---
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-E3-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: 18.9850
    - name: Cer
      type: cer
      value: 4.2170
    - name: Real Time Factor
      type: rtf
      value: 0.0697
---
![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-E3-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: 18.9850
- Cer: 4.2170
- Real Time Factor: 0.0697

## 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-E3-lus-v2026.06")
model = Qwen2AudioForConditionalGeneration.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-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 | 0.12 | 0.7348 | 1.0950 | 66.07 | 21.06 | 1.51e-05 | 33.00 |
| 400 | 0.24 | 0.2043 | 0.4573 | 36.26 | 9.28 | 1.98e-05 | 9.00 |
| 600 | 0.36 | 0.1685 | 0.3415 | 27.38 | 6.61 | 1.95e-05 | 8.50 |
| 800 | 0.49 | 0.1129 | 0.3107 | 24.91 | 5.80 | 1.92e-05 | 5.50 |
| 1000 | 0.61 | 0.1239 | 0.2829 | 22.88 | 5.29 | 1.89e-05 | 4.62 |
| 1200 | 0.73 | 0.0943 | 0.2676 | 21.77 | 5.23 | 1.86e-05 | 5.00 |
| 1400 | 0.85 | 0.0740 | 0.2625 | 21.12 | 4.89 | 1.82e-05 | 4.53 |
| 1600 | 0.97 | 0.0765 | 0.2584 | 21.08 | 4.94 | 1.79e-05 | 3.56 |
| 1800 | 1.09 | 0.0375 | 0.2681 | 20.73 | 4.72 | 1.76e-05 | 4.09 |
| 2000 | 1.21 | 0.0411 | 0.2577 | 19.94 | 4.56 | 1.73e-05 | 1.96 |
| 2200 | 1.34 | 0.0347 | 0.2623 | 19.56 | 4.43 | 1.70e-05 | 5.03 |
| 2400 | 1.46 | 0.0325 | 0.2672 | 19.84 | 4.52 | 1.67e-05 | 5.09 |
| 2600 | 1.58 | 0.0306 | 0.2619 | 19.00 | 4.37 | 1.64e-05 | 2.80 |
| 2800 | 1.70 | 0.0282 | 0.2636 | 19.22 | 4.31 | 1.61e-05 | 2.92 |
| 3000 | 1.82 | 0.0266 | 0.2612 | 18.38 | 4.24 | 1.58e-05 | 2.70 |
| 3200 | 1.94 | 0.0183 | 0.2616 | 19.08 | 4.44 | 1.55e-05 | 4.00 |
| 3400 | 2.06 | 0.0107 | 0.2810 | 22.51 | 8.80 | 1.51e-05 | 2.66 |
| 3600 | 2.19 | 0.0158 | 0.2766 | 18.65 | 4.23 | 1.48e-05 | 2.73 |
| 3800 | 2.31 | 0.0114 | 0.2837 | 18.78 | 4.33 | 1.45e-05 | 1.63 |
| 4000 | 2.43 | 0.0135 | 0.2862 | 18.95 | 4.31 | 1.42e-05 | 6.94 |
| 4200 | 2.55 | 0.0123 | 0.2905 | 18.46 | 4.24 | 1.39e-05 | 4.53 |
| 4400 | 2.67 | 0.0073 | 0.2967 | 18.55 | 4.31 | 1.36e-05 | 2.66 |
| 4600 | 2.79 | 0.0073 | 0.3002 | 18.21 | 4.28 | 1.33e-05 | 2.28 |
| 4800 | 2.91 | 0.0074 | 0.3017 | 18.03 | 4.21 | 1.30e-05 | 1.87 |
| 5000 | 3.04 | 0.0042 | 0.3065 | 18.30 | 4.29 | 1.27e-05 | 0.58 |
| 5200 | 3.16 | 0.0034 | 0.3183 | 18.12 | 4.16 | 1.24e-05 | 0.80 |
| 5400 | 3.28 | 0.0027 | 0.3178 | 18.19 | 4.22 | 1.20e-05 | 1.76 |
| 5600 | 3.40 | 0.0039 | 0.3146 | 18.23 | 4.24 | 1.17e-05 | 0.49 |
