transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer vi dataset:mozilla-foundation/common_voice_16_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- vi
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Vietnamese
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 vi
      type: mozilla-foundation/common_voice_16_0
      config: vi
      split: test
      args: vi
    metrics:
    - name: Wer
      type: wer
      value: 37.80239886155723
---

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

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 vi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7770
- Wer: 37.8024

## 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: 5e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.6043        | 33.0  | 500   | 0.9039          | 42.6408 |
| 0.2836        | 66.0  | 1000  | 0.7761          | 38.3106 |
| 0.1593        | 99.0  | 1500  | 0.7770          | 37.8024 |
| 0.0835        | 133.0 | 2000  | 0.8019          | 37.8634 |
| 0.0395        | 166.0 | 2500  | 0.8317          | 38.1582 |
| 0.0217        | 199.0 | 3000  | 0.8563          | 38.2395 |
| 0.0146        | 233.0 | 3500  | 0.8744          | 38.2801 |
| 0.0107        | 266.0 | 4000  | 0.8893          | 38.4733 |
| 0.0082        | 299.0 | 4500  | 0.9031          | 38.3310 |
| 0.0065        | 333.0 | 5000  | 0.9155          | 38.4326 |
| 0.0053        | 366.0 | 5500  | 0.9267          | 38.6156 |
| 0.0044        | 399.0 | 6000  | 0.9381          | 38.7579 |
| 0.0037        | 433.0 | 6500  | 0.9486          | 38.7782 |
| 0.0032        | 466.0 | 7000  | 0.9580          | 39.0120 |
| 0.0028        | 499.0 | 7500  | 0.9669          | 39.1441 |
| 0.0025        | 533.0 | 8000  | 0.9747          | 39.1746 |
| 0.0022        | 566.0 | 8500  | 0.9810          | 39.2864 |
| 0.0021        | 599.0 | 9000  | 0.9866          | 39.2763 |
| 0.002         | 633.0 | 9500  | 0.9899          | 39.3271 |
| 0.0019        | 666.0 | 10000 | 0.9911          | 39.3271 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
