---
library_name: transformers
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- automatic-speech-recognition
- original_kakeiken_W_meeting_room
- generated_from_trainer
metrics:
- wer
model-index:
- name: Hubert-kakeiken-W-meeting_room
  results: []
---

<!-- 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-kakeiken-W-meeting_room

This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the ORIGINAL_KAKEIKEN_W_MEETING_ROOM - JA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0146
- Wer: 0.9990
- Cer: 1.0147

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 12500
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 29.232        | 1.0     | 820   | 11.3507         | 1.0    | 1.1284 |
| 9.4623        | 2.0     | 1640  | 7.8286          | 1.0    | 1.1284 |
| 7.1817        | 3.0     | 2460  | 4.2914          | 1.0    | 1.1284 |
| 3.6746        | 4.0     | 3280  | 2.9959          | 1.0    | 1.1284 |
| 2.6344        | 5.0     | 4100  | 2.3796          | 1.0    | 1.1284 |
| 2.224         | 6.0     | 4920  | 1.1763          | 1.0    | 1.0736 |
| 0.8482        | 7.0     | 5740  | 0.4680          | 0.9999 | 0.9927 |
| 0.4357        | 8.0     | 6560  | 0.2710          | 0.9993 | 0.9734 |
| 0.3568        | 9.0     | 7380  | 0.1256          | 0.9996 | 1.0188 |
| 0.2374        | 10.0    | 8200  | 0.1476          | 0.9990 | 1.0219 |
| 0.1941        | 11.0    | 9020  | 0.0597          | 0.9990 | 1.0187 |
| 0.1829        | 12.0    | 9840  | 0.0470          | 0.9990 | 1.0188 |
| 0.1701        | 13.0    | 10660 | 0.0510          | 0.9990 | 1.0181 |
| 0.1603        | 14.0    | 11480 | 0.0375          | 0.9988 | 1.0193 |
| 0.1564        | 15.0    | 12300 | 0.0865          | 0.9991 | 1.0064 |
| 0.1555        | 16.0    | 13120 | 0.0240          | 0.9990 | 1.0172 |
| 0.1446        | 17.0    | 13940 | 0.0388          | 0.9990 | 1.0184 |
| 0.1374        | 18.0    | 14760 | 0.0724          | 0.9991 | 1.0229 |
| 0.1358        | 19.0    | 15580 | 0.0293          | 0.9988 | 1.0168 |
| 0.126         | 20.0    | 16400 | 0.0250          | 0.9990 | 1.0173 |
| 0.1238        | 21.0    | 17220 | 0.0337          | 0.9991 | 1.0174 |
| 0.1106        | 22.0    | 18040 | 0.0181          | 0.9988 | 1.0159 |
| 0.1124        | 23.0    | 18860 | 0.0199          | 0.9990 | 1.0165 |
| 0.104         | 24.0    | 19680 | 0.0151          | 0.9990 | 1.0151 |
| 0.1039        | 25.0    | 20500 | 0.0136          | 0.9990 | 1.0149 |
| 0.094         | 26.0    | 21320 | 0.0132          | 0.9988 | 1.0148 |
| 0.0921        | 27.0    | 22140 | 0.0171          | 0.9990 | 1.0158 |
| 0.0832        | 28.0    | 22960 | 0.0122          | 0.9990 | 1.0146 |
| 0.08          | 29.0    | 23780 | 0.0147          | 0.9988 | 1.0151 |
| 0.0797        | 30.0    | 24600 | 0.0122          | 0.9990 | 1.0147 |
| 0.0775        | 31.0    | 25420 | 0.0131          | 0.9990 | 1.0151 |
| 0.0675        | 32.0    | 26240 | 0.0131          | 0.9991 | 1.0148 |
| 0.0676        | 33.0    | 27060 | 0.0171          | 0.9988 | 1.0146 |
| 0.069         | 34.0    | 27880 | 0.0147          | 0.9991 | 1.0146 |
| 0.0588        | 35.0    | 28700 | 0.0140          | 0.9990 | 1.0146 |
| 0.0608        | 36.0    | 29520 | 0.0152          | 0.9988 | 1.0146 |
| 0.0598        | 37.0    | 30340 | 0.0158          | 0.9990 | 1.0146 |
| 0.0578        | 38.0    | 31160 | 0.0150          | 0.9990 | 1.0147 |
| 0.0563        | 39.0    | 31980 | 0.0154          | 0.9990 | 1.0147 |
| 0.0591        | 39.9518 | 32760 | 0.0146          | 0.9990 | 1.0147 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0