Upload README.md

README.md

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-license: cc-by-nc-sa-4.0
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+license: cc-by-nc-nd-4.0
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+# Vietnamese Speech-to-Text (ASR) — ZipFormer-30M-RNNT-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.
+
+---
+
+## 🚀 Online Demo
+
+You can try the model directly here:  
+👉 https://huggingface.co/spaces/hynt/k2-automatic-speech-recognition-demo
+
+---
+
+## ⚙️ Model Architecture and Training strategy:
+- **Architecture:** ZipFormer  
+- **Parameters:** ~30M  
+- **Language:** Vietnamese  
+- **Loss Function:** RNN-Transducer (RNNT Loss)  
+- **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  
+
+---
+
+## 🧠 Training Data
+The model was trained on approximately **6000 hours of high-quality Vietnamese speech** collected from various public datasets:
+
+| Dataset |  |  |
+|----------|----------|----------|
+| VLSP2020 | VLSP2021 | VLSP2023-voting-pseudo-labeled |
+| VLSP2023 | FPT | VIET_BUD500 |
+| VietSpeech | FLEURS | VietMed_Labeled |
+| Sub-GigaSpeech2-Vi | ViVoice | Sub-PhoAudioBook |
+
+---
+
+## 🧪 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** |
+
+> Lower is better (WER %)
+
+---
+
+## 🏆 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**.
+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)
+
+---
+
+## ⚡ Inference Speed
+
+| **Device** | **Audio Length** | **Inference Time** |
+|-------------|------------------|--------------------|
+| CPU (Hugging Face Basic) | 12 seconds | **0.3 s** |
+| GPU (RTX 3090) | 12 seconds | **< 0.1 s** |
+
+---
+
+## ⚙️ How to Run This Model
+
+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/)
+
+## 💬 Summary
+The **ZipFormer-30M-RNNT-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**.
+
+---