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Hubert-noisy_common_voice_debug

Automatic Speech Recognition
Transformers
Safetensors
Japanese
hubert
original_noisy_common_voice
Generated from Trainer
Inference Endpoints
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Hubert-noisy_common_voice_debug / README.md
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metadata
library_name: transformers
language:
  - ja
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
  - automatic-speech-recognition
  - original_noisy_common_voice
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Hubert-noisy_common_voice_debug
    results: []

Hubert-noisy_common_voice_debug

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

  • Loss: 0.9787
  • Wer: 1.0
  • Cer: 0.3145

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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: 30.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 0.2660 100 12.0822 1.1570 1.0539
No log 0.5319 200 5.8789 1.0 0.9817
No log 0.7979 300 5.3627 1.0 0.9817
No log 1.0638 400 4.9316 1.0 0.9817
6.372 1.3298 500 4.4556 1.0 0.9817
6.372 1.5957 600 3.9890 1.0 0.9817
6.372 1.8617 700 3.5734 1.0 0.9817
6.372 2.1277 800 3.2932 1.0 0.9817
6.372 2.3936 900 3.1536 1.0 0.9817
3.4101 2.6596 1000 3.0484 1.0 0.9817
3.4101 2.9255 1100 2.8470 1.0 0.9808
3.4101 3.1915 1200 2.5211 1.0 0.8702
3.4101 3.4574 1300 2.0354 1.0 0.5518
3.4101 3.7234 1400 1.6780 1.0 0.4414
2.3222 3.9894 1500 1.5039 1.0 0.4312
2.3222 4.2553 1600 1.3419 1.0 0.3965
2.3222 4.5213 1700 1.2054 1.0 0.3686
2.3222 4.7872 1800 1.0588 1.0 0.3321
2.3222 5.0532 1900 0.9546 1.0 0.3158
1.2343 5.3191 2000 0.9042 1.0 0.3106
1.2343 5.5851 2100 0.8747 1.0 0.3088
1.2343 5.8511 2200 0.8224 1.0 0.2972
1.2343 6.1170 2300 0.8101 1.0 0.2996
1.2343 6.3830 2400 0.7892 1.0 0.2970
0.8716 6.6489 2500 0.7661 1.0 0.2915
0.8716 6.9149 2600 0.7654 1.0 0.2886
0.8716 7.1809 2700 0.7677 1.0 0.2898
0.8716 7.4468 2800 0.7528 1.0 0.2861
0.8716 7.7128 2900 0.7433 1.0 0.2880
0.7324 7.9787 3000 0.7498 1.0 0.2877
0.7324 8.2447 3100 0.7267 1.0 0.2827
0.7324 8.5106 3200 0.7319 1.0 0.2813
0.7324 8.7766 3300 0.7478 1.0 0.2882
0.7324 9.0426 3400 0.7337 1.0 0.2815
0.6486 9.3085 3500 0.7341 1.0 0.2851
0.6486 9.5745 3600 0.7419 1.0 0.2803
0.6486 9.8404 3700 0.7033 0.9998 0.2773
0.6486 10.1064 3800 0.7327 1.0 0.2829
0.6486 10.3723 3900 0.7554 0.9998 0.2855
0.6034 10.6383 4000 0.7361 1.0 0.2841
0.6034 10.9043 4100 0.7459 1.0 0.2833
0.6034 11.1702 4200 0.7384 1.0 0.2801
0.6034 11.4362 4300 0.7337 1.0 0.2776
0.6034 11.7021 4400 0.7572 1.0 0.2819
0.5687 11.9681 4500 0.7522 1.0 0.2824
0.5687 12.2340 4600 0.7491 1.0 0.2789
