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Hubert-common_voice-ja-demo-japanese-debug-cosine

Automatic Speech Recognition
Transformers
Safetensors
Japanese
hubert
mozilla-foundation/common_voice_13_0
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: rinna/japanese-hubert-base
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - common_voice_13_0
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: Hubert-common_voice-ja-demo-japanese-debug-cosine
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+ results:
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+ - task:
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+ name: Automatic Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: common_voice_13_0
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+ type: common_voice_13_0
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+ config: ja
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+ split: test
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+ args: ja
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+ metrics:
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+ - name: Wer
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+ type: wer
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+ value: 1.945630285944422
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # Hubert-common_voice-ja-demo-japanese-debug-cosine
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+
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+ This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the common_voice_13_0 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3636
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+ - Wer: 1.9456
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+ - Cer: 0.5335
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 12500
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+ - num_epochs: 50.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
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+ | No log | 0.2660 | 100 | 85.6894 | 1.0085 | 8.6671 |
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+ | No log | 0.5319 | 200 | 84.9253 | 1.0149 | 8.6014 |
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+ | No log | 0.7979 | 300 | 82.9367 | 1.0159 | 6.5921 |
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+ | No log | 1.0638 | 400 | 75.8485 | 1.0 | 0.9860 |
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+ | 68.3352 | 1.3298 | 500 | 62.8321 | 1.0 | 0.9859 |
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+ | 68.3352 | 1.5957 | 600 | 57.6763 | 1.0 | 0.9859 |
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+ | 68.3352 | 1.8617 | 700 | 56.3682 | 1.0 | 0.9859 |
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+ | 68.3352 | 2.1277 | 800 | 55.7325 | 1.0 | 0.9859 |
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+ | 68.3352 | 2.3936 | 900 | 55.0750 | 1.0 | 0.9859 |
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+ | 48.4779 | 2.6596 | 1000 | 54.3879 | 1.0 | 0.9859 |
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+ | 48.4779 | 2.9255 | 1100 | 53.6522 | 1.0 | 0.9859 |
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+ | 48.4779 | 3.1915 | 1200 | 52.8021 | 1.0 | 0.9859 |
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+ | 48.4779 | 3.4574 | 1300 | 51.9015 | 1.0 | 0.9859 |
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+ | 48.4779 | 3.7234 | 1400 | 50.9048 | 1.0 | 0.9859 |
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+ | 44.647 | 3.9894 | 1500 | 49.8562 | 1.0 | 0.9859 |
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+ | 44.647 | 4.2553 | 1600 | 48.6802 | 1.0 | 0.9859 |
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+ | 44.647 | 4.5213 | 1700 | 47.4341 | 1.0 | 0.9860 |
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+ | 44.647 | 4.7872 | 1800 | 46.0720 | 1.0 | 0.9859 |
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+ | 44.647 | 5.0532 | 1900 | 44.6128 | 1.0 | 0.9859 |
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+ | 39.9761 | 5.3191 | 2000 | 43.0685 | 1.0 | 0.9859 |
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+ | 39.9761 | 5.5851 | 2100 | 41.3959 | 1.0 | 0.9859 |
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+ | 39.9761 | 5.8511 | 2200 | 39.6261 | 1.0 | 0.9859 |
