---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: vi_whisper-small
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Vivos + Commonvoice
      type: vivos
      config: None
      split: None
    metrics:
    - name: Wer
      type: wer
      value: 21.8855
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vi_whisper-small

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mixing of Vivos and CommonVoice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2894 
- Wer: 21.8855

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

In training phase i used VIVOS dataset and cleaned CommonVoice 
The VIVOS evaluation dataset was used

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- 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
- training_steps: 8000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.249         | 1.1   | 1000 | 0.3766          | 32.1678 |
| 0.1416        | 2.2   | 2000 | 0.2881          | 46.4646 |
| 0.0839        | 3.3   | 3000 | 0.2799          | 22.7791 |
| 0.0546        | 4.41  | 4000 | 0.2894          | 21.8855 |
| 0.0256        | 5.51  | 5000 | 0.3023          | 32.2973 |
| 0.0111        | 6.61  | 6000 | 0.3061          | 31.0153 |
| 0.0028        | 7.71  | 7000 | 0.3143          | 27.1691 |
| 0.0014        | 8.81  | 8000 | 0.3187          | 27.3634 |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3