MODE`          | `stream_vllm`      | `stream_vllm` or `onetime_vllm`                        |
+| `LANGUAGE`            | `Chinese`          | Language hint (e.g. `Chinese`, `English`, …)           |
+| `CHUNK_SIZE_MS`       | `160`              | Streaming chunk size (80 ms–2 s supported)             |
+| `UNFIXED_TOKEN_NUM`   | `1`                | Number of unfixed trailing tokens (rollback window)    |
+| `CONTEXT`             | `""`               | Context / hotword hint prepended to the prompt         |
+| `CUDA_VISIBLE_DEVICES`| `0`                | GPU id(s) to expose                                    |
+| `LOG_FILE`            | `run_example.log`  | Where to write logs                                    |
+
+You can also call `example.py` directly and pass any of these as flags (`--audio`, `--model_path`, `--infer_mode`, `--language`, `--chunk_size_ms`, `--lookahead_ms`, `--unfixed_token_num`, `--context`).
+
+## Python API
+
+Audio inputs can be passed as a local path, a URL, base64 data, or a `(np.ndarray, sr)` tuple. Batched inference is supported. Remember to wrap vLLM code under `if __name__ == '__main__':` to avoid the `spawn` error described in [vLLM Troubleshooting](https://docs.vllm.ai/en/latest/usage/troubleshooting/#python-multiprocessing).
+
+### Offline transcription (vLLM backend)
+
+```python
+import librosa
+from qwen_asr import Qwen3ASRModel
+
+if __name__ == "__main__":
+    asr = Qwen3ASRModel.LLM(
+        model="/path/to/Confucius4-R2T2",
+        gpu_memory_utilization=0.5,
+        max_inference_batch_size=32,
+        max_new_tokens=4096,
+    )
+
+    wav, sr = librosa.load("path/to/audio.wav", sr=16000, mono=True)
+
+    results = asr.transcribe(
+        audio=[(wav, 16000)],
+        language=["Chinese"],       # or [None]
+        return_time_stamps=False,
+    )
+    print(results[0].language, results[0].text)
+```
+
+### Streaming transcription (vLLM backend)
+
+```python
+import librosa
+from qwen_asr import Qwen3ASRModel
+
+if __name__ == "__main__":
+    asr = Qwen3ASRModel.LLM(
+        model="/path/to/Confucius4-R2T2",
+        gpu_memory_utilization=0.4,
+        max_new_tokens=4,           # keep small for low-latency streaming
+    )
+
+    wav, sr = librosa.load("path/to/audio.wav", sr=16000, mono=True)
+
+    state = asr.init_streaming_state(
+        context="",                 # optional hotword / topic hint
+        language="Chinese",         # or None
+        unfixed_chunk_num=0,
+        unfixed_token_num=1,
+        chunk_size_sec=0.16,
+    )
+
+    step = int(0.16 * 16000)
+    for pos in range(0, len(wav), step):
+        seg = wav[pos : pos + step]
+        _, text = asr.streaming_transcribe(seg, state, max_new_tokens=2)
+        print("text:", text)
+
+    asr.finish_streaming_transcribe(state)
+    print("final:", state.text)
+```
+
+For a complete streaming example with adaptive `max_new_tokens` and initial-chunk lookahead handling, see [`example.py`](https://github.com/netease-youdao/Confucius4-R2T2/blob/master/example.py).
+
+## WebSocket Server
+
+For real-time, multi-client streaming ASR, the [GitHub repository](https://github.com/netease-youdao/Confucius4-R2T2) ships a ready-to-run WebSocket server (`ws_server.py`), a launcher script (`run_start_server.sh`), and a reference Python client (`ws_client.py`).