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+ | 39.9761 | 6.1170 | 2300 | 37.7607 | 1.0 | 0.9859 |
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+ | 39.9761 | 6.3830 | 2400 | 35.7686 | 1.0 | 0.9859 |
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+ | 33.1415 | 6.6489 | 2500 | 33.6543 | 1.0 | 0.9859 |
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+ | 33.1415 | 6.9149 | 2600 | 31.4395 | 1.0 | 0.9859 |
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+ | 33.1415 | 7.1809 | 2700 | 29.1500 | 1.0 | 0.9859 |
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+ | 33.1415 | 7.4468 | 2800 | 26.7739 | 1.0 | 0.9859 |
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+ | 33.1415 | 7.7128 | 2900 | 24.3323 | 1.0 | 0.9859 |
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+ | 24.1405 | 7.9787 | 3000 | 21.8788 | 1.0 | 0.9861 |
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+ | 24.1405 | 8.2447 | 3100 | 19.3927 | 1.0 | 0.9859 |
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+ | 24.1405 | 8.5106 | 3200 | 17.0046 | 1.0 | 0.9859 |
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+ | 24.1405 | 8.7766 | 3300 | 14.7217 | 1.0 | 0.9859 |
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+ | 24.1405 | 9.0426 | 3400 | 12.6468 | 1.0 | 0.9860 |
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+ | 14.1936 | 9.3085 | 3500 | 10.8049 | 1.0 | 0.9859 |
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+ | 14.1936 | 9.5745 | 3600 | 9.2678 | 1.0 | 0.9859 |
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+ | 14.1936 | 9.8404 | 3700 | 8.0368 | 1.0 | 0.9860 |
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+ | 14.1936 | 10.1064 | 3800 | 7.1212 | 1.0 | 0.9860 |
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+ | 14.1936 | 10.3723 | 3900 | 6.4852 | 1.0 | 0.9859 |
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+ | 7.4544 | 10.6383 | 4000 | 6.0728 | 1.0 | 0.9859 |
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+ | 7.4544 | 10.9043 | 4100 | 5.8352 | 1.0 | 0.9859 |
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+ | 7.4544 | 11.1702 | 4200 | 5.7644 | 1.0 | 0.9859 |
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+ | 7.4544 | 11.4362 | 4300 | 5.6726 | 1.0 | 0.9859 |
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+ | 7.4544 | 11.7021 | 4400 | 5.6452 | 1.0 | 0.9860 |
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+ | 5.7484 | 11.9681 | 4500 | 5.5683 | 1.0 | 0.9859 |
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+ | 5.7484 | 12.2340 | 4600 | 5.5095 | 1.0 | 0.9859 |
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+ | 5.7484 | 12.5 | 4700 | 5.4579 | 1.0 | 0.9859 |
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+ | 5.7484 | 12.7660 | 4800 | 5.4164 | 1.0 | 0.9859 |
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+ | 5.7484 | 13.0319 | 4900 | 5.3754 | 1.0 | 0.9859 |
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+ | 5.466 | 13.2979 | 5000 | 5.3542 | 1.0008 | 0.9859 |
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+ | 5.466 | 13.5638 | 5100 | 5.3131 | 1.1907 | 0.9857 |
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+ | 5.466 | 13.8298 | 5200 | 5.2739 | 1.5616 | 0.9856 |
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+ | 5.466 | 14.0957 | 5300 | 5.2416 | 1.2078 | 0.9860 |
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+ | 5.466 | 14.3617 | 5400 | 5.2095 | 1.3713 | 0.9860 |
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+ | 5.257 | 14.6277 | 5500 | 5.1721 | 1.5914 | 0.9858 |
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+ | 5.257 | 14.8936 | 5600 | 5.1435 | 1.3063 | 0.9857 |
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+ | 5.257 | 15.1596 | 5700 | 5.1101 | 1.5610 | 0.9857 |
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+ | 5.257 | 15.4255 | 5800 | 5.0661 | 1.7831 | 0.9861 |
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+ | 5.257 | 15.6915 | 5900 | 5.0305 | 1.7904 | 0.9862 |
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+ | 5.0645 | 15.9574 | 6000 | 4.9771 | 1.6460 | 0.9852 |
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+ | 5.0645 | 16.2234 | 6100 | 4.9207 | 1.4819 | 0.9849 |
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+ | 5.0645 | 16.4894 | 6200 | 4.8480 | 1.3870 | 0.9846 |
