---
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