--- 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-phoneme-ctc_zero_infinity 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: 1.0 --- # Hubert-common_voice-phoneme-ctc_zero_infinity 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: 0.5230 - Wer: 1.0 - Cer: 0.1953 ## 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: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:----:|:---------------:|:------:|:------:| | No log | 0.2660 | 100 | 18.2381 | 1.1471 | 1.8153 | | No log | 0.5319 | 200 | 8.1726 | 1.0 | 0.9817 | | No log | 0.7979 | 300 | 6.9386 | 1.0 | 0.9817 | | No log | 1.0638 | 400 | 6.2389 | 1.0 | 0.9817 | | 8.8178 | 1.3298 | 500 | 5.4653 | 1.0 | 0.9817 | | 8.8178 | 1.5957 | 600 | 4.6745 | 1.0 | 0.9817 | | 8.8178 | 1.8617 | 700 | 3.9771 | 1.0 | 0.9817 | | 8.8178 | 2.1277 | 800 | 3.4579 | 1.0 | 0.9817 | | 8.8178 | 2.3936 | 900 | 3.1745 | 1.0 | 0.9817 | | 3.6858 | 2.6596 | 1000 | 3.0675 | 1.0 | 0.9817 | | 3.6858 | 2.9255 | 1100 | 3.0343 | 1.0 | 0.9817 | | 3.6858 | 3.1915 | 1200 | 3.0102 | 1.0 | 0.9817 | | 3.6858 | 3.4574 | 1300 | 2.9925 | 1.0 | 0.9817 | | 3.6858 | 3.7234 | 1400 | 2.5595 | 1.0 | 0.9367 | | 2.7891 | 3.9894 | 1500 | 1.5432 | 1.0 | 0.3742 | | 2.7891 | 4.2553 | 1600 | 1.0799 | 1.0 | 0.2972 | | 2.7891 | 4.5213 | 1700 | 0.8670 | 1.0 | 0.2639 | | 2.7891 | 4.7872 | 1800 | 0.7350 | 1.0 | 0.2559 | | 2.7891 | 5.0532 | 1900 | 0.6753 | 1.0 | 0.2468 | | 0.9179 | 5.3191 | 2000 | 0.6171 | 1.0 | 0.2389 | | 0.9179 | 5.5851 | 2100 | 0.5866 | 1.0 | 0.2386 | | 0.9179 | 5.8511 | 2200 | 0.5649 | 1.0 | 0.2389 | | 0.9179 | 6.1170 | 2300 | 0.5368 | 1.0 | 0.2321 | | 0.9179 | 6.3830 | 2400 | 0.5225 | 1.0 | 0.2289 | | 0.563 | 6.6489 | 2500 | 0.5042 | 1.0 | 0.2293 | | 0.563 | 6.9149 | 2600 | 0.4918 | 1.0 | 0.2247 | | 0.563 | 7.1809 | 2700 | 0.4881 | 1.0 | 0.2208 | | 0.563 | 7.4468 | 2800 | 0.4787 | 1.0 | 0.2198 | | 0.563 | 7.7128 | 2900 | 0.4692 | 1.0 | 0.2181 | | 0.4453 | 7.9787 | 3000 | 0.4733 | 1.0 | 0.2151 | | 0.4453 | 8.2447 | 3100 | 0.4585 | 1.0 | 0.2147 | | 0.4453 | 8.5106 | 3200 | 0.4463 | 1.0 | 0.2116 | | 0.4453 | 8.7766 | 3300 | 0.4183 | 1.0 | 0.2055 | | 0.4453 | 9.0426 | 3400 | 0.4308 | 0.9998 | 0.2032 | | 0.3596 | 9.3085 | 3500 | 0.4070 | 1.0 | 0.2022 | | 0.3596 | 9.5745 | 3600 | 0.4259 | 1.0 | 0.2024 | | 0.3596 | 9.8404 | 3700 | 0.4038 | 1.0 | 0.1985 | | 0.3596 | 10.1064 | 3800 | 0.4272 | 1.0 | 0.1976 | | 0.3596 | 10.3723 | 3900 | 0.3961 | 0.9998 | 0.1969 | | 0.2945 | 10.6383 | 4000 | 0.4180 | 1.0 | 0.1943 | | 0.2945 | 10.9043 | 4100 | 0.3999 | 1.0 | 0.1975 | | 0.2945 | 11.1702 | 4200 | 0.3879 | 1.0 | 0.1930 | | 0.2945 | 11.4362 | 4300 | 0.3799 | 1.0 | 0.1918 | | 0.2945 | 11.7021 | 4400 | 0.3764 | 0.9998 | 0.1927 | | 0.2605 | 11.9681 | 4500 | 0.3725 | 1.0 | 0.1919 | | 0.2605 | 12.2340 | 4600 | 0.3910 | 1.0 | 0.1919 | | 0.2605 | 12.5 | 4700 | 0.3851 | 0.9996 | 0.1908 | | 0.2605 | 12.7660 | 4800 | 0.4115 | 1.0 | 0.1906 | | 0.2605 | 13.0319 | 4900 | 0.3779 | 1.0 | 0.1894 | | 0.2223 | 13.2979 | 5000 | 0.3956 | 1.0 | 0.1904 | | 0.2223 | 13.5638 | 5100 | 0.4001 | 1.0 | 0.1907 | | 0.2223 | 13.8298 | 5200 | 0.3891 | 1.0 | 0.1948 | | 0.2223 | 14.0957 | 5300 | 0.3940 | 1.0 | 0.1902 | | 0.2223 | 14.3617 | 5400 | 0.4056 | 1.0 | 0.1909 | | 0.211 | 14.6277 | 5500 | 0.4000 | 0.9998 | 0.1929 | | 0.211 | 14.8936 | 5600 | 0.3926 | 1.0 | 0.1895 | | 0.211 | 15.1596 | 5700 | 0.3852 | 0.9998 | 0.1930 | | 0.211 | 15.4255 | 5800 | 0.3864 | 1.0 | 0.1886 | | 0.211 | 15.6915 | 5900 | 0.3951 | 0.9998 | 0.1909 | | 0.1983 | 15.9574 | 6000 | 0.3951 | 1.0 | 0.1882 | | 0.1983 | 16.2234 | 6100 | 0.4087 | 1.0 | 0.1918 | | 0.1983 | 16.4894 | 6200 | 0.4150 | 1.0 | 0.1891 | | 0.1983 | 16.7553 | 6300 | 0.4008 | 0.9998 | 0.1907 | | 0.1983 | 17.0213 | 6400 | 0.4220 | 1.0 | 0.1943 | | 0.1829 | 17.2872 | 6500 | 0.4154 | 1.0 | 0.1925 | | 0.1829 | 17.5532 | 6600 | 0.4482 | 1.0 | 0.1959 | | 0.1829 | 17.8191 | 6700 | 0.4217 | 0.9998 | 0.1939 | | 0.1829 | 18.0851 | 6800 | 0.4383 | 0.9998 | 0.1916 | | 0.1829 | 18.3511 | 6900 | 0.4226 | 1.0 | 0.1926 | | 0.1757 | 18.6170 | 7000 | 0.4170 | 0.9998 | 0.1916 | | 0.1757 | 18.8830 | 7100 | 0.4162 | 1.0 | 0.1918 | | 0.1757 | 19.1489 | 7200 | 0.4350 | 0.9998 | 0.1910 | | 0.1757 | 19.4149 | 7300 | 0.4403 | 1.0 | 0.2022 | | 0.1757 | 19.6809 | 7400 | 0.4325 | 0.9998 | 0.1944 | | 0.1801 | 19.9468 | 7500 | 0.5488 | 1.0 | 0.1977 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3