--- 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 metrics: - wer model-index: - name: Hubert-common_voice_JSUT-ja-demo-roma results: [] --- # Hubert-common_voice_JSUT-ja-demo-roma This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the MOZILLA-FOUNDATION/COMMON_VOICE_13_0 - JA dataset. It achieves the following results on the evaluation set: - Loss: 0.3850 - Wer: 0.9984 - Cer: 0.1901 ## 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: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| | No log | 0.1934 | 100 | 16.2719 | 3.0918 | 3.3907 | | No log | 0.3868 | 200 | 16.0057 | 2.8734 | 2.8937 | | No log | 0.5803 | 300 | 15.3748 | 1.9646 | 1.6906 | | No log | 0.7737 | 400 | 13.0439 | 1.0 | 0.9292 | | 11.8785 | 0.9671 | 500 | 7.6485 | 1.0 | 0.9292 | | 11.8785 | 1.1605 | 600 | 6.0074 | 1.0 | 0.9292 | | 11.8785 | 1.3540 | 700 | 5.6465 | 1.0 | 0.9292 | | 11.8785 | 1.5474 | 800 | 5.5026 | 1.0 | 0.9292 | | 11.8785 | 1.7408 | 900 | 5.3618 | 1.0 | 0.9292 | | 4.9912 | 1.9342 | 1000 | 5.2193 | 1.0 | 0.9292 | | 4.9912 | 2.1277 | 1100 | 5.0688 | 1.0 | 0.9292 | | 4.9912 | 2.3211 | 1200 | 4.9141 | 1.0 | 0.9292 | | 4.9912 | 2.5145 | 1300 | 4.7537 | 1.0 | 0.9292 | | 4.9912 | 2.7079 | 1400 | 4.5894 | 1.0 | 0.9292 | | 4.3024 | 2.9014 | 1500 | 4.4225 | 1.0 | 0.9292 | | 4.3024 | 3.0948 | 1600 | 4.2562 | 1.0 | 0.9292 | | 4.3024 | 3.2882 | 1700 | 4.0944 | 1.0 | 0.9292 | | 4.3024 | 3.4816 | 1800 | 3.9344 | 1.0 | 0.9292 | | 4.3024 | 3.6750 | 1900 | 3.7835 | 1.0 | 0.9292 | | 3.6966 | 3.8685 | 2000 | 3.6411 | 1.0 | 0.9292 | | 3.6966 | 4.0619 | 2100 | 3.5156 | 1.0 | 0.9292 | | 3.6966 | 4.2553 | 2200 | 3.3973 | 1.0 | 0.9292 | | 3.6966 | 4.4487 | 2300 | 3.2909 | 1.0 | 0.9292 | | 3.6966 | 4.6422 | 2400 | 3.1957 | 1.0 | 0.9292 | | 3.2011 | 4.8356 | 2500 | 3.1159 | 1.0 | 0.9292 | | 3.2011 | 5.0290 | 2600 | 3.0544 | 1.0 | 0.9292 | | 3.2011 | 5.2224 | 2700 | 3.0039 | 1.0 | 0.9292 | | 3.2011 | 5.4159 | 2800 | 2.9654 | 1.0 | 0.9292 | | 3.2011 | 5.6093 | 2900 | 2.9387 | 1.0 | 0.9292 | | 2.9439 | 5.8027 | 3000 | 2.9091 | 1.0 | 0.9292 | | 2.9439 | 5.9961 | 3100 | 2.8868 | 1.0 | 0.9292 | | 2.9439 | 6.1896 | 3200 | 2.8660 | 1.0 | 0.9292 | | 2.9439 | 6.3830 | 3300 | 2.8533 | 1.0 | 0.9292 | | 2.9439 | 6.5764 | 3400 | 2.7337 | 1.0 | 0.9292 | | 2.7884 | 6.7698 | 3500 | 2.5230 | 1.0 | 0.9292 | | 2.7884 | 6.9632 | 3600 | 2.2724 | 1.0 | 0.9182 | | 2.7884 | 7.1567 | 3700 | 1.9633 | 1.0 | 0.6316 | | 2.7884 | 7.3501 | 3800 | 1.5858 | 1.0 | 0.4242 | | 2.7884 | 7.5435 | 3900 | 1.3510 | 0.9998 | 0.3861 | | 1.7651 | 7.7369 | 4000 | 1.1917 | 0.9993 | 0.3334 | | 1.7651 | 7.9304 | 4100 | 1.0716 | 0.9980 | 0.2982 | | 1.7651 | 8.1238 | 4200 | 0.9762 | 0.9976 | 0.2782 | | 1.7651 | 8.3172 | 4300 | 0.9044 | 0.9965 | 0.2596 | | 1.7651 | 8.5106 | 4400 | 0.8529 | 0.9963 | 0.2566 | | 0.9278 | 8.7041 | 4500 | 0.7958 | 0.9971 | 0.2466 | | 0.9278 | 8.8975 | 4600 | 0.7535 | 0.9965 | 0.2435 | | 0.9278 | 9.0909 | 4700 | 0.7190 | 0.9974 | 0.2403 | | 0.9278 | 9.2843 | 4800 | 0.6800 | 0.9974 | 0.2356 | | 0.9278 | 9.4778 | 4900 | 0.6568 | 0.9963 | 0.2330 | | 0.6673 | 9.6712 | 5000 | 0.6318 | 0.9960 | 0.2329 | | 0.6673 | 9.8646 | 5100 | 0.6132 | 0.9973 | 0.2293 | | 0.6673 | 10.0580 | 5200 | 0.5896 | 0.9971 | 0.2261 | | 0.6673 | 10.2515 | 5300 | 0.5743 | 0.9962 | 0.2231 | | 0.6673 | 10.4449 | 5400 | 0.5562 | 0.9960 | 0.2215 | | 0.5392 | 10.6383 | 5500 | 0.5473 | 0.9973 | 0.2237 | | 0.5392 | 10.8317 | 5600 | 0.5307 | 0.9963 | 0.2185 | | 0.5392 | 11.0251 | 5700 | 0.5195 | 0.9976 | 0.2173 | | 0.5392 | 11.2186 | 5800 | 0.5090 | 0.9978 | 0.2164 | | 0.5392 | 11.4120 | 5900 | 0.4979 | 0.9974 | 0.2135 | | 0.4572 | 11.6054 | 6000 | 0.4901 | 0.9974 | 0.2127 | | 0.4572 | 11.7988 | 6100 | 0.4872 | 0.9993 | 0.2137 | | 0.4572 | 11.9923 | 6200 | 0.4754 | 0.9973 | 0.2119 | | 0.4572 | 12.1857 | 6300 | 0.4724 | 0.9969 | 0.2120 | | 0.4572 | 12.3791 | 6400 | 0.4650 | 0.9987 | 0.2088 | | 0.41 | 12.5725 | 6500 | 0.4592 | 0.9976 | 0.2076 | | 0.41 | 12.7660 | 6600 | 0.4503 | 0.9982 | 0.2064 | | 0.41 | 12.9594 | 6700 | 0.4478 | 0.9963 | 0.2099 | | 0.41 | 13.1528 | 6800 | 0.4496 | 0.9982 | 0.2061 | | 0.41 | 13.3462 | 6900 | 0.4438 | 0.9982 | 0.2052 | | 0.3688 | 13.5397 | 7000 | 0.4365 | 0.9991 | 0.2040 | | 0.3688 | 13.7331 | 7100 | 0.4288 | 0.9980 | 0.2046 | | 0.3688 | 13.9265 | 7200 | 0.4299 | 0.9982 | 0.2025 | | 0.3688 | 14.1199 | 7300 | 0.4274 | 0.9985 | 0.2026 | | 0.3688 | 14.3133 | 7400 | 0.4242 | 0.9984 | 0.2006 | | 0.3394 | 14.5068 | 7500 | 0.4253 | 0.9971 | 0.2001 | | 0.3394 | 14.7002 | 7600 | 0.4178 | 0.9974 | 0.1996 | | 0.3394 | 14.8936 | 7700 | 0.4182 | 0.9984 | 0.2004 | | 0.3394 | 15.0870 | 7800 | 0.4194 | 0.9971 | 0.1979 | | 0.3394 | 15.2805 | 7900 | 0.4160 | 0.9978 | 0.1997 | | 0.3157 | 15.4739 | 8000 | 0.4096 | 0.9974 | 0.2010 | | 0.3157 | 15.6673 | 8100 | 0.4088 | 0.9978 | 0.1980 | | 0.3157 | 15.8607 | 8200 | 0.4119 | 0.9984 | 0.1974 | | 0.3157 | 16.0542 | 8300 | 0.4099 | 0.9984 | 0.1965 | | 0.3157 | 16.2476 | 8400 | 0.4086 | 0.9985 | 0.1977 | | 0.2917 | 16.4410 | 8500 | 0.4097 | 0.9984 | 0.1968 | | 0.2917 | 16.6344 | 8600 | 0.4113 | 0.9980 | 0.1949 | | 0.2917 | 16.8279 | 8700 | 0.4018 | 0.9984 | 0.1956 | | 0.2917 | 17.0213 | 8800 | 0.4043 | 0.9984 | 0.1934 | | 0.2917 | 17.2147 | 8900 | 0.4046 | 0.9980 | 0.1946 | | 0.2785 | 17.4081 | 9000 | 0.4046 | 0.9982 | 0.1927 | | 0.2785 | 17.6015 | 9100 | 0.4016 | 0.9989 | 0.1948 | | 0.2785 | 17.7950 | 9200 | 0.4013 | 0.9984 | 0.1922 | | 0.2785 | 17.9884 | 9300 | 0.3879 | 0.9989 | 0.1930 | | 0.2785 | 18.1818 | 9400 | 0.4009 | 0.9980 | 0.1928 | | 0.2647 | 18.3752 | 9500 | 0.3904 | 0.9985 | 0.1926 | | 0.2647 | 18.5687 | 9600 | 0.3944 | 0.9984 | 0.1959 | | 0.2647 | 18.7621 | 9700 | 0.3957 | 0.9989 | 0.1959 | | 0.2647 | 18.9555 | 9800 | 0.3949 | 0.9982 | 0.1938 | | 0.2647 | 19.1489 | 9900 | 0.4039 | 0.9973 | 0.1933 | | 0.248 | 19.3424 | 10000 | 0.4082 | 0.9991 | 0.1934 | | 0.248 | 19.5358 | 10100 | 0.4074 | 0.9993 | 0.1922 | | 0.248 | 19.7292 | 10200 | 0.3955 | 0.9989 | 0.1906 | | 0.248 | 19.9226 | 10300 | 0.3856 | 0.9980 | 0.1909 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3