README.md

metadata

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

hubert-japanese-base-noise-0426

This model is a fine-tuned version of 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