---
language:
- vi
base_model: openai/whisper-largev2-ja-v2
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Large V2 Ja - Anh Phuong
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Google fleurs
      type: google/fleurs
      config: ja_jp
      split: None
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 58.247168882323976
---

<!-- 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. -->

# Whisper Large V2 Ja - Anh Phuong

This model is a fine-tuned version of [openai/whisper-largev2-ja-v2](https://huggingface.co/openai/whisper-largev2-ja-v2) on the Google fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2626
- Wer: 58.2472

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.004         | 6.25  | 1000 | 0.2030          | 61.0044 |
| 0.0022        | 12.5  | 2000 | 0.2081          | 60.6105 |
| 0.0002        | 18.75 | 3000 | 0.2401          | 58.7888 |
| 0.0001        | 25.0  | 4000 | 0.2531          | 58.6411 |
| 0.0001        | 31.25 | 5000 | 0.2598          | 58.2472 |
| 0.0001        | 37.5  | 6000 | 0.2626          | 58.2472 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1