---
language:
- ja
license: other
tags:
- whisper-event
- generated_from_trainer
datasets:
- Elite35P-Server/EliteVoiceProject
metrics:
- wer
model-index:
- name: Whisper Base Japanese Elite
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Elite35P-Server/EliteVoiceProject twitter
      type: Elite35P-Server/EliteVoiceProject
      config: twitter
      split: test
      args: twitter
    metrics:
    - name: Wer
      type: wer
      value: 17.073170731707318
---

<!-- 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 Base Japanese Elite

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Elite35P-Server/EliteVoiceProject twitter dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4385
- Wer: 17.0732

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.0002        | 111.0  | 1000  | 0.2155          | 9.7561  |
| 0.0001        | 222.0  | 2000  | 0.2448          | 12.1951 |
| 0.0           | 333.0  | 3000  | 0.2674          | 13.4146 |
| 0.0           | 444.0  | 4000  | 0.2943          | 15.8537 |
| 0.0           | 555.0  | 5000  | 0.3182          | 17.0732 |
| 0.0           | 666.0  | 6000  | 0.3501          | 18.9024 |
| 0.0           | 777.0  | 7000  | 0.3732          | 16.4634 |
| 0.0           | 888.0  | 8000  | 0.4025          | 17.0732 |
| 0.0           | 999.0  | 9000  | 0.4178          | 20.1220 |
| 0.0           | 1111.0 | 10000 | 0.4385          | 17.0732 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2