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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-base-ls960 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_16_1 |
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metrics: |
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- wer |
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model-index: |
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- name: hubert-base-common-voice-vi-demo |
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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_16_1 |
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type: common_voice_16_1 |
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config: vi |
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split: None |
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args: vi |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.3678324522163481 |
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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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# hubert-base-common-voice-vi-demo |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the common_voice_16_1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5121 |
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- Wer: 0.3678 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 1000 |
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- num_epochs: 30 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 8.8731 | 1.14 | 500 | 3.5477 | 1.0 | |
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| 3.3329 | 2.28 | 1000 | 2.1928 | 1.0171 | |
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| 1.4603 | 3.42 | 1500 | 0.9074 | 0.6542 | |
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| 0.9413 | 4.57 | 2000 | 0.7490 | 0.5568 | |
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| 0.7664 | 5.71 | 2500 | 0.6418 | 0.5052 | |
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| 0.6719 | 6.85 | 3000 | 0.6240 | 0.4819 | |
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| 0.6261 | 7.99 | 3500 | 0.6048 | 0.4657 | |
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| 0.5771 | 9.13 | 4000 | 0.5555 | 0.4512 | |
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| 0.525 | 10.27 | 4500 | 0.5475 | 0.4392 | |
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| 0.4948 | 11.42 | 5000 | 0.5619 | 0.4261 | |
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| 0.4585 | 12.56 | 5500 | 0.5646 | 0.4280 | |
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| 0.4584 | 13.7 | 6000 | 0.5326 | 0.4168 | |
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| 0.4157 | 14.84 | 6500 | 0.5126 | 0.4038 | |
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| 0.4113 | 15.98 | 7000 | 0.5282 | 0.4004 | |
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| 0.3955 | 17.12 | 7500 | 0.5310 | 0.3959 | |
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| 0.3658 | 18.26 | 8000 | 0.4936 | 0.3886 | |
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| 0.3584 | 19.41 | 8500 | 0.5438 | 0.3895 | |
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| 0.3536 | 20.55 | 9000 | 0.5167 | 0.3860 | |
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| 0.3665 | 21.69 | 9500 | 0.5194 | 0.3842 | |
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| 0.3231 | 22.83 | 10000 | 0.5269 | 0.3866 | |
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| 0.315 | 23.97 | 10500 | 0.5219 | 0.3768 | |
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| 0.3191 | 25.11 | 11000 | 0.5054 | 0.3728 | |
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| 0.3264 | 26.26 | 11500 | 0.5068 | 0.3710 | |
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| 0.3014 | 27.4 | 12000 | 0.5009 | 0.3694 | |
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| 0.3055 | 28.54 | 12500 | 0.5066 | 0.3676 | |
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| 0.3098 | 29.68 | 13000 | 0.5121 | 0.3678 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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