---
language:
  - vi
license: apache-2.0
tags:
  - onnx
  - wav2vec2
  - audio-classification
  - pronunciation
  - vietnamese
  - speech
pipeline_tag: audio-classification
library_name: onnxruntime
base_model: nguyenvulebinh/wav2vec2-large-vi-vlsp2020
model-index:
  - name: wav2vec2-vi-pronunciation-onnx
    results:
      - task:
          type: audio-classification
          name: Audio Classification
        metrics:
          - type: accuracy
            value: 80.3
            name: Validation Accuracy
---

# 🎤 Vietnamese Pronunciation Classifier (ONNX)

Mô hình phân loại phát âm tiếng Việt (Miền Nam) — phân biệt **phát âm đúng** và **phát âm sai** cho các phụ âm D/R/GI.

## Model Details

| Attribute | Value |
|-----------|-------|
| **Base Model** | [wav2vec2-large-vi-vlsp2020](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) |
| **Format** | ONNX (cross-platform) |
| **Task** | Binary Classification (correct / wrong) |
| **Language** | Vietnamese (Southern dialect) |
| **Sample Rate** | 16 kHz |
| **Audio Duration** | 500ms per sample |
| **Validation Accuracy** | 80.3% |
| **ONNX Opset** | 14 |

## Classes

| Label | Class ID | Description |
|-------|----------|-------------|
| ✅ `correct` | 0 | Phát âm đúng |
| ❌ `wrong` | 1 | Phát âm sai (nhầm D/R/GI) |

## Quick Start — ONNX Runtime

```bash
pip install onnxruntime librosa numpy
```

```python
import numpy as np
import librosa
import onnxruntime as ort
from huggingface_hub import hf_hub_download

# 1. Download model
repo_id = "Bao2311/wav2vec2-vi-pronunciation-onnx"
onnx_path = hf_hub_download(repo_id, "phoneme_classifier.onnx")
data_path = hf_hub_download(repo_id, "phoneme_classifier.onnx.data")

# 2. Load ONNX session
session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])

# 3. Load & preprocess audio (16kHz, 500ms)
audio, _ = librosa.load("your_audio.wav", sr=16000, mono=True)
audio = audio[:8000]  # crop to 500ms = 8000 samples
if len(audio) < 8000:
    audio = np.pad(audio, (0, 8000 - len(audio)))

# 4. Normalize
audio = audio.astype(np.float32)
mean, std = audio.mean(), audio.std()
if std > 0:
    audio = (audio - mean) / std

# 5. Inference
input_values = audio.reshape(1, -1)
logits = session.run(None, {"input_values": input_values})[0][0]

# 6. Softmax → prediction
exp_logits = np.exp(logits - np.max(logits))
probs = exp_logits / exp_logits.sum()
pred = int(np.argmax(probs))

labels = {0: "correct ✅", 1: "wrong ❌"}
print(f"Prediction: {labels[pred]} (confidence: {probs[pred]:.1%})")
```

## Files

| File | Size | Description |
|------|------|-------------|
| `phoneme_classifier.onnx` | ~2.5 MB | ONNX model graph |
| `phoneme_classifier.onnx.data` | ~1.2 GB | Model weights (external data) |
| `config.json` | <1 KB | Model configuration |
| `preprocessor_config.json` | <1 KB | Feature extractor config |

## Training Details

- **Base model:** `nguyenvulebinh/wav2vec2-large-vi-vlsp2020`
- **Epochs:** 10
- **Batch Size:** 4
- **Learning Rate:** 1e-4
- **Optimizer:** AdamW
- **Loss:** CrossEntropyLoss
- **Frozen layers:** Wav2Vec2 feature extractor + first 8 encoder layers

## Limitations

- Chỉ hỗ trợ tiếng Việt (giọng Miền Nam)
- Tối ưu cho audio 500ms, 16kHz mono
- Binary classification (đúng/sai), chưa phân loại chi tiết loại lỗi

## License

Apache 2.0