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--- |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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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: w2v-bert-2.0-sr |
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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: sr |
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split: test |
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args: sr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.05344857999647204 |
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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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# w2v-bert-2.0-sr |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) 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: 0.1469 |
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- Wer: 0.0534 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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: 20 |
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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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| 2.1994 | 1.89 | 300 | 0.1350 | 0.1078 | |
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| 0.2331 | 3.77 | 600 | 0.2306 | 0.1341 | |
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| 0.1879 | 5.66 | 900 | 0.1354 | 0.0766 | |
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| 0.1579 | 7.54 | 1200 | 0.1646 | 0.0958 | |
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| 0.1293 | 9.43 | 1500 | 0.1207 | 0.0713 | |
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| 0.1182 | 11.31 | 1800 | 0.1376 | 0.0737 | |
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| 0.1061 | 13.2 | 2100 | 0.1244 | 0.0580 | |
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| 0.1011 | 15.08 | 2400 | 0.1390 | 0.0602 | |
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| 0.0933 | 16.97 | 2700 | 0.1313 | 0.0524 | |
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| 0.0948 | 18.85 | 3000 | 0.1469 | 0.0534 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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