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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
base_model: rinna/japanese-hubert-base
model-index:
- name: hubert-japanese-base-noise-0426
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: None
args: default
metrics:
- type: wer
value: 0.992
name: Wer
---
<!-- 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-base-noise-0426
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co./rinna/japanese-hubert-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2302
- Cer: 0.0598
- Wer: 0.992
## Model description
This model is a hiragana recognition model created by the proposed method.
The model is based on rinna's hubert base model.
## Intended uses & limitations
More information needed
## Training and evaluation data
Train : noisepaused_JNAS_train_0408\
Test : noisepaused_JNAS_test_0408
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----:|
| 11.9556 | 1.0 | 2500 | 9.5354 | 0.9998 | 1.0 |
| 3.8038 | 2.0 | 5000 | 3.6912 | 0.9998 | 1.0 |
| 1.668 | 3.0 | 7500 | 1.1310 | 0.2733 | 1.0 |
| 0.688 | 4.0 | 10000 | 0.4272 | 0.1880 | 1.0 |
| 0.4959 | 5.0 | 12500 | 0.3254 | 0.1356 | 0.998 |
| 0.4275 | 6.0 | 15000 | 0.2856 | 0.1026 | 1.0 |
| 0.3647 | 7.0 | 17500 | 0.2720 | 0.0884 | 0.998 |
| 0.346 | 8.0 | 20000 | 0.2625 | 0.0848 | 0.998 |
| 0.3273 | 9.0 | 22500 | 0.2646 | 0.0896 | 0.996 |
| 0.301 | 10.0 | 25000 | 0.2479 | 0.0734 | 0.996 |
| 0.2871 | 11.0 | 27500 | 0.2466 | 0.0778 | 0.998 |
| 0.268 | 12.0 | 30000 | 0.2403 | 0.0717 | 0.992 |
| 0.2494 | 13.0 | 32500 | 0.2467 | 0.0705 | 0.994 |
| 0.2336 | 14.0 | 35000 | 0.2411 | 0.0702 | 0.994 |
| 0.2347 | 15.0 | 37500 | 0.2352 | 0.0662 | 0.994 |
| 0.2261 | 16.0 | 40000 | 0.2400 | 0.0708 | 0.996 |
| 0.207 | 17.0 | 42500 | 0.2341 | 0.0652 | 0.996 |
| 0.2018 | 18.0 | 45000 | 0.2340 | 0.0635 | 0.994 |
| 0.196 | 19.0 | 47500 | 0.2323 | 0.0578 | 0.992 |
| 0.1856 | 20.0 | 50000 | 0.2343 | 0.0625 | 0.992 |
| 0.1788 | 21.0 | 52500 | 0.2303 | 0.0597 | 0.992 |
| 0.1821 | 22.0 | 55000 | 0.2285 | 0.0596 | 0.99 |
| 0.1824 | 23.0 | 57500 | 0.2305 | 0.0591 | 0.99 |
| 0.1693 | 24.0 | 60000 | 0.2297 | 0.0598 | 0.99 |
| 0.1807 | 25.0 | 62500 | 0.2302 | 0.0598 | 0.992 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.1
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