qwen3-asr-1.7b-mizonal3-E2-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: 22.6171
• Cer: 4.9795
• Real Time Factor: 0.0616

Quick Inference

import torch
from qwen_asr import Qwen3ASRModel

# Load the model
model = Qwen3ASRModel.from_pretrained(
    "andrewbawitlung/qwen3-asr-1.7b-mizonal3-E2-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	1.4517	0.4690	0.2704	23.2461	5.2357	1.67e-05	6.9688
400	2.9035	0.2158	0.2535	20.4048	4.5148	1.30e-05	6.1562
600	4.3497	0.0796	0.2904	20.5799	4.8010	9.34e-06	4.6875
800	5.8015	0.0518	0.3153	21.3876	5.1986	5.64e-06	3.4844
1000	7.2477	0.0325	0.3302	21.2708	4.9724	1.94e-06	2.4062