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+ | 5.0645 | 16.7553 | 6300 | 4.7647 | 1.5983 | 0.9794 |
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+ | 5.0645 | 17.0213 | 6400 | 4.6895 | 1.6639 | 0.9769 |
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+ | 4.7633 | 17.2872 | 6500 | 4.6069 | 1.6518 | 0.9685 |
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+ | 4.7633 | 17.5532 | 6600 | 4.4924 | 1.7159 | 0.9643 |
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+ | 4.7633 | 17.8191 | 6700 | 4.3929 | 1.7851 | 0.9537 |
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+ | 4.7633 | 18.0851 | 6800 | 4.2750 | 1.8838 | 0.9071 |
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+ | 4.7633 | 18.3511 | 6900 | 4.1479 | 1.9080 | 0.8752 |
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+ | 4.298 | 18.6170 | 7000 | 4.0495 | 1.9148 | 0.8506 |
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+ | 4.298 | 18.8830 | 7100 | 3.9533 | 1.9289 | 0.8382 |
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+ | 4.298 | 19.1489 | 7200 | 3.8649 | 1.9211 | 0.8414 |
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+ | 4.298 | 19.4149 | 7300 | 3.7631 | 1.9229 | 0.8150 |
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+ | 4.298 | 19.6809 | 7400 | 3.6881 | 1.9156 | 0.8070 |
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+ | 3.8085 | 19.9468 | 7500 | 3.6324 | 1.9325 | 0.8051 |
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+ | 3.8085 | 20.2128 | 7600 | 3.5423 | 1.9184 | 0.7772 |
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+ | 3.8085 | 20.4787 | 7700 | 3.4706 | 1.9229 | 0.7731 |
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+ | 3.8085 | 20.7447 | 7800 | 3.4111 | 1.9084 | 0.7712 |
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+ | 3.8085 | 21.0106 | 7900 | 3.3576 | 1.9211 | 0.7663 |
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+ | 3.4397 | 21.2766 | 8000 | 3.2964 | 1.9219 | 0.7642 |
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+ | 3.4397 | 21.5426 | 8100 | 3.2338 | 1.9184 | 0.7542 |
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+ | 3.4397 | 21.8085 | 8200 | 3.2086 | 1.9388 | 0.7627 |
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+ | 3.4397 | 22.0745 | 8300 | 3.1352 | 1.9172 | 0.7449 |
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+ | 3.4397 | 22.3404 | 8400 | 3.1061 | 1.9388 | 0.7425 |
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+ | 3.1352 | 22.6064 | 8500 | 3.0486 | 1.9084 | 0.7352 |
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+ | 3.1352 | 22.8723 | 8600 | 2.9802 | 1.9221 | 0.7325 |
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+ | 3.1352 | 23.1383 | 8700 | 2.9792 | 1.9652 | 0.7435 |
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+ | 3.1352 | 23.4043 | 8800 | 2.9154 | 1.9333 | 0.7334 |
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+ | 3.1352 | 23.6702 | 8900 | 2.8665 | 1.9783 | 0.7203 |
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+ | 2.8479 | 23.9362 | 9000 | 2.8167 | 1.9569 | 0.7277 |
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+ | 2.8479 | 24.2021 | 9100 | 2.7657 | 1.9364 | 0.7062 |
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+ | 2.8479 | 24.4681 | 9200 | 2.7301 | 1.9807 | 0.7037 |
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+ | 2.8479 | 24.7340 | 9300 | 2.6957 | 1.9501 | 0.6956 |
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+ | 2.8479 | 25.0 | 9400 | 2.6688 | 1.9851 | 0.6947 |
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+ | 2.5799 | 25.2660 | 9500 | 2.6196 | 1.9869 | 0.6925 |
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+ | 2.5799 | 25.5319 | 9600 | 2.5810 | 1.9917 | 0.6914 |
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+ | 2.5799 | 25.7979 | 9700 | 2.5493 | 1.9895 | 0.6860 |
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+ | 2.5799 | 26.0638 | 9800 | 2.5001 | 1.9837 | 0.6787 |
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+ | 2.5799 | 26.3298 | 9900 | 2.4738 | 1.9750 | 0.6767 |
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+ | 2.3389 | 26.5957 | 10000 | 2.4752 | 1.9829 | 0.6685 |
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+ | 2.3389 | 26.8617 | 10100 | 2.4339 | 1.9698 | 0.6659 |
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+ | 2.3389 | 27.1277 | 10200 | 2.3766 | 1.9909 | 0.6602 |
