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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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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xlsr-hindi |
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results: [] |
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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-hindi |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0220 |
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- Wer: 0.5697 |
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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: 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: 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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| 5.6122 | 1.81 | 400 | 3.3749 | 1.0 | |
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| 1.6592 | 3.61 | 800 | 1.0003 | 0.7554 | |
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| 0.7745 | 5.42 | 1200 | 0.9482 | 0.6972 | |
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| 0.6286 | 7.22 | 1600 | 1.0754 | 0.6750 | |
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| 0.5413 | 9.03 | 2000 | 0.9040 | 0.6405 | |
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| 0.4833 | 10.84 | 2400 | 0.9086 | 0.6116 | |
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| 0.4331 | 12.64 | 2800 | 0.9273 | 0.6283 | |
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| 0.4047 | 14.45 | 3200 | 1.0076 | 0.6138 | |
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| 0.3739 | 16.25 | 3600 | 0.9818 | 0.6018 | |
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| 0.3445 | 18.06 | 4000 | 0.9948 | 0.5952 | |
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| 0.3305 | 19.86 | 4400 | 0.9897 | 0.5834 | |
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| 0.3107 | 21.67 | 4800 | 1.0022 | 0.5751 | |
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| 0.2879 | 23.48 | 5200 | 1.0235 | 0.5744 | |
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| 0.2836 | 25.28 | 5600 | 1.0238 | 0.5765 | |
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| 0.2706 | 27.09 | 6000 | 1.0276 | 0.5694 | |
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| 0.2656 | 28.89 | 6400 | 1.0220 | 0.5697 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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