---
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-E5-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: 18.6414
    - name: Cer
      type: cer
      value: 4.2134
    - name: Real Time Factor
      type: rtf
      value: 0.0718
---
![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-E5-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: 18.6414
- Cer: 4.2134
- Real Time Factor: 0.0718

## 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-E5-lus-v2026.06")
model = Qwen2AudioForConditionalGeneration.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E5-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 | 0.09 | 1.9158 | 1.5851 | 76.79 | 25.27 | 1.13e-05 | 90.00 |
| 400 | 0.18 | 0.2754 | 0.5047 | 37.90 | 9.54 | 1.99e-05 | 8.38 |
| 600 | 0.27 | 0.2056 | 0.3755 | 30.17 | 7.52 | 1.97e-05 | 7.00 |
| 800 | 0.36 | 0.1508 | 0.3205 | 25.75 | 6.06 | 1.95e-05 | 5.41 |
| 1000 | 0.46 | 0.1193 | 0.2937 | 24.42 | 5.88 | 1.92e-05 | 7.34 |
| 1200 | 0.55 | 0.1207 | 0.2833 | 23.31 | 5.47 | 1.90e-05 | 5.91 |
| 1400 | 0.64 | 0.1156 | 0.2642 | 22.26 | 5.12 | 1.88e-05 | 6.03 |
| 1600 | 0.73 | 0.0874 | 0.2507 | 20.53 | 4.72 | 1.86e-05 | 3.44 |
| 1800 | 0.82 | 0.0909 | 0.2488 | 19.69 | 4.73 | 1.83e-05 | 5.31 |
| 2000 | 0.91 | 0.0897 | 0.2517 | 20.01 | 4.69 | 1.81e-05 | 5.47 |
| 2200 | 1.00 | 0.0499 | 0.2411 | 18.98 | 4.30 | 1.79e-05 | 4.53 |
| 2400 | 1.09 | 0.0346 | 0.2440 | 18.28 | 4.21 | 1.76e-05 | 5.09 |
| 2600 | 1.18 | 0.0506 | 0.2463 | 19.26 | 4.44 | 1.74e-05 | 5.19 |
| 2800 | 1.28 | 0.0384 | 0.2533 | 19.08 | 4.43 | 1.72e-05 | 4.81 |
| 3000 | 1.37 | 0.0339 | 0.2554 | 18.82 | 4.26 | 1.69e-05 | 3.86 |
| 3200 | 1.46 | 0.0280 | 0.2577 | 18.61 | 4.33 | 1.67e-05 | 4.31 |
| 3400 | 1.55 | 0.0316 | 0.2543 | 18.33 | 4.22 | 1.65e-05 | 6.47 |
| 3600 | 1.64 | 0.0226 | 0.2546 | 18.08 | 4.13 | 1.62e-05 | 1.62 |
| 3800 | 1.73 | 0.0278 | 0.2602 | 18.05 | 4.18 | 1.60e-05 | 3.23 |
| 4000 | 1.82 | 0.0200 | 0.2637 | 18.03 | 4.14 | 1.58e-05 | 3.69 |
| 4200 | 1.91 | 0.0190 | 0.2629 | 17.76 | 4.00 | 1.55e-05 | 5.22 |
| 4400 | 2.00 | 0.0121 | 0.2688 | 18.23 | 4.11 | 1.53e-05 | 3.03 |
| 4600 | 2.09 | 0.0099 | 0.2832 | 18.19 | 4.18 | 1.51e-05 | 3.30 |