+
+### Start and stop the server
+
+```bash
+# Start with a VAD model
+./run_start_server.sh start \
+    --model_path /path/to/Confucius4-R2T2 \
+    --vad_model_path /path/to/Stream-VAD \
+    --port 8272 \
+    --gpu 0
+
+# Stop
+./run_start_server.sh kill
+
+# Restart in one step
+./run_start_server.sh restart \
+    --model_path /path/to/Confucius4-R2T2 \
+    --vad_model_path /path/to/Stream-VAD \
+    --port 8272 \
+    --gpu 0
+```
+
+| Flag                  | Env var              | Default                                                        | Description                                          |
+| --------------------- | -------------------- | -------------------------------------------------------------- | ---------------------------------------------------- |
+| `-m`, `--model_path`  | `ASR_MODEL_PATH`     | (required)                                                    | Path or HF repo id of the R2T2 checkpoint            |
+| `-v`,`--vad_model_path`    | `VAD_MODEL_PATH`     | `checkpoints/vad/Stream-VAD`                                        | Path to the FireRedVAD Stream-VAD model              |
+| `-p`, `--port`        | `PORT`               | `8272`                                                         | Port the WebSocket server binds to                   |
+| `-g`, `--gpu`         | `CUDA_VISIBLE_DEVICES` | `0`                                                            | GPU id(s) exposed to the server process              |
+| `-h`, `--host`        | `HOST_TAG`           | `localhost`                                                    | Host tag used only in the log file name              |
+
+The launcher resolves its own directory, so it can be invoked from anywhere. Logs are written to `nohup_service_ws_<host_tag>_<port>.log` in the current directory. The FireRedVAD model is available from [Hugging Face](https://huggingface.co/FireRedTeam/FireRedVAD/tree/main). We recommend downloading the model files into this repository's `checkpoints` directory:
+
+```bash
+# The FireRedVAD repo ships several detectors, but only the streaming one is
+# needed. Both commands below keep the `Stream-VAD/` folder name, so the files
+# land in checkpoints/vad/Stream-VAD with no extra nesting.
+
+# Option A — hf CLI (pip install -U "huggingface_hub[cli]")
+hf download FireRedTeam/FireRedVAD \
+    --include "Stream-VAD/*" \
+    --local-dir checkpoints/vad
+
+# Option B — git clone
+git clone https://huggingface.co/FireRedTeam/FireRedVAD
+cp -r FireRedVAD/Stream-VAD checkpoints/vad/
+```
+
+Either command leaves the model at `checkpoints/vad/Stream-VAD`, which is exactly what `--vad_model_path` defaults to — so you can drop the flag entirely.
+
+### WebSocket endpoint
+
+| Path                       | Behavior                                                                 |
+| -------------------------- | ------------------------------------------------------------------------ |
+| `/asr_stream_api_v1`       | Streaming ASR. Each message's `text` is the **new (incremental)** chunk. |
+
+### Message format
+
+**Client → Server:**
+
+- Send raw 16 kHz mono PCM as `int16` binary frames (the reference client uses ≈160 ms per frame, i.e. 2560 samples × 2 bytes).
+- Send the string `"YOUDAO_ONETIME_ASR_STREAM_EOS"` to signal end-of-audio; the server will emit any final text and close.
+
+**Server → Client:** JSON messages of the form
+
+```json
+{
+  "status": "success",
+  "requestId": "<uuid>",
+  "msg": {
+    "text": "hello",
+    "reset": false,
+    "asr_cost_ms": 35.4,
+    "total_cost_ms": 42.0
+  }
+}
+```
+
+- `text` is the newly recognized (incremental) segment since the previous message. Concatenate them client-side to get the full transcript.
+
+### Example client
+
+`ws_client.py` is a minimal example that streams a WAV file to the server and prints the responses.
+
+```bash
+# Uses the default URI (ws://localhost:8272/asr_stream_api_v1) and built-in sample audio
+python ws_client.py
+
+# Point at a custom endpoint and audio file
+python ws_client.py \
+    --uri wss://your.host/asr_stream_api_v1 \
+    --audio resources/test.wav \
+    --save service_ws_test \
+    --audio-id test.wav
+```
+
+Command-line options:
+
+| Flag                 | Env var        | Default                                       | Description                                                        |
+| -------------------- | -------------- | --------------------------------------------- | ------------------------------------------------------------------ |
+| `--uri` / `-u`       | `ASR_WS_URI`   | `ws://localhost:8272/asr_stream_api_v1`       | WebSocket endpoint to connect to.                                  |
+| `--audio` / `-a`     | —              | built-in sample path                          | Input audio file (WAV, 16 kHz mono recommended).                   |
+| `--save` / `-s`      | —              | `service_ws_test`                             | File to append the final transcript to.                            |
+| `--audio-id`         | —              | basename of `--audio`                         | Identifier written next to the result in `--save`.                 |
+
+## Supported Languages
+
+R2T2 is optimized for streaming recognition in Chinese and English. Beyond these primary languages, it retains useful cross-lingual streaming capability on languages such as French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish, Arabic, etc.
+
+## Community & Contact
+
+Join our community to ask questions, share ideas, and connect with other users and developers.
