---
language:
  - vi
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 vi
      type: mozilla-foundation/common_voice_11_0
      config: vi
      split: test
      args: vi
    metrics:
    - name: Wer
      type: wer
      value: 15.7710
    - name: Cer
      type: cer
      value: 7.6691
---

<!-- 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. -->

# openai/whisper-large-v2

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4041
- Wer: 15.7710
- Cer: 7.6691

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Training data: 
* [mozilla-foundation/common_voice_11_0](https://huggingface.co/openai/whisper-large-v2)
* [google/fleurs](https://huggingface.co/datasets/google/fleurs)

Evaluation data:
* [mozilla-foundation/common_voice_11_0](https://huggingface.co/openai/whisper-large-v2)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.3983        | 0.1   | 500  | 0.5338          | 19.5876 | 10.6391 |
| 0.2277        | 1.08  | 1000 | 0.4134          | 16.5826 | 8.2668  |
| 0.172         | 2.05  | 1500 | 0.3968          | 16.3084 | 7.9787  |
| 0.1823        | 3.03  | 2000 | 0.3956          | 16.1768 | 7.8159  |
| 0.1445        | 4.0   | 2500 | 0.3955          | 16.0342 | 7.7438  |
| 0.147         | 4.1   | 3000 | 0.3965          | 15.8807 | 7.7145  |
| 0.1292        | 5.08  | 3500 | 0.4000          | 15.8587 | 7.7065  |
| 0.1187        | 6.05  | 4000 | 0.4029          | 15.7491 | 7.6398  |
| 0.1368        | 7.03  | 4500 | 0.4041          | 15.7600 | 7.6558  |
| 0.1231        | 8.0   | 5000 | 0.4041          | 15.7710 | 7.6691  |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2