---
license: apache-2.0
base_model: rinna/japanese-hubert-large
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: hubert-japanese-large-noise-0427
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: None
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.998
---

<!-- 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. -->

# hubert-japanese-large-noise-0427

This model is a fine-tuned version of [rinna/japanese-hubert-large](https://huggingface.co./rinna/japanese-hubert-large) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3383
- Cer: 0.0896
- Wer: 0.998

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 12500.0
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer    | Wer   |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----:|
| 9.4222        | 1.0   | 2500  | 8.3462          | 0.9998 | 1.0   |
| 3.8795        | 2.0   | 5000  | 3.7791          | 0.7299 | 1.0   |
| 3.6295        | 3.0   | 7500  | 3.5969          | 0.7280 | 1.0   |
| 1.451         | 4.0   | 10000 | 1.0974          | 0.1931 | 1.0   |
| 0.7754        | 5.0   | 12500 | 0.5525          | 0.1595 | 1.0   |
| 0.636         | 6.0   | 15000 | 0.4586          | 0.1605 | 1.0   |
| 0.5528        | 7.0   | 17500 | 0.4240          | 0.1377 | 1.0   |
| 0.5064        | 8.0   | 20000 | 0.3931          | 0.1412 | 1.0   |
| 0.4767        | 9.0   | 22500 | 0.3593          | 0.1403 | 1.0   |
| 0.449         | 10.0  | 25000 | 0.3519          | 0.1112 | 1.0   |
| 0.4261        | 11.0  | 27500 | 0.3578          | 0.1048 | 1.0   |
| 0.4131        | 12.0  | 30000 | 0.3459          | 0.1142 | 1.0   |
| 0.3807        | 13.0  | 32500 | 0.3355          | 0.1072 | 1.0   |
| 0.3759        | 14.0  | 35000 | 0.3380          | 0.0967 | 1.0   |
| 0.3532        | 15.0  | 37500 | 0.3310          | 0.1198 | 1.0   |
| 0.3469        | 16.0  | 40000 | 0.3383          | 0.0927 | 1.0   |
| 0.3297        | 17.0  | 42500 | 0.3363          | 0.0911 | 1.0   |
| 0.3347        | 18.0  | 45000 | 0.3333          | 0.0895 | 0.998 |
| 0.3225        | 19.0  | 47500 | 0.3393          | 0.0944 | 0.998 |
| 0.3199        | 20.0  | 50000 | 0.3341          | 0.0873 | 0.998 |
| 0.3141        | 21.0  | 52500 | 0.3363          | 0.0863 | 0.998 |
| 0.2927        | 22.0  | 55000 | 0.3384          | 0.0889 | 0.998 |
| 0.3051        | 23.0  | 57500 | 0.3389          | 0.0902 | 0.998 |
| 0.3072        | 24.0  | 60000 | 0.3387          | 0.0895 | 0.998 |
| 0.3109        | 25.0  | 62500 | 0.3383          | 0.0896 | 0.998 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.1