0.5687 12.5 4700 0.7485 1.0 0.2832
0.5687 12.7660 4800 0.7623 1.0 0.2849
0.5687 13.0319 4900 0.7829 1.0 0.2859
0.5255 13.2979 5000 0.7819 1.0 0.2820
0.5255 13.5638 5100 0.7783 0.9998 0.2824
0.5255 13.8298 5200 0.7653 1.0 0.2840
0.5255 14.0957 5300 0.7816 1.0 0.2822
0.5255 14.3617 5400 0.7608 1.0 0.2824
0.5016 14.6277 5500 0.7712 0.9998 0.2841
0.5016 14.8936 5600 0.7712 1.0 0.2864
0.5016 15.1596 5700 0.8153 0.9996 0.2851
0.5016 15.4255 5800 0.8161 0.9998 0.2852
0.5016 15.6915 5900 0.7911 1.0 0.2883
0.4821 15.9574 6000 0.7926 1.0 0.2823
0.4821 16.2234 6100 0.8147 1.0 0.2867
0.4821 16.4894 6200 0.7700 1.0 0.2826
0.4821 16.7553 6300 0.8119 1.0 0.2910
0.4821 17.0213 6400 0.8355 1.0 0.2846
0.4503 17.2872 6500 0.7936 0.9998 0.2859
0.4503 17.5532 6600 0.7976 0.9998 0.2952
0.4503 17.8191 6700 0.8274 0.9998 0.2902
0.4503 18.0851 6800 0.9034 0.9998 0.2885
0.4503 18.3511 6900 0.8066 0.9998 0.2882
0.4435 18.6170 7000 0.8495 1.0 0.2921
0.4435 18.8830 7100 0.8448 0.9998 0.2896
0.4435 19.1489 7200 0.8774 1.0 0.2904
0.4435 19.4149 7300 0.8293 0.9998 0.2973
0.4435 19.6809 7400 0.8038 1.0 0.2925
0.4457 19.9468 7500 0.8062 0.9998 0.2908
0.4457 20.2128 7600 0.8740 1.0 0.2918
0.4457 20.4787 7700 0.8489 1.0 0.2977
0.4457 20.7447 7800 0.8606 1.0 0.2973
0.4457 21.0106 7900 0.8141 0.9998 0.2926
0.4252 21.2766 8000 0.8832 0.9998 0.2984
0.4252 21.5426 8100 0.8590 0.9998 0.2945
0.4252 21.8085 8200 0.8304 0.9998 0.2940
0.4252 22.0745 8300 0.8734 0.9998 0.2974
0.4252 22.3404 8400 0.8417 0.9998 0.2930
0.418 22.6064 8500 0.9387 1.0 0.2993
0.418 22.8723 8600 0.8810 1.0 0.2996
0.418 23.1383 8700 0.9090 1.0 0.3074
0.418 23.4043 8800 0.8993 0.9998 0.3107
0.418 23.6702 8900 0.8724 1.0 0.3033
0.424 23.9362 9000 0.8895 0.9998 0.3042
0.424 24.2021 9100 0.8863 1.0 0.3014
0.424 24.4681 9200 0.9255 0.9998 0.3112
0.424 24.7340 9300 0.9398 0.9998 0.3011
0.424 25.0 9400 0.8763 0.9998 0.3071
0.4122 25.2660 9500 0.9353 1.0 0.3090
0.4122 25.5319 9600 0.9382 1.0 0.3128
0.4122 25.7979 9700 0.9295 0.9998 0.3102
0.4122 26.0638 9800 0.9286 0.9998 0.3092
0.4122 26.3298 9900 0.9141 1.0 0.3014
0.4146 26.5957 10000 0.9426 1.0 0.3126
0.4146 26.8617 10100 0.8652 1.0 0.3032
0.4146 27.1277 10200 0.9289 1.0 0.3105
0.4146 27.3936 10300 0.9459 1.0 0.3103
0.4146 27.6596 10400 0.9137 0.9998 0.3124
0.416 27.9255 10500 0.9305 1.0 0.3100
0.416 28.1915 10600 0.9589 1.0 0.3071
0.416 28.4574 10700 0.9276 0.9998 0.3061
0.416 28.7234 10800 0.9259 1.0 0.3078
0.416 28.9894 10900 0.9287 1.0 0.3150
0.4078 29.2553 11000 0.9346 0.9998 0.3166
0.4078 29.5213 11100 0.9559 1.0 0.3165
0.4078 29.7872 11200 0.9670 1.0 0.3173

Framework versions

  • Transformers 4.47.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3