---
language:
- lus
license: apache-2.0
pipeline_tag: automatic-speech-recognition
base_model: Qwen/Qwen3-ASR-0.6B
tags:
- generated_from_trainer
datasets:
- andrewbawitlung/MiZonal-v3.0
metrics:
- wer
- cer
model-index:
- name: qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: MiZonal v3.0
      type: andrewbawitlung/MiZonal-v3.0
      config: default
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 22.2146
    - name: Cer
      type: cer
      value: 5.2331
    - name: Real Time Factor
      type: rtf
      value: 0.0685
---
![Mizo Automatic Speech Recognition (ASR) Models v3.0](banner.jpg)

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06

This model is a fine-tuned version of [Qwen/Qwen3-ASR-0.6B](https://huggingface.co/Qwen/Qwen3-ASR-0.6B) on the **MiZonal v3.0** dataset. 
Note: ~1 hour of conversational speech was added to this dataset version.

It achieves the following results on the evaluation set:
- Wer: 22.2146
- Cer: 5.2331
- Real Time Factor: 0.0685

## Quick Inference


```python
import torch
import librosa
from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06")
model = Qwen2AudioForConditionalGeneration.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06").to(device)

audio, sr = librosa.load("your_audio.wav", sr=16000)

conversation = [
    {"role": "user", "content": [
        {"type": "audio", "audio_url": "your_audio.wav"},
        {"type": "text", "text": "Transcribe the audio:"}
    ]}
]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
inputs = processor(text=text, audios=[audio], return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to(device)

with torch.no_grad():
    generate_ids = model.generate(**inputs, max_length=256)

generate_ids = generate_ids[:, inputs.input_ids.size(1):]
transcription = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(transcription)
```


## Model description

### Experiment Configurations
This repository is part of a series of experiments. The different configurations are:
- **E1 (Baseline):** Standard training configuration.
- **E2 (Noise):** Training with background noise augmentation.
- **E3 (Speed):** Training with speed perturbation augmentation.
- **E4 (SpecAug):** Training with SpecAugment (time and frequency masking).
- **E5 (Combined):** Training with a combination of all augmentations.

### All Models in this Family
| Experiment | Hugging Face Repository |
| :--- | :--- |
| **E1 (Baseline)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E1-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E1-lus-v2026.06) |
| **E2 (Noise)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06) |
| **E3 (Speed)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-lus-v2026.06) |
| **E4 (SpecAug)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06) |
| **E5 (Combined)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E5-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E5-lus-v2026.06) |

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: OptimizerNames.ADAMW_TORCH_FUSED
- lr_scheduler_type: SchedulerType.LINEAR
- num_epochs: 8

### Training results

| step | epoch | train_loss | eval_loss | eval_wer | eval_cer | learning_rate | grad_norm |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 200 | 0.36 | 0.3447 | 0.5944 | 43.21 | 11.49 | 1.95e-05 | 27.88 |
| 400 | 0.73 | 0.2183 | 0.3971 | 32.67 | 8.08 | 1.86e-05 | 9.94 |
| 600 | 1.09 | 0.1382 | 0.3338 | 27.18 | 6.40 | 1.76e-05 | 9.44 |
| 800 | 1.46 | 0.0984 | 0.3060 | 24.81 | 5.92 | 1.67e-05 | 4.19 |
| 1000 | 1.82 | 0.1005 | 0.2859 | 23.03 | 5.36 | 1.58e-05 | 4.47 |
| 1200 | 2.19 | 0.0763 | 0.2803 | 21.74 | 5.16 | 1.48e-05 | 6.12 |
| 1400 | 2.55 | 0.0566 | 0.2762 | 21.13 | 4.91 | 1.39e-05 | 4.47 |
| 1600 | 2.91 | 0.0576 | 0.2692 | 21.14 | 4.81 | 1.30e-05 | 4.25 |
| 1800 | 3.28 | 0.0401 | 0.2816 | 20.85 | 4.85 | 1.20e-05 | 3.98 |
| 2000 | 3.64 | 0.0317 | 0.2860 | 20.30 | 4.69 | 1.11e-05 | 6.34 |
| 2200 | 4.01 | 0.0396 | 0.2856 | 20.52 | 4.71 | 1.02e-05 | 4.41 |
| 2400 | 4.37 | 0.0219 | 0.3018 | 20.54 | 4.82 | 9.26e-06 | 4.47 |
| 2600 | 4.74 | 0.0200 | 0.2963 | 20.79 | 4.90 | 8.33e-06 | 6.00 |
| 2800 | 5.10 | 0.0109 | 0.3231 | 20.76 | 4.80 | 7.40e-06 | 2.19 |
| 3000 | 5.46 | 0.0113 | 0.3205 | 20.75 | 4.85 | 6.47e-06 | 2.75 |
| 3200 | 5.83 | 0.0100 | 0.3252 | 20.54 | 4.78 | 5.54e-06 | 1.66 |
| 3400 | 6.19 | 0.0079 | 0.3390 | 21.07 | 4.99 | 4.61e-06 | 0.77 |
| 3600 | 6.56 | 0.0084 | 0.3384 | 20.97 | 4.97 | 3.68e-06 | 1.29 |
| 3800 | 6.92 | 0.0083 | 0.3375 | 20.72 | 4.85 | 2.76e-06 | 2.17 |
| 4000 | 7.29 | 0.0087 | 0.3430 | 20.73 | 4.89 | 1.83e-06 | 2.41 |
| 4200 | 7.65 | 0.0070 | 0.3444 | 20.72 | 4.91 | 8.97e-07 | 3.73 |