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+ | 2.3389 | 27.3936 | 10300 | 2.3440 | 1.9905 | 0.6588 |
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+ | 2.3389 | 27.6596 | 10400 | 2.3124 | 1.9911 | 0.6591 |
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+ | 2.1275 | 27.9255 | 10500 | 2.2800 | 1.9917 | 0.6543 |
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+ | 2.1275 | 28.1915 | 10600 | 2.2481 | 1.9497 | 0.6418 |
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+ | 2.1275 | 28.4574 | 10700 | 2.2140 | 1.9857 | 0.6396 |
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+ | 2.1275 | 28.7234 | 10800 | 2.1964 | 1.9889 | 0.6345 |
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+ | 2.1275 | 28.9894 | 10900 | 2.1698 | 1.9905 | 0.6459 |
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+ | 1.9256 | 29.2553 | 11000 | 2.1353 | 1.9925 | 0.6352 |
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+ | 1.9256 | 29.5213 | 11100 | 2.1014 | 1.9905 | 0.6392 |
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+ | 1.9256 | 29.7872 | 11200 | 2.0988 | 1.9917 | 0.6413 |
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+ | 1.9256 | 30.0532 | 11300 | 2.0646 | 1.9917 | 0.6259 |
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+ | 1.9256 | 30.3191 | 11400 | 2.0163 | 1.9899 | 0.6277 |
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+ | 1.7455 | 30.5851 | 11500 | 1.9995 | 1.9895 | 0.6217 |
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+ | 1.7455 | 30.8511 | 11600 | 1.9474 | 1.9887 | 0.6199 |
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+ | 1.7455 | 31.1170 | 11700 | 1.9520 | 1.9865 | 0.6122 |
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+ | 1.7455 | 31.3830 | 11800 | 1.9263 | 1.9883 | 0.6086 |
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+ | 1.7455 | 31.6489 | 11900 | 1.9068 | 1.9881 | 0.6072 |
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+ | 1.5853 | 31.9149 | 12000 | 1.8782 | 1.9875 | 0.6112 |
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+ | 1.5853 | 32.1809 | 12100 | 1.8634 | 1.9869 | 0.6092 |
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+ | 1.5853 | 32.4468 | 12200 | 1.8379 | 1.9847 | 0.5918 |
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+ | 1.5853 | 32.7128 | 12300 | 1.8158 | 1.9853 | 0.6022 |
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+ | 1.5853 | 32.9787 | 12400 | 1.7806 | 1.9861 | 0.5930 |
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+ | 1.4359 | 33.2447 | 12500 | 1.7551 | 1.9823 | 0.5925 |
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+ | 1.4359 | 33.5106 | 12600 | 1.7456 | 1.9827 | 0.5923 |
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+ | 1.4359 | 33.7766 | 12700 | 1.7167 | 1.9774 | 0.5796 |
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+ | 1.4359 | 34.0426 | 12800 | 1.7160 | 1.9789 | 0.5743 |
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+ | 1.4359 | 34.3085 | 12900 | 1.7003 | 1.9766 | 0.5821 |
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+ | 1.3053 | 34.5745 | 13000 | 1.6828 | 1.9758 | 0.5650 |
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+ | 1.3053 | 34.8404 | 13100 | 1.6581 | 1.9750 | 0.5748 |
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+ | 1.3053 | 35.1064 | 13200 | 1.6576 | 1.9730 | 0.5659 |
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+ | 1.3053 | 35.3723 | 13300 | 1.6066 | 1.9708 | 0.5649 |
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+ | 1.3053 | 35.6383 | 13400 | 1.6108 | 1.9714 | 0.5658 |
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+ | 1.1806 | 35.9043 | 13500 | 1.6060 | 1.9732 | 0.5596 |
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+ | 1.1806 | 36.1702 | 13600 | 1.5894 | 1.9756 | 0.5651 |
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+ | 1.1806 | 36.4362 | 13700 | 1.5770 | 1.9710 | 0.5585 |
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+ | 1.1806 | 36.7021 | 13800 | 1.5784 | 1.9706 | 0.5614 |
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+ | 1.1806 | 36.9681 | 13900 | 1.5618 | 1.9702 | 0.5567 |
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+ | 1.0758 | 37.2340 | 14000 | 1.5304 | 1.9680 | 0.5560 |
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+ | 1.0758 | 37.5 | 14100 | 1.5293 | 1.9662 | 0.5488 |
