Qwen3 Asr 1.7b Mizonal3 E2 Lus v2026.06
> Disclaimer / Notice: Details for these are in Peer Review and publications of the paper will be made available soon for more details.
Fonte do modelo
- Tipo
- Text model
- Tamanho do arquivo
- 3,80 GB
- Modelo base
- Qwen/Qwen3-ASR-1.7B
- Versão
- vmodel
- Atualizado
- 19 de out. de 2026
- Downloads
- 15
- Fonte
- Hugging Face
Descrição da fonte
Mizo Automatic Speech Recognition (ASR) Models v3.0
Disclaimer / Notice: Details for these are in Peer Review and publications of the paper will be made available soon for more details.
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 |
Fontes
1 fonte
Fontes
1 fonteVerificado 19 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- language: - lus license: apache-2.0 pipeline_tag: automatic-speech-recognition base_model: Qwen/Qwen3-ASR-1.7B tags: - generated_from_trainer datasets: - andrewbawitlung/MiZonal-v3.0 metrics: - wer - cer model-index: - name: qwen3-asr-1.7b-mizonal3-E2-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: 22.6171 - name: Cer type: cer value: 4.9795 - name: Real Time Factor type: rtf value: 0.0616 ---  > **Disclaimer / Notice:** Details for these are in Peer Review and publications of the paper will be made available soon for more details. # qwen3-asr-1.7b-mizonal3-E2-lus-v2026.06 This model is a fine-tuned version of [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/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 ```python 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](https://huggingface.co/andrewbawitlung/qwen3-asr-1.7b-mizonal3-E1-lus-v2026.06) | | **E2 (Noise)** | [andrewbawitlung/qwen3-asr-1.7b-mizonal3-E2-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-1.7b-mizonal3-E2-lus-v2026.06) | | **E3 (Speed)** | [andrewbawitlung/qwen3-asr-1.7b-mizonal3-E3-l...
Source context: 15 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/qwen3-asr-1.7b-mizonal3-E2-lus-v2026.06
Source context: 43 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/qwen3-asr-1.7b-mizonal3-E2-lus-v2026.06
