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--- |
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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 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xlsr53-zh-cn-subset-colab |
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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 |
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type: common_voice |
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config: zh-CN |
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split: test[:20%] |
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args: zh-CN |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.9394977168949772 |
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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-xlsr53-zh-cn-subset-colab |
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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 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3992 |
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- Wer: 0.9395 |
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- Cer: 0.3184 |
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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: 5e-05 |
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- train_batch_size: 13 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 26 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 100 |
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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 | 1.9 | 400 | 33.6533 | 1.0 | 1.0 | |
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| 70.5767 | 3.81 | 800 | 6.8140 | 1.0 | 1.0 | |
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| 7.1379 | 5.71 | 1200 | 6.5163 | 1.0 | 1.0 | |
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| 6.4771 | 7.62 | 1600 | 6.4602 | 1.0 | 1.0 | |
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| 6.3627 | 9.52 | 2000 | 6.3406 | 1.0 | 0.9700 | |
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| 6.3627 | 11.43 | 2400 | 6.1021 | 1.0 | 0.9678 | |
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| 6.1201 | 13.33 | 2800 | 5.1523 | 1.0 | 0.8385 | |
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| 5.3531 | 15.24 | 3200 | 4.2224 | 1.0 | 0.7084 | |
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| 4.1733 | 17.14 | 3600 | 3.6981 | 1.0 | 0.6332 | |
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| 3.5472 | 19.05 | 4000 | 3.2708 | 0.9994 | 0.5827 | |
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| 3.5472 | 20.95 | 4400 | 2.9629 | 0.9989 | 0.5510 | |
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| 3.0668 | 22.86 | 4800 | 2.7122 | 0.9943 | 0.5165 | |
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| 2.7248 | 24.76 | 5200 | 2.5171 | 0.9914 | 0.4976 | |
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| 2.4609 | 26.67 | 5600 | 2.3538 | 0.9897 | 0.4759 | |
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| 2.2323 | 28.57 | 6000 | 2.2112 | 0.9874 | 0.4555 | |
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| 2.2323 | 30.48 | 6400 | 2.0850 | 0.9834 | 0.4370 | |
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| 2.0438 | 32.38 | 6800 | 1.9982 | 0.9806 | 0.4261 | |
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| 1.8837 | 34.29 | 7200 | 1.9179 | 0.9766 | 0.4137 | |
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| 1.7646 | 36.19 | 7600 | 1.8278 | 0.9766 | 0.4030 | |
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| 1.6469 | 38.1 | 8000 | 1.7627 | 0.9755 | 0.3937 | |
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| 1.6469 | 40.0 | 8400 | 1.7063 | 0.9709 | 0.3853 | |
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| 1.5422 | 41.9 | 8800 | 1.6649 | 0.9663 | 0.3787 | |
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| 1.4561 | 43.81 | 9200 | 1.6336 | 0.9697 | 0.3714 | |
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| 1.3842 | 45.71 | 9600 | 1.5943 | 0.9606 | 0.3647 | |
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| 1.3164 | 47.62 | 10000 | 1.5681 | 0.9669 | 0.3621 | |
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| 1.3164 | 49.52 | 10400 | 1.5535 | 0.9600 | 0.3582 | |
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| 1.2654 | 51.43 | 10800 | 1.5354 | 0.9538 | 0.3544 | |
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| 1.2186 | 53.33 | 11200 | 1.5003 | 0.9555 | 0.3482 | |
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| 1.1781 | 55.24 | 11600 | 1.4979 | 0.9572 | 0.3473 | |
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| 1.1344 | 57.14 | 12000 | 1.4820 | 0.9549 | 0.3453 | |
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| 1.1344 | 59.05 | 12400 | 1.4707 | 0.9509 | 0.3396 | |
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| 1.0965 | 60.95 | 12800 | 1.4657 | 0.9509 | 0.3384 | |
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| 1.0637 | 62.86 | 13200 | 1.4610 | 0.9509 | 0.3371 | |
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| 1.0306 | 64.76 | 13600 | 1.4461 | 0.9509 | 0.3361 | |
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| 1.0014 | 66.67 | 14000 | 1.4437 | 0.9503 | 0.3328 | |
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| 1.0014 | 68.57 | 14400 | 1.4334 | 0.9463 | 0.3304 | |
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| 0.9758 | 70.48 | 14800 | 1.4267 | 0.9429 | 0.3295 | |
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| 0.9486 | 72.38 | 15200 | 1.4250 | 0.9469 | 0.3269 | |
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| 0.933 | 74.29 | 15600 | 1.4214 | 0.9441 | 0.3273 | |
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| 0.9121 | 76.19 | 16000 | 1.4161 | 0.9441 | 0.3267 | |
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| 0.9121 | 78.1 | 16400 | 1.4137 | 0.9446 | 0.3268 | |
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| 0.9001 | 80.0 | 16800 | 1.4216 | 0.9446 | 0.3253 | |
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| 0.8789 | 81.9 | 17200 | 1.4164 | 0.9435 | 0.3264 | |
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| 0.8659 | 83.81 | 17600 | 1.3996 | 0.9424 | 0.3216 | |
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| 0.8471 | 85.71 | 18000 | 1.4079 | 0.9458 | 0.3226 | |
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| 0.8471 | 87.62 | 18400 | 1.4042 | 0.9412 | 0.3214 | |
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| 0.8387 | 89.52 | 18800 | 1.4073 | 0.9424 | 0.3214 | |
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| 0.8299 | 91.43 | 19200 | 1.4005 | 0.9418 | 0.3192 | |
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| 0.8257 | 93.33 | 19600 | 1.4040 | 0.9406 | 0.3200 | |
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| 0.813 | 95.24 | 20000 | 1.4012 | 0.9412 | 0.3184 | |
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| 0.813 | 97.14 | 20400 | 1.4011 | 0.9389 | 0.3183 | |
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| 0.8062 | 99.05 | 20800 | 1.3992 | 0.9395 | 0.3184 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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