qwen3-asr-1.7b-mizonal3-E3-lus-v2026.06

This model is a fine-tuned version of Qwen/Qwen3-ASR-1.7B on the MiZonal v3.0 dataset.

It achieves the following results on the evaluation set:

• Wer: 21.3802
• Cer: 4.4457
• Real Time Factor: 0.0589

Quick Inference

import torch
from qwen_asr import Qwen3ASRModel

# Load the model
model = Qwen3ASRModel.from_pretrained(
    "andrewbawitlung/qwen3-asr-1.7b-mizonal3-E3-lus-v2026.06", 
    dtype=torch.bfloat16, 
    device_map="cuda:0"  # Adjust device as needed
)

# Transcribe audio
results = model.transcribe("your_audio.wav")
print(results)

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-1.7b-mizonal3-E1-lus-v2026.06
E2 (Noise)	andrewbawitlung/qwen3-asr-1.7b-mizonal3-E2-lus-v2026.06
E3 (Speed)	andrewbawitlung/qwen3-asr-1.7b-mizonal3-E3-lus-v2026.06
E4 (SpecAug)	andrewbawitlung/qwen3-asr-1.7b-mizonal3-E4-lus-v2026.06
E5 (Combined)	andrewbawitlung/qwen3-asr-1.7b-mizonal3-E5-lus-v2026.06

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• 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	0.9709	0.4075	0.2658	22.4385	4.9848	1.79e-05	6.1250
400	1.9417	0.1853	0.2452	19.5096	4.4547	1.55e-05	5.7188
600	2.9126	0.0737	0.2774	19.1982	4.4847	1.30e-05	3.8750
800	3.8835	0.0359	0.3090	19.7626	4.5784	1.05e-05	3.2031
1000	4.8544	0.0132	0.3367	19.1398	4.4158	8.04e-06	2.7344
1200	5.8252	0.0077	0.3561	19.3344	4.5642	5.56e-06	0.9766
1400	6.7961	0.0053	0.3702	19.2469	4.5271	3.08e-06	0.6406
1600	7.7670	0.0052	0.3726	19.2955	4.5713	6.07e-07	0.4590