| 4800 | 2.19 | 0.0117 | 0.2814 | 18.17 | 4.10 | 1.48e-05 | 6.50 |
| 5000 | 2.28 | 0.0093 | 0.2842 | 17.73 | 4.23 | 1.46e-05 | 3.62 |
| 5200 | 2.37 | 0.0089 | 0.2876 | 18.03 | 4.21 | 1.44e-05 | 1.44 |
| 5400 | 2.46 | 0.0107 | 0.2814 | 17.81 | 4.16 | 1.41e-05 | 1.63 |
| 5600 | 2.55 | 0.0079 | 0.2831 | 17.24 | 3.92 | 1.39e-05 | 1.46 |
| 5800 | 2.64 | 0.0064 | 0.2927 | 17.58 | 3.95 | 1.37e-05 | 4.53 |
| 6000 | 2.73 | 0.0111 | 0.2926 | 17.63 | 3.98 | 1.34e-05 | 7.41 |
| 6200 | 2.82 | 0.0050 | 0.2963 | 17.72 | 4.12 | 1.32e-05 | 1.07 |
| 6400 | 2.91 | 0.0050 | 0.2935 | 17.27 | 3.96 | 1.30e-05 | 0.96 |
| 6600 | 3.01 | 0.0025 | 0.2995 | 17.06 | 3.90 | 1.27e-05 | 0.81 |
| 6800 | 3.10 | 0.0031 | 0.3090 | 17.32 | 3.94 | 1.25e-05 | 0.84 |
| 7000 | 3.19 | 0.0036 | 0.3094 | 17.17 | 3.92 | 1.23e-05 | 0.48 |
| 7200 | 3.28 | 0.0055 | 0.3113 | 17.28 | 3.99 | 1.20e-05 | 1.09 |
| 7400 | 3.37 | 0.0035 | 0.3116 | 17.32 | 3.95 | 1.18e-05 | 0.30 |
| 7600 | 3.46 | 0.0049 | 0.3128 | 16.95 | 3.90 | 1.16e-05 | 1.99 |
| 7800 | 3.55 | 0.0036 | 0.3111 | 17.19 | 4.00 | 1.13e-05 | 2.77 |
| 8000 | 3.64 | 0.0021 | 0.3138 | 17.22 | 3.90 | 1.11e-05 | 0.37 |
| 8200 | 3.73 | 0.0039 | 0.3096 | 17.13 | 3.84 | 1.09e-05 | 0.62 |
| 8400 | 3.83 | 0.0031 | 0.3160 | 17.15 | 3.90 | 1.07e-05 | 1.31 |
| 8600 | 3.92 | 0.0017 | 0.3132 | 17.40 | 3.91 | 1.04e-05 | 0.56 |
| 8800 | 4.01 | 0.0016 | 0.3194 | 17.06 | 3.91 | 1.02e-05 | 0.25 |
| 9000 | 4.10 | 0.0024 | 0.3214 | 17.25 | 3.90 | 9.95e-06 | 0.17 |
| 9200 | 4.19 | 0.0017 | 0.3217 | 17.32 | 3.96 | 9.72e-06 | 2.45 |
| 9400 | 4.28 | 0.0012 | 0.3262 | 17.29 | 3.95 | 9.49e-06 | 0.27 |
| 9600 | 4.37 | 0.0018 | 0.3231 | 17.26 | 3.92 | 9.26e-06 | 0.26 |
| 9800 | 4.46 | 0.0013 | 0.3260 | 17.61 | 4.01 | 9.03e-06 | 0.51 |
| 10000 | 4.55 | 0.0012 | 0.3224 | 17.25 | 3.91 | 8.79e-06 | 0.17 |
| 10200 | 4.64 | 0.0016 | 0.3262 | 17.41 | 3.98 | 8.56e-06 | 0.66 |
| 10400 | 4.74 | 0.0012 | 0.3246 | 17.46 | 3.97 | 8.33e-06 | 0.23 |
| 10600 | 4.83 | 0.0011 | 0.3277 | 17.25 | 3.92 | 8.10e-06 | 0.16 |
| 10800 | 4.92 | 0.0020 | 0.3261 | 17.47 | 3.97 | 7.86e-06 | 2.86 |
| 11000 | 5.01 | 0.0011 | 0.3268 | 17.26 | 3.94 | 7.63e-06 | 0.59 |
