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Hubert-common_voice-ja-demo-roma-debug-40epochs-cosine

Automatic Speech Recognition
Transformers
Safetensors
Japanese
hubert
mozilla-foundation/common_voice_13_0
Generated from Trainer
Eval Results
Inference Endpoints
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Hubert-common_voice-ja-demo-roma-debug-40epochs-cosine / 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
  - mozilla-foundation/common_voice_13_0
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: Hubert-common_voice-ja-demo-roma-debug-40epochs-cosine
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MOZILLA-FOUNDATION/COMMON_VOICE_13_0 - JA
          type: common_voice_13_0
          config: ja
          split: test
          args: 'Config: ja, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.9989917322040734

Hubert-common_voice-ja-demo-roma-debug-40epochs-cosine

This model is a fine-tuned version of rinna/japanese-hubert-base on the MOZILLA-FOUNDATION/COMMON_VOICE_13_0 - JA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5318
  • Wer: 0.9990
  • Cer: 0.1993

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: 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: 40.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 0.2660 100 16.8172 2.9494 3.4779
No log 0.5319 200 16.5502 2.7606 2.9561
No log 0.7979 300 15.8340 1.9101 1.6839
No log 1.0638 400 13.2919 1.0 0.9276
12.6588 1.3298 500 7.8792 1.0 0.9276
12.6588 1.5957 600 6.1542 1.0 0.9276
12.6588 1.8617 700 5.7757 1.0 0.9276
12.6588 2.1277 800 5.6188 1.0 0.9276
12.6588 2.3936 900 5.4753 1.0 0.9276
5.2353 2.6596 1000 5.3239 1.0 0.9276
5.2353 2.9255 1100 5.1676 1.0 0.9276
5.2353 3.1915 1200 5.0084 1.0 0.9276
5.2353 3.4574 1300 4.8402 1.0 0.9276
5.2353 3.7234 1400 4.6702 1.0 0.9276
4.4502 3.9894 1500 4.4957 1.0 0.9276
4.4502 4.2553 1600 4.3219 1.0 0.9276
4.4502 4.5213 1700 4.1502 1.0 0.9276
4.4502 4.7872 1800 3.9856 1.0 0.9276
4.4502 5.0532 1900 3.8343 1.0 0.9276
3.7863 5.3191 2000 3.6907 1.0 0.9276
3.7863 5.5851 2100 3.5544 1.0 0.9276
3.7863 5.8511 2200 3.4332 1.0 0.9276
3.7863 6.1170 2300 3.3063 1.0 0.9276
3.7863 6.3830 2400 3.2075 1.0 0.9276
3.2473 6.6489 2500 3.1272 1.0 0.9276
3.2473 6.9149 2600 3.0657 1.0 0.9276
3.2473 7.1809 2700 3.0164 1.0 0.9276
3.2473 7.4468 2800 2.9748 1.0 0.9276
3.2473 7.7128 2900 2.9447 1.0 0.9276
2.9649 7.9787 3000 2.9188 1.0 0.9276
2.9649 8.2447 3100 2.9006 1.0 0.9276
2.9649 8.5106 3200 2.8877 1.0 0.9276
2.9649 8.7766 3300 2.8712 1.0 0.9276
2.9649 9.0426 3400 2.8529 1.0 0.9276
2.8673 9.3085 3500 2.8439 1.0 0.9276
2.8673 9.5745 3600 2.8313 1.0 0.9276
2.8673 9.8404 3700 2.8182 1.0 0.9276
2.8673 10.1064 3800 2.7311 1.0 0.9276
2.8673 10.3723 3900 2.4997 1.0 0.9276
2.6801 10.6383 4000 2.2398 1.0 0.8951
2.6801 10.9043 4100 1.9111 1.0 0.6154
2.6801 11.1702 4200 1.5447 1.0 0.4341
2.6801 11.4362 4300 1.3182 1.0 0.3959
2.6801 11.7021 4400 1.1702 0.9996 0.3706
1.5214 11.9681 4500 1.0558 0.9992 0.3214
1.5214 12.2340 4600 0.9717 0.9988 0.3024
1.5214 12.5 4700 0.8959 0.9982 0.2874
1.5214 12.7660 4800 0.8399 0.9978 0.2747
1.5214 13.0319 4900 0.7891 0.9974 0.2657
0.8719 13.2979 5000 0.7484 0.9980 0.2580
0.8719 13.5638 5100 0.7145 0.9976 0.2523
0.8719 13.8298 5200 0.6852 0.9976 0.2481
0.8719 14.0957 5300 0.6618 0.9980 0.2487
0.8719 14.3617 5400 0.6400 0.9986 0.2477
0.6568 14.6277 5500 0.6200 0.9988 0.2449
0.6568 14.8936 5600 0.6032 0.9988 0.2421
0.6568 15.1596 5700 0.5875 0.9984 0.2395
0.6568 15.4255 5800 0.5776 0.9990 0.2409
0.6568 15.6915 5900 0.5617 0.9994 0.2360
0.548 15.9574 6000 0.5485 0.9982 0.2347
0.548 16.2234 6100 0.5394 0.9988 0.2334
0.548 16.4894 6200 0.5322 0.9984 0.2317
0.548 16.7553 6300 0.5243 0.9970 0.2320
