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---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: hubert-base-common-voice-vi-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: vi
split: None
args: vi
metrics:
- name: Wer
type: wer
value: 0.3678324522163481
---
<!-- 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. -->
# hubert-base-common-voice-vi-demo
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5121
- Wer: 0.3678
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 8.8731 | 1.14 | 500 | 3.5477 | 1.0 |
| 3.3329 | 2.28 | 1000 | 2.1928 | 1.0171 |
| 1.4603 | 3.42 | 1500 | 0.9074 | 0.6542 |
| 0.9413 | 4.57 | 2000 | 0.7490 | 0.5568 |
| 0.7664 | 5.71 | 2500 | 0.6418 | 0.5052 |
| 0.6719 | 6.85 | 3000 | 0.6240 | 0.4819 |
| 0.6261 | 7.99 | 3500 | 0.6048 | 0.4657 |
| 0.5771 | 9.13 | 4000 | 0.5555 | 0.4512 |
| 0.525 | 10.27 | 4500 | 0.5475 | 0.4392 |
| 0.4948 | 11.42 | 5000 | 0.5619 | 0.4261 |
| 0.4585 | 12.56 | 5500 | 0.5646 | 0.4280 |
| 0.4584 | 13.7 | 6000 | 0.5326 | 0.4168 |
| 0.4157 | 14.84 | 6500 | 0.5126 | 0.4038 |
| 0.4113 | 15.98 | 7000 | 0.5282 | 0.4004 |
| 0.3955 | 17.12 | 7500 | 0.5310 | 0.3959 |
| 0.3658 | 18.26 | 8000 | 0.4936 | 0.3886 |
| 0.3584 | 19.41 | 8500 | 0.5438 | 0.3895 |
| 0.3536 | 20.55 | 9000 | 0.5167 | 0.3860 |
| 0.3665 | 21.69 | 9500 | 0.5194 | 0.3842 |
| 0.3231 | 22.83 | 10000 | 0.5269 | 0.3866 |
| 0.315 | 23.97 | 10500 | 0.5219 | 0.3768 |
| 0.3191 | 25.11 | 11000 | 0.5054 | 0.3728 |
| 0.3264 | 26.26 | 11500 | 0.5068 | 0.3710 |
| 0.3014 | 27.4 | 12000 | 0.5009 | 0.3694 |
| 0.3055 | 28.54 | 12500 | 0.5066 | 0.3676 |
| 0.3098 | 29.68 | 13000 | 0.5121 | 0.3678 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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