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Hubert-kakeiken-W-elevator_hall

Automatic Speech Recognition
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hubert
original_kakeiken_W_elevator_hall
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metadata
library_name: transformers
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
  - automatic-speech-recognition
  - original_kakeiken_W_elevator_hall
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Hubert-kakeiken-W-elevator_hall
    results: []

Hubert-kakeiken-W-elevator_hall

This model is a fine-tuned version of rinna/japanese-hubert-base on the ORIGINAL_KAKEIKEN_W_ELEVATOR_HALL - JA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0253
  • Wer: 0.9988
  • Cer: 1.0162

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
53.9873 1.0 820 19.7137 1.0 1.1284
16.2788 2.0 1640 12.8321 1.0 1.1284
11.6832 3.0 2460 5.6187 1.0 1.1284
4.3059 4.0 3280 3.3938 1.0 1.1284
3.1294 5.0 4100 2.8854 1.0 1.1284
2.6448 6.0 4920 1.4073 1.0 1.1019
1.022 7.0 5740 0.6323 1.0 1.0336
0.4449 8.0 6560 0.3110 0.9988 1.0403
0.305 9.0 7380 0.2348 0.9991 1.0485
0.1895 10.0 8200 0.1109 0.9988 1.0230
0.1437 11.0 9020 0.0931 0.9990 1.0221
0.1258 12.0 9840 0.0828 0.9988 1.0258
0.1175 13.0 10660 0.0814 0.9991 1.0232
0.1083 14.0 11480 0.0415 0.9988 1.0194
0.0974 15.0 12300 0.0653 0.9990 1.0239
0.0967 16.0 13120 0.0495 0.9991 1.0200
0.087 17.0 13940 0.0601 0.9990 1.0224
0.0798 18.0 14760 0.0544 0.9990 1.0218
0.0719 19.0 15580 0.0426 0.9990 1.0191
0.0731 20.0 16400 0.0587 0.9991 1.0208
0.0693 21.0 17220 0.0603 0.9988 1.0222
0.0614 22.0 18040 0.0361 0.9988 1.0191
0.0582 23.0 18860 0.0332 0.9988 1.0173
0.0535 24.0 19680 0.0347 0.9988 1.0172
0.0467 25.0 20500 0.0334 0.9988 1.0180
0.0456 26.0 21320 0.0283 0.9988 1.0164
0.0389 27.0 22140 0.0361 0.9988 1.0172
0.04 28.0 22960 0.0258 0.9988 1.0167
0.0348 29.0 23780 0.0328 0.9990 1.0176
0.0343 30.0 24600 0.0276 0.9988 1.0162
0.0323 31.0 25420 0.0297 0.9988 1.0165
0.0283 32.0 26240 0.0291 0.9988 1.0165
0.0275 33.0 27060 0.0252 0.9988 1.0161
0.0256 34.0 27880 0.0245 0.9988 1.0164
0.0241 35.0 28700 0.0240 0.9988 1.0159
0.0237 36.0 29520 0.0278 0.9988 1.0166
0.0238 37.0 30340 0.0275 0.9988 1.0163
0.022 38.0 31160 0.0247 0.9988 1.0163
0.0184 39.0 31980 0.0262 0.9988 1.0163
0.0199 39.9518 32760 0.0244 0.9988 1.0160

Framework versions

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