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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: facebook/wav2vec2-large-xlsr-53 |
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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: wav2vec2-large-xlsr-53-common_voice-ja-demo-kana-only |
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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: 0.9997984277363435 |
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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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# wav2vec2-large-xlsr-53-common_voice-ja-demo-kana-only |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) 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: 0.6984 |
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- Wer: 0.9998 |
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- Cer: 0.3127 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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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: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 15.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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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 | 6.8309 | 1.0 | 0.9999 | |
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| No log | 0.5319 | 200 | 4.1299 | 1.0 | 0.9999 | |
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| No log | 0.7979 | 300 | 3.9930 | 1.0 | 0.9869 | |
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| No log | 1.0638 | 400 | 2.0400 | 1.0 | 0.5876 | |
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| 7.1041 | 1.3298 | 500 | 1.0610 | 1.0 | 0.4309 | |
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| 7.1041 | 1.5957 | 600 | 0.8837 | 1.0 | 0.3955 | |
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| 7.1041 | 1.8617 | 700 | 0.7706 | 0.9998 | 0.3791 | |
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| 7.1041 | 2.1277 | 800 | 0.7662 | 1.0 | 0.3816 | |
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| 7.1041 | 2.3936 | 900 | 0.7621 | 1.0 | 0.3790 | |
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| 0.803 | 2.6596 | 1000 | 0.6969 | 1.0 | 0.3626 | |
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| 0.803 | 2.9255 | 1100 | 0.6736 | 1.0 | 0.3573 | |
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| 0.803 | 3.1915 | 1200 | 0.6823 | 0.9998 | 0.3544 | |
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| 0.803 | 3.4574 | 1300 | 0.6360 | 1.0 | 0.3460 | |
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| 0.803 | 3.7234 | 1400 | 0.6504 | 1.0 | 0.3443 | |
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| 0.5675 | 3.9894 | 1500 | 0.6247 | 1.0 | 0.3414 | |
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| 0.5675 | 4.2553 | 1600 | 0.6397 | 0.9998 | 0.3425 | |
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| 0.5675 | 4.5213 | 1700 | 0.6589 | 1.0 | 0.3439 | |
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| 0.5675 | 4.7872 | 1800 | 0.6345 | 1.0 | 0.3449 | |
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| 0.5675 | 5.0532 | 1900 | 0.6522 | 0.9996 | 0.3380 | |
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| 0.4421 | 5.3191 | 2000 | 0.6293 | 1.0 | 0.3372 | |
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| 0.4421 | 5.5851 | 2100 | 0.6096 | 1.0 | 0.3342 | |
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| 0.4421 | 5.8511 | 2200 | 0.6108 | 1.0 | 0.3321 | |
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| 0.4421 | 6.1170 | 2300 | 0.6200 | 1.0 | 0.3354 | |
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| 0.4421 | 6.3830 | 2400 | 0.6413 | 1.0 | 0.3341 | |
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| 0.3699 | 6.6489 | 2500 | 0.6303 | 0.9996 | 0.3359 | |
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| 0.3699 | 6.9149 | 2600 | 0.6013 | 1.0 | 0.3308 | |
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| 0.3699 | 7.1809 | 2700 | 0.6343 | 1.0 | 0.3286 | |
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| 0.3699 | 7.4468 | 2800 | 0.6208 | 0.9998 | 0.3260 | |
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| 0.3699 | 7.7128 | 2900 | 0.6095 | 0.9998 | 0.3287 | |
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| 0.3146 | 7.9787 | 3000 | 0.6058 | 0.9996 | 0.3266 | |
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| 0.3146 | 8.2447 | 3100 | 0.6613 | 0.9996 | 0.3251 | |
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| 0.3146 | 8.5106 | 3200 | 0.6539 | 1.0 | 0.3244 | |
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| 0.3146 | 8.7766 | 3300 | 0.6331 | 1.0 | 0.3264 | |
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| 0.3146 | 9.0426 | 3400 | 0.6436 | 1.0 | 0.3228 | |
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| 0.2576 | 9.3085 | 3500 | 0.6329 | 1.0 | 0.3235 | |
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| 0.2576 | 9.5745 | 3600 | 0.6315 | 0.9998 | 0.3197 | |
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| 0.2576 | 9.8404 | 3700 | 0.6281 | 0.9998 | 0.3203 | |
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| 0.2576 | 10.1064 | 3800 | 0.6696 | 0.9996 | 0.3196 | |
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| 0.2576 | 10.3723 | 3900 | 0.6630 | 0.9996 | 0.3199 | |
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| 0.2201 | 10.6383 | 4000 | 0.6781 | 1.0 | 0.3203 | |
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| 0.2201 | 10.9043 | 4100 | 0.6531 | 1.0 | 0.3196 | |
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| 0.2201 | 11.1702 | 4200 | 0.6763 | 0.9998 | 0.3193 | |
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| 0.2201 | 11.4362 | 4300 | 0.6785 | 1.0 | 0.3184 | |
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| 0.2201 | 11.7021 | 4400 | 0.6664 | 0.9998 | 0.3179 | |
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| 0.1931 | 11.9681 | 4500 | 0.6682 | 0.9998 | 0.3184 | |
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| 0.1931 | 12.2340 | 4600 | 0.6800 | 0.9998 | 0.3168 | |
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| 0.1931 | 12.5 | 4700 | 0.6925 | 1.0 | 0.3162 | |
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| 0.1931 | 12.7660 | 4800 | 0.7047 | 1.0 | 0.3145 | |
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| 0.1931 | 13.0319 | 4900 | 0.6919 | 0.9998 | 0.3147 | |
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| 0.1694 | 13.2979 | 5000 | 0.6999 | 0.9998 | 0.3142 | |
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| 0.1694 | 13.5638 | 5100 | 0.6995 | 1.0 | 0.3134 | |
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| 0.1694 | 13.8298 | 5200 | 0.6917 | 0.9998 | 0.3134 | |
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| 0.1694 | 14.0957 | 5300 | 0.6963 | 0.9998 | 0.3129 | |
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| 0.1694 | 14.3617 | 5400 | 0.6961 | 0.9998 | 0.3128 | |
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| 0.1548 | 14.6277 | 5500 | 0.6964 | 1.0 | 0.3129 | |
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| 0.1548 | 14.8936 | 5600 | 0.6984 | 0.9998 | 0.3127 | |
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### Framework versions |
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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 |
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