+
+### WeChat Group
+
+Scan the QR code below to join our WeChat group:
+
+<img src="https://raw.githubusercontent.com/netease-youdao/Confucius4-R2T2/refs/heads/master/resources/wechat-qrcode.png" alt="WeChat group QR code" width="200">
+
+### Discord Server
+
+[Join our Discord server](https://discord.gg/GfhaWkCyb)
+
+### Business contact
+
+For high-concurrency, production-grade, domestically deployable, or private deployment solutions, as well as business inquiries and partnership opportunities, please feel free to contact us through the channels below.
+
+- **Phone:** +86 010-82558901
+- **Email:** [AIcloud_Business@corp.youdao.com](mailto:AIcloud_Business@corp.youdao.com)
+
+### GitHub Issues
+
+We also welcome discussions in this repository’s [Issues](https://github.com/netease-youdao/Confucius4-R2T2/issues) section. Feel free to ask questions, report bugs, or suggest improvements!
+
+---
+
+## Acknowledgements
+
+We sincerely thank the Alibaba Qwen team for open-sourcing the [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) modeling code, which provides the architectural foundation for R2T2.
+
+## Citation
+
+If you use this repository or the R2T2 checkpoint in your research, please cite **Confucius4-R2T2** (this project):
+
+```bibtex
+@misc{Confucius4-R2T2,
+  title        = {Confucius4-R2T2: A Low Latency and High Accuracy Real-Time Speech Recognition Model},
+  author       = {NetEase Youdao},
+  year         = {2026},
+  howpublished = {https://github.com/netease-youdao/Confucius4-R2T2}
+}
+```
+
+## License
+
+R2T2 uses **dual licensing** to distinguish the source code from the model weights:
+
+- **Code** in the accompanying GitHub repository is released under the [Apache License 2.0](https://github.com/netease-youdao/Confucius4-R2T2/blob/master/LICENSE) and is free to use, modify, and redistribute (including commercially) under the terms of that license.
+- **Model weights** are released under the [NetEase Model Use License Agreement](https://github.com/netease-youdao/Confucius4-R2T2/blob/master/MODEL_LICENSE).

added_tokens.json

@@ -0,0 +1,64 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<asr_text>": 151704,
+  "<blank10>": 151686,
+  "<blank11>": 151687,
+  "<blank12>": 151688,
+  "<blank13>": 151689,
+  "<blank14>": 151690,
+  "<blank15>": 151691,
+  "<blank16>": 151692,
+  "<blank17>": 151693,
+  "<blank18>": 151694,
+  "<blank19>": 151695,
+  "<blank1>": 151677,
+  "<blank20>": 151696,
+  "<blank21>": 151697,
+  "<blank22>": 151698,
+  "<blank23>": 151699,
+  "<blank24>": 151700,
+  "<blank25>": 151701,
+  "<blank26>": 151702,
+  "<blank27>": 151703,
+  "<blank2>": 151678,
+  "<blank3>": 151679,
+  "<blank4>": 151680,
+  "<blank5>": 151681,
+  "<blank6>": 151682,
+  "<blank7>": 151683,
+  "<blank8>": 151684,
+  "<blank9>": 151685,
+  "<non_speech>": 151675,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<tts_pad>": 151671,
+  "<tts_text_bos>": 151672,
+  "<tts_text_bos_single>": 151674,
+  "<tts_text_eod>": 151673,
+  "<|audio_end|>": 151670,
+  "<|audio_pad|>": 151676,
+  "<|audio_start|>": 151669,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.json

@@ -0,0 +1 @@
+{"chat_template": "{%- set ns = namespace(system_text=\"\") -%}\n{%- for m in messages -%}\n  {%- if m.role == 'system' -%}\n    {%- if m.content is string -%}\n      {%- set ns.system_text = ns.system_text + m.content -%}\n    {%- else -%}\n      {%- for c in m.content -%}\n        {%- if c.type == 'text' and (c.text is defined) -%}\n          {%- set ns.system_text = ns.system_text + c.text -%}\n        {%- endif -%}\n      {%- endfor -%}\n    {%- endif -%}\n  {%- endif -%}\n{%- endfor -%}\n\n{%- set ns2 = namespace(audio_tokens=\"\") -%}\n{%- for m in messages -%}\n  {%- if m.content is not string -%}\n    {%- for c in m.content -%}\n      {%- if c.type == 'audio' or ('audio' in c) or ('audio_url' in c) -%}\n        {%- set ns2.audio_tokens = ns2.audio_tokens + \"<|audio_start|><|audio_pad|><|audio_end|>\" -%}\n      {%- endif -%}\n    {%- endfor -%}\n  {%- endif -%}\n{%- endfor -%}\n\n{{- '<|im_start|>system\\n' + (ns.system_text if ns.system_text is string else '') + '<|im_end|>\\n' -}}\n{{- '<|im_start|>user\\n' + ns2.audio_tokens + '<|im_end|>\\n' -}}\n{%- if add_generation_prompt -%}\n{{- '<|im_start|>assistant\\n' -}}\n{%- endif -%}"}

