Update README.md

README.md

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 license: cc-by-nc-nd-4.0
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-# Vietnamese Speech-to-Text (ASR) — ZipFormer-30M-RNNT-6000h
+# Vietnamese Streaming Speech-to-Text (ASR) — ZipFormer-30M-RNNT-Streaming-6000h
 
 ## 🔍 Overview
-The **Vietnamese Speech-to-Text (ASR)** model is built on the **ZipFormer architecture** — an improved variant of the Conformer — featuring only **30 million parameters** yet achieving **exceptional performance** in both speed and accuracy.  
-On CPU, the model can transcribe a **12-second audio clip in just 0.3 seconds**, significantly faster than most traditional ASR systems without requiring a GPU.
+The **Vietnamese Streaming Speech-to-Text (ASR)** model is built on the **ZipFormer architecture with chunk size 16,32,64** — an improved variant of the Conformer — featuring only **30 million parameters** yet achieving **exceptional performance** in both speed and accuracy.  
+On CPU, the none-streaming model can transcribe a **12-second audio clip in just 0.3 seconds**, significantly faster than most traditional ASR systems without requiring a GPU.
 
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 ## 🚀 Online Demo
 
-You can try the model directly here:  
+You can test the streaming and none-streaming model directly here:  
 👉 https://huggingface.co/spaces/hynt/k2-automatic-speech-recognition-demo
 
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@@ -20,7 +20,8 @@ You can try the model directly here:
 - **Architecture:** ZipFormer  
 - **Parameters:** ~30M  
 - **Language:** Vietnamese  
-- **Loss Function:** RNN-Transducer (RNNT Loss)  
+- **Loss Function:** RNN-Transducer (RNNT Loss)
+- **Chunk Size:** 16, 32, 64  
 - **Framework:** PyTorch + k2
 - **Training strategy**: Carefully preprocess the data, apply an augmentation strategy based on the distribution of out-of-vocabulary (OOV) tokens and refine the transcriptions using Whisper.  
 - **Optimized for:** High-speed CPU inference  
@@ -41,21 +42,21 @@ The model was trained on approximately **6000 hours of high-quality Vietnamese s
 
 ## 🧪 Evaluation Results
 
-| **Dataset** | **ZipFormer-30M-6000h** | **ChunkFormer-110M-3000h** | **PhoWhisper-Large-1.5B-800h** | **VietASR-ZipFormer-68M-70.000h** |
-|--------------|--------------------------|-----------------------------|--------------------------------|---------------------------------|
-| **VLSP2020-Test-T1** | **12.29** | 14.09 | 13.75 | 14.45 |
-| **VLSP2023-PublicTest** | **10.40** | 16.15 | 16.83 | 14.70 |
-| **VLSP2023-PrivateTest** | **11.10** | 17.12 | 17.10 | 15.07 |
-| **VLSP2025-PublicTest** | **7.97** | 15.55 | 16.14 | 13.55 |
-| **VLSP2025-PrivateTest** | **8.10** | 16.07 | 16.31 | 13.97 |
-| **GigaSpeech2-Test** | 7.56 | 10.35 | 10.00 | **6.88** |
+| **Dataset** | **ZipFormer-30M-6000h** | **ZipFormer-30M-Streaming-6000h** | **ChunkFormer-110M-3000h** | **PhoWhisper-Large-1.5B-800h** | **VietASR-ZipFormer-68M-70.000h** |
+|--------------|--------------------------|------------------------------------|-----------------------------|--------------------------------|---------------------------------|
+| **VLSP2020-Test-T1** | **12.29** | -- | 14.09 | 13.75 | 14.45 |
+| **VLSP2023-PublicTest** | **10.40** | -- | 16.15 | 16.83 | 14.70 |
+| **VLSP2023-PrivateTest** | **11.10** | -- | 17.12 | 17.10 | 15.07 |
+| **VLSP2025-PublicTest** | **7.97** | -- | 15.55 | 16.14 | 13.55 |
+| **VLSP2025-PrivateTest** | **8.10** | -- | 16.07 | 16.31 | 13.97 |
+| **GigaSpeech2-Test** | 7.56 | -- | 10.35 | 10.00 | **6.88** |
 
 > Lower is better (WER %)
 
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 ## 🏆 Achievements
-By training this model architecture on 4,000 hours of data, I **won First Place** in the **Vietnamese Language Speech Processing (VLSP)** competition **2025**.
+By training this none-streaming model architecture on 4,000 hours of data, I **won First Place** in the **Vietnamese Language Speech Processing (VLSP)** competition **2025**.
 Comprehensive details about **training data**, **optimization strategies**, **architecture improvements**, and **evaluation methodologies** are available in the paper below:
 
 👉 [Read the full paper on Overleaf](https://www.overleaf.com/read/wjntrgchhbgv#48aa25)
@@ -76,9 +77,10 @@ Comprehensive details about **training data**, **optimization strategies**, **ar
 Please refer to the following guides for instructions on how to run and deploy this model:  
 - **For Torch JIT Script:** [https://k2-fsa.github.io/sherpa/](https://k2-fsa.github.io/sherpa/)  
 - **For ONNX:** [https://k2-fsa.github.io/sherpa/onnx/](https://k2-fsa.github.io/sherpa/onnx/)
+- **For Streaming Web Test:**: [https://github.com/k2-fsa/sherpa/tree/master/sherpa/bin](https://github.com/k2-fsa/sherpa/tree/master/sherpa/bin)
 
 ## 💬 Summary
-The **ZipFormer-30M-RNNT-6000h** model demonstrates that a lightweight architecture can still achieve state-of-the-art accuracy for Vietnamese ASR.  
+The **ZipFormer-30M-RNNT-6000h** and **ZipFormer-30M-RNNT-Streaming-6000h** model demonstrates that a lightweight architecture can still achieve state-of-the-art accuracy for Vietnamese ASR.  
 It is designed for **fast deployment on CPU-based systems**, making it ideal for **real-time speech recognition**, **callbots**, and **embedded speech interfaces**.
 
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