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+ | 1.0758 | 37.7660 | 14200 | 1.5076 | 1.9670 | 0.5478 |
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+ | 1.0758 | 38.0319 | 14300 | 1.4922 | 1.9625 | 0.5493 |
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+ | 1.0758 | 38.2979 | 14400 | 1.4985 | 1.9621 | 0.5491 |
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+ | 0.9784 | 38.5638 | 14500 | 1.4935 | 1.9627 | 0.5509 |
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+ | 0.9784 | 38.8298 | 14600 | 1.4927 | 1.9636 | 0.5503 |
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+ | 0.9784 | 39.0957 | 14700 | 1.5040 | 1.9631 | 0.5491 |
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+ | 0.9784 | 39.3617 | 14800 | 1.4626 | 1.9593 | 0.5422 |
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+ | 0.9784 | 39.6277 | 14900 | 1.4509 | 1.9629 | 0.5418 |
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+ | 0.9014 | 39.8936 | 15000 | 1.4410 | 1.9555 | 0.5420 |
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+ | 0.9014 | 40.1596 | 15100 | 1.4515 | 1.9585 | 0.5469 |
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+ | 0.9014 | 40.4255 | 15200 | 1.4180 | 1.9579 | 0.5449 |
225
+ | 0.9014 | 40.6915 | 15300 | 1.4222 | 1.9565 | 0.5386 |
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+ | 0.9014 | 40.9574 | 15400 | 1.4090 | 1.9555 | 0.5416 |
227
+ | 0.8296 | 41.2234 | 15500 | 1.4069 | 1.9529 | 0.5403 |
228
+ | 0.8296 | 41.4894 | 15600 | 1.4077 | 1.9527 | 0.5362 |
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+ | 0.8296 | 41.7553 | 15700 | 1.4173 | 1.9563 | 0.5406 |
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+ | 0.8296 | 42.0213 | 15800 | 1.4264 | 1.9593 | 0.5431 |
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+ | 0.8296 | 42.2872 | 15900 | 1.4124 | 1.9557 | 0.5376 |
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+ | 0.7808 | 42.5532 | 16000 | 1.4008 | 1.9547 | 0.5363 |
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+ | 0.7808 | 42.8191 | 16100 | 1.4079 | 1.9533 | 0.5366 |
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+ | 0.7808 | 43.0851 | 16200 | 1.3887 | 1.9509 | 0.5405 |
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+ | 0.7808 | 43.3511 | 16300 | 1.3935 | 1.9513 | 0.5390 |
236
+ | 0.7808 | 43.6170 | 16400 | 1.3829 | 1.9531 | 0.5399 |
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+ | 0.7318 | 43.8830 | 16500 | 1.3820 | 1.9515 | 0.5324 |
238
+ | 0.7318 | 44.1489 | 16600 | 1.3826 | 1.9519 | 0.5359 |
239
+ | 0.7318 | 44.4149 | 16700 | 1.3815 | 1.9499 | 0.5343 |
240
+ | 0.7318 | 44.6809 | 16800 | 1.3834 | 1.9489 | 0.5295 |
241
+ | 0.7318 | 44.9468 | 16900 | 1.3662 | 1.9468 | 0.5365 |
242
+ | 0.697 | 45.2128 | 17000 | 1.3707 | 1.9487 | 0.5348 |
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+ | 0.697 | 45.4787 | 17100 | 1.3659 | 1.9478 | 0.5358 |
244
+ | 0.697 | 45.7447 | 17200 | 1.3755 | 1.9482 | 0.5380 |
245
+ | 0.697 | 46.0106 | 17300 | 1.3710 | 1.9480 | 0.5373 |
246
+ | 0.697 | 46.2766 | 17400 | 1.3653 | 1.9462 | 0.5317 |
247
+ | 0.6792 | 46.5426 | 17500 | 1.3715 | 1.9478 | 0.5314 |
248
+ | 0.6792 | 46.8085 | 17600 | 1.3711 | 1.9468 | 0.5311 |
249
+ | 0.6792 | 47.0745 | 17700 | 1.3691 | 1.9484 | 0.5368 |
250
+ | 0.6792 | 47.3404 | 17800 | 1.3693 | 1.9480 | 0.5379 |
251
+ | 0.6792 | 47.6064 | 17900 | 1.3689 | 1.9456 | 0.5343 |
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+ | 0.6549 | 47.8723 | 18000 | 1.3651 | 1.9456 | 0.5336 |
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+ | 0.6549 | 48.1383 | 18100 | 1.3628 | 1.9474 | 0.5331 |
254
+ | 0.6549 | 48.4043 | 18200 | 1.3662 | 1.9466 | 0.5346 |
255
+ | 0.6549 | 48.6702 | 18300 | 1.3633 | 1.9464 | 0.5339 |
256
+ | 0.6549 | 48.9362 | 18400 | 1.3643 | 1.9452 | 0.5342 |
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+ | 0.6521 | 49.2021 | 18500 | 1.3636 | 1.9470 | 0.5337 |
258
+ | 0.6521 | 49.4681 | 18600 | 1.3633 | 1.9472 | 0.5335 |
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+ | 0.6521 | 49.7340 | 18700 | 1.3638 | 1.9468 | 0.5340 |
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+ | 0.6521 | 50.0 | 18800 | 1.3636 | 1.9456 | 0.5335 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.0.dev0
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3