| 5800 | 3.52 | 0.0024 | 0.3200 | 18.22 | 4.28 | 1.14e-05 | 0.29 |
| 6000 | 3.64 | 0.0015 | 0.3197 | 17.88 | 4.23 | 1.11e-05 | 0.43 |
| 6200 | 3.76 | 0.0021 | 0.3237 | 17.94 | 4.30 | 1.08e-05 | 0.99 |
| 6400 | 3.89 | 0.0020 | 0.3280 | 17.95 | 4.25 | 1.05e-05 | 0.85 |
| 6600 | 4.01 | 0.0013 | 0.3216 | 17.83 | 4.16 | 1.02e-05 | 0.29 |
| 6800 | 4.13 | 0.0014 | 0.3293 | 17.89 | 4.14 | 9.88e-06 | 0.28 |
| 7000 | 4.25 | 0.0011 | 0.3299 | 18.00 | 4.15 | 9.57e-06 | 0.18 |
| 7200 | 4.37 | 0.0014 | 0.3329 | 17.83 | 4.10 | 9.26e-06 | 0.31 |
| 7400 | 4.49 | 0.0019 | 0.3337 | 17.90 | 4.11 | 8.95e-06 | 2.44 |
| 7600 | 4.61 | 0.0013 | 0.3339 | 17.78 | 4.10 | 8.64e-06 | 0.44 |
| 7800 | 4.74 | 0.0014 | 0.3368 | 17.94 | 4.16 | 8.33e-06 | 0.31 |
| 8000 | 4.86 | 0.0017 | 0.3385 | 18.04 | 4.20 | 8.02e-06 | 0.25 |
| 8200 | 4.98 | 0.0014 | 0.3357 | 17.88 | 4.17 | 7.71e-06 | 0.37 |
| 8400 | 5.10 | 0.0009 | 0.3368 | 17.97 | 4.23 | 7.40e-06 | 0.24 |
| 8600 | 5.22 | 0.0009 | 0.3375 | 17.87 | 4.28 | 7.09e-06 | 0.14 |
| 8800 | 5.34 | 0.0012 | 0.3406 | 17.96 | 4.18 | 6.78e-06 | 0.16 |
| 9000 | 5.46 | 0.0010 | 0.3402 | 17.88 | 4.24 | 6.47e-06 | 0.30 |
| 9200 | 5.59 | 0.0009 | 0.3400 | 17.93 | 4.23 | 6.16e-06 | 0.21 |
| 9400 | 5.71 | 0.0013 | 0.3426 | 17.92 | 4.25 | 5.85e-06 | 0.23 |
| 9600 | 5.83 | 0.0012 | 0.3423 | 17.92 | 4.14 | 5.54e-06 | 0.23 |
| 9800 | 5.95 | 0.0010 | 0.3415 | 17.91 | 4.18 | 5.23e-06 | 0.18 |
| 10000 | 6.07 | 0.0010 | 0.3428 | 17.87 | 4.25 | 4.92e-06 | 0.22 |
| 10200 | 6.19 | 0.0012 | 0.3426 | 17.89 | 4.21 | 4.61e-06 | 0.23 |
| 10400 | 6.31 | 0.0010 | 0.3425 | 17.77 | 4.08 | 4.30e-06 | 0.20 |
| 10600 | 6.44 | 0.0010 | 0.3431 | 17.91 | 4.28 | 3.99e-06 | 0.13 |
| 10800 | 6.56 | 0.0011 | 0.3425 | 18.05 | 4.28 | 3.68e-06 | 0.22 |
| 11000 | 6.68 | 0.0011 | 0.3433 | 17.97 | 4.26 | 3.37e-06 | 0.32 |
| 11200 | 6.80 | 0.0012 | 0.3430 | 17.94 | 4.29 | 3.06e-06 | 0.27 |
| 11400 | 6.92 | 0.0023 | 0.3430 | 18.03 | 4.26 | 2.75e-06 | 0.19 |
| 11600 | 7.04 | 0.0010 | 0.3430 | 17.96 | 4.30 | 2.44e-06 | 0.26 |
| 11800 | 7.16 | 0.0010 | 0.3426 | 18.04 | 4.25 | 2.13e-06 | 0.22 |
| 12000 | 7.29 | 0.0010 | 0.3428 | 18.05 | 4.27 | 1.82e-06 | 0.17 |
| 12200 | 7.41 | 0.0010 | 0.3425 | 17.85 | 4.23 | 1.51e-06 | 0.43 |
| 12400 | 7.53 | 0.0009 | 0.3433 | 17.95 | 4.24 | 1.20e-06 | 0.21 |
| 12600 | 7.65 | 0.0009 | 0.3424 | 17.98 | 4.23 | 8.94e-07 | 0.39 |
| 12800 | 7.77 | 0.0010 | 0.3431 | 17.97 | 4.24 | 5.84e-07 | 0.24 |
| 13000 | 7.89 | 0.0009 | 0.3430 | 18.08 | 4.31 | 2.74e-07 | 0.60 |