| 11200 | 5.10 | 0.0017 | 0.3292 | 17.27 | 3.93 | 7.40e-06 | 0.46 |
| 11400 | 5.19 | 0.0011 | 0.3290 | 17.31 | 3.97 | 7.17e-06 | 0.14 |
| 11600 | 5.28 | 0.0011 | 0.3295 | 17.16 | 3.94 | 6.93e-06 | 0.27 |
| 11800 | 5.37 | 0.0011 | 0.3300 | 17.25 | 3.91 | 6.70e-06 | 0.44 |
| 12000 | 5.46 | 0.0011 | 0.3300 | 17.18 | 3.95 | 6.47e-06 | 0.36 |
| 12200 | 5.56 | 0.0012 | 0.3291 | 17.29 | 3.96 | 6.24e-06 | 0.29 |
| 12400 | 5.65 | 0.0011 | 0.3295 | 17.25 | 3.92 | 6.00e-06 | 0.28 |
| 12600 | 5.74 | 0.0012 | 0.3286 | 17.27 | 3.88 | 5.77e-06 | 0.45 |
| 12800 | 5.83 | 0.0013 | 0.3303 | 17.20 | 3.92 | 5.54e-06 | 0.24 |
| 13000 | 5.92 | 0.0018 | 0.3291 | 17.24 | 3.95 | 5.31e-06 | 0.55 |
| 13200 | 6.01 | 0.0011 | 0.3308 | 17.22 | 3.92 | 5.08e-06 | 0.18 |
| 13400 | 6.10 | 0.0012 | 0.3307 | 17.29 | 3.92 | 4.84e-06 | 0.30 |
| 13600 | 6.19 | 0.0010 | 0.3317 | 17.25 | 3.97 | 4.61e-06 | 0.52 |
| 13800 | 6.28 | 0.0012 | 0.3317 | 17.21 | 3.96 | 4.38e-06 | 0.14 |
| 14000 | 6.38 | 0.0012 | 0.3307 | 17.21 | 3.92 | 4.15e-06 | 0.44 |
| 14200 | 6.47 | 0.0012 | 0.3311 | 17.26 | 3.93 | 3.91e-06 | 0.34 |
| 14400 | 6.56 | 0.0011 | 0.3316 | 17.24 | 3.99 | 3.68e-06 | 0.19 |
| 14600 | 6.65 | 0.0010 | 0.3308 | 17.30 | 3.94 | 3.45e-06 | 0.25 |
| 14800 | 6.74 | 0.0013 | 0.3311 | 17.22 | 3.92 | 3.22e-06 | 0.27 |
| 15000 | 6.83 | 0.0010 | 0.3320 | 17.15 | 3.95 | 2.98e-06 | 0.17 |
| 15200 | 6.92 | 0.0010 | 0.3307 | 17.28 | 3.93 | 2.75e-06 | 0.18 |
| 15400 | 7.01 | 0.0011 | 0.3308 | 17.32 | 3.91 | 2.52e-06 | 0.22 |
| 15600 | 7.10 | 0.0011 | 0.3305 | 17.26 | 3.92 | 2.29e-06 | 0.21 |
| 15800 | 7.19 | 0.0011 | 0.3317 | 17.29 | 3.92 | 2.06e-06 | 0.33 |
| 16000 | 7.29 | 0.0011 | 0.3316 | 17.22 | 3.93 | 1.82e-06 | 0.43 |
| 16200 | 7.38 | 0.0010 | 0.3312 | 17.32 | 3.93 | 1.59e-06 | 0.21 |
| 16400 | 7.47 | 0.0011 | 0.3314 | 17.30 | 3.94 | 1.36e-06 | 0.61 |
| 16600 | 7.56 | 0.0010 | 0.3313 | 17.38 | 3.93 | 1.13e-06 | 0.18 |
| 16800 | 7.65 | 0.0010 | 0.3313 | 17.29 | 3.92 | 8.93e-07 | 0.21 |
| 17000 | 7.74 | 0.0010 | 0.3310 | 17.19 | 3.90 | 6.61e-07 | 0.23 |
| 17200 | 7.83 | 0.0009 | 0.3306 | 17.41 | 3.98 | 4.29e-07 | 0.15 |
| 17400 | 7.92 | 0.0011 | 0.3309 | 17.26 | 3.91 | 1.96e-07 | 0.18 |