0.548 17.0213 6400 0.5121 0.9986 0.2272
0.4681 17.2872 6500 0.5070 0.9990 0.2266
0.4681 17.5532 6600 0.5014 0.9992 0.2263
0.4681 17.8191 6700 0.4943 0.9986 0.2242
0.4681 18.0851 6800 0.4930 0.9988 0.2228
0.4681 18.3511 6900 0.4969 0.9986 0.2245
0.4198 18.6170 7000 0.4883 0.9986 0.2225
0.4198 18.8830 7100 0.4805 0.9986 0.2215
0.4198 19.1489 7200 0.4777 0.9984 0.2208
0.4198 19.4149 7300 0.4718 0.9988 0.2209
0.4198 19.6809 7400 0.4721 0.9984 0.2199
0.3795 19.9468 7500 0.4675 0.9984 0.2205
0.3795 20.2128 7600 0.4692 0.9988 0.2162
0.3795 20.4787 7700 0.4732 0.9986 0.2173
0.3795 20.7447 7800 0.4654 0.9982 0.2173
0.3795 21.0106 7900 0.4557 0.9986 0.2158
0.3504 21.2766 8000 0.4562 0.9982 0.2144
0.3504 21.5426 8100 0.4679 0.9982 0.2144
0.3504 21.8085 8200 0.4584 0.9990 0.2169
0.3504 22.0745 8300 0.4561 0.9982 0.2134
0.3504 22.3404 8400 0.4595 0.9988 0.2143
0.3134 22.6064 8500 0.4544 0.9986 0.2155
0.3134 22.8723 8600 0.4544 0.9984 0.2134
0.3134 23.1383 8700 0.4552 0.9984 0.2129
0.3134 23.4043 8800 0.4524 0.9984 0.2121
0.3134 23.6702 8900 0.4554 0.9986 0.2113
0.3014 23.9362 9000 0.4617 0.9982 0.2103
0.3014 24.2021 9100 0.4606 0.9978 0.2130
0.3014 24.4681 9200 0.4561 0.9974 0.2105
0.3014 24.7340 9300 0.4566 0.9984 0.2089
0.3014 25.0 9400 0.4486 0.9990 0.2119
0.2791 25.2660 9500 0.4542 0.9990 0.2117
0.2791 25.5319 9600 0.4540 0.9986 0.2095
0.2791 25.7979 9700 0.4419 0.9984 0.2091
0.2791 26.0638 9800 0.4569 0.9982 0.2074
0.2791 26.3298 9900 0.4543 0.9984 0.2090
0.2564 26.5957 10000 0.4689 0.9982 0.2088
0.2564 26.8617 10100 0.4590 0.9984 0.2089
0.2564 27.1277 10200 0.4986 0.9986 0.2093
0.2564 27.3936 10300 0.4693 0.9990 0.2100
0.2564 27.6596 10400 0.5128 0.9982 0.2085
0.2449 27.9255 10500 0.4512 0.9984 0.2099
0.2449 28.1915 10600 0.4651 0.9994 0.2091
0.2449 28.4574 10700 0.4604 0.9984 0.2068
0.2449 28.7234 10800 0.4687 0.9990 0.2080
0.2449 28.9894 10900 0.4688 0.9994 0.2064
0.2258 29.2553 11000 0.4759 0.9994 0.2092
0.2258 29.5213 11100 0.4816 0.9988 0.2068
0.2258 29.7872 11200 0.4750 0.9988 0.2053
0.2258 30.0532 11300 0.4753 0.9986 0.2048
0.2258 30.3191 11400 0.4829 0.9992 0.2060
0.2124 30.5851 11500 0.4800 0.9986 0.2081
0.2124 30.8511 11600 0.5290 0.9990 0.2061
0.2124 31.1170 11700 0.5369 0.9988 0.2055
0.2124 31.3830 11800 0.5170 0.9978 0.2041
0.2124 31.6489 11900 0.5229 0.9990 0.2070
0.2007 31.9149 12000 0.5035 0.9986 0.2060
0.2007 32.1809 12100 0.5103 0.9974 0.2049
0.2007 32.4468 12200 0.4868 0.9972 0.2032
0.2007 32.7128 12300 0.4867 0.9996 0.2043
0.2007 32.9787 12400 0.5049 0.9982 0.2040
0.1867 33.2447 12500 0.5126 0.9984 0.2040
0.1867 33.5106 12600 0.5321 0.9992 0.2037
0.1867 33.7766 12700 0.5187 0.9978 0.2040
0.1867 34.0426 12800 0.5319 0.9990 0.2064
0.1867 34.3085 12900 0.5275 0.9980 0.2041
0.1749 34.5745 13000 0.5433 0.9982 0.2043
0.1749 34.8404 13100 0.5094 0.9984 0.2023
0.1749 35.1064 13200 0.5363 0.9990 0.2004
0.1749 35.3723 13300 0.5331 0.9994 0.2022
0.1749 35.6383 13400 0.5053 0.9990 0.2009
0.1604 35.9043 13500 0.5157 0.9990 0.2026
0.1604 36.1702 13600 0.5299 0.9990 0.2018
0.1604 36.4362 13700 0.5117 0.9996 0.2050
0.1604 36.7021 13800 0.5067 0.9994 0.2038
0.1604 36.9681 13900 0.4994 0.9996 0.2028
0.1412 37.2340 14000 0.5346 0.9984 0.2024
0.1412 37.5 14100 0.5350 0.9994 0.2015
0.1412 37.7660 14200 0.5237 0.9990 0.2010
0.1412 38.0319 14300 0.5305 0.9992 0.1993
0.1412 38.2979 14400 0.5309 0.9986 0.1973
0.1286 38.5638 14500 0.5270 0.9992 0.1992
0.1286 38.8298 14600 0.5363 0.9990 0.1999
0.1286 39.0957 14700 0.5347 0.9990 0.1999
0.1286 39.3617 14800 0.5319 0.9990 0.1999
0.1286 39.6277 14900 0.5322 0.9994 0.1995
0.1217 39.8936 15000 0.5322 0.9992 0.1992

Framework versions

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