config.json

@@ -0,0 +1,221 @@
+{
+    "architectures": [
+      "Qwen3ASRForConditionalGeneration"
+    ],
+    "model_type": "qwen3_asr",
+    "support_languages": [
+      "Chinese",
+      "English",
+      "Cantonese",
+      "Arabic",
+      "German",
+      "French",
+      "Spanish",
+      "Portuguese",
+      "Indonesian",
+      "Italian",
+      "Korean",
+      "Russian",
+      "Thai",
+      "Vietnamese",
+      "Japanese",
+      "Turkish",
+      "Hindi",
+      "Malay",
+      "Dutch",
+      "Swedish",
+      "Danish",
+      "Finnish",
+      "Polish",
+      "Czech",
+      "Filipino",
+      "Persian",
+      "Greek",
+      "Romanian",
+      "Hungarian",
+      "Macedonian"
+    ],
+    "thinker_config": {
+      "model_type": "qwen3_asr",
+      "architectures": [
+        "Qwen3ASRForConditionalGeneration"
+      ],
+      "audio_config": {
+        "_name_or_path": "",
+        "activation_dropout": 0,
+        "activation_function": "gelu",
+        "add_cross_attention": false,
+        "architectures": null,
+        "attention_dropout": 0,
+        "bad_words_ids": null,
+        "begin_suppress_tokens": null,
+        "bos_token_id": null,
+        "chunk_size_feed_forward": 0,
+        "conv_chunksize": 500,
+        "cross_attention_hidden_size": null,
+        "d_model": 1024,
+        "decoder_start_token_id": null,
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "downsample_hidden_size": 480,
+        "dropout": 0,
+        "dtype": null,
+        "early_stopping": false,
+        "encoder_attention_heads": 16,
+        "encoder_ffn_dim": 4096,
+        "encoder_layers": 24,
+        "encoder_no_repeat_ngram_size": 0,
+        "eos_token_id": null,
+        "exponential_decay_length_penalty": null,
+        "finetuning_task": null,
+        "forced_bos_token_id": null,
+        "forced_eos_token_id": null,
+        "id2label": {
+          "0": "LABEL_0",
+          "1": "LABEL_1"
+        },
+        "initializer_range": 0.02,
+        "is_decoder": false,
+        "is_encoder_decoder": false,
+        "label2id": {
+          "LABEL_0": 0,
+          "LABEL_1": 1
+        },
+        "length_penalty": 1.0,
+        "max_length": 20,
+        "max_source_positions": 1500,
+        "min_length": 0,
+        "model_type": "qwen3_asr_audio_encoder",
+        "n_window": 50,
+        "n_window_infer": 800,
+        "no_repeat_ngram_size": 0,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_hidden_layers": 24,
+        "num_mel_bins": 128,
+        "num_return_sequences": 1,
+        "output_attentions": false,
+        "output_dim": 2048,
+        "output_hidden_states": false,
+        "output_scores": false,
+        "pad_token_id": null,
+        "prefix": null,
+        "problem_type": null,
+        "pruned_heads": {},
+        "remove_invalid_values": false,
+        "repetition_penalty": 1.0,
+        "return_dict": true,
+        "return_dict_in_generate": false,
+        "scale_embedding": false,
+        "sep_token_id": null,
+        "suppress_tokens": null,
+        "task_specific_params": null,
+        "temperature": 1.0,
+        "tf_legacy_loss": false,
+        "tie_encoder_decoder": false,
+        "tie_word_embeddings": true,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
+        "torchscript": false,
+        "typical_p": 1.0,
+        "use_bfloat16": false
+      },
+      "audio_end_token_id": 151670,
+      "audio_start_token_id": 151669,
+      "audio_token_id": 151676,
+      "dtype": "bfloat16",
+      "initializer_range": 0.02,
+      "text_config": {
+        "_name_or_path": "",
+        "add_cross_attention": false,
+        "architectures": null,
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "bad_words_ids": null,
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