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--- |
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license: apache-2.0 |
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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-xls-r-300m-telugu-asr |
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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-xls-r-300m-telugu-asr |
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This model is a fine-tuned version of [henilp105/wav2vec2-large-xls-r-300m-telugu-asr](https://huggingface.co/henilp105/wav2vec2-large-xls-r-300m-telugu-asr) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3902 |
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- Wer: 0.7443 |
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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: 8 |
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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: 16 |
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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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| 6.9225 | 2.3 | 200 | 3.3972 | 1.0 | |
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| 1.4526 | 4.59 | 400 | 1.0196 | 0.7959 | |
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| 0.5384 | 6.89 | 600 | 1.0260 | 0.7790 | |
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| 0.3483 | 9.19 | 800 | 1.0932 | 0.7740 | |
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| 0.2428 | 11.49 | 1000 | 1.2085 | 0.7747 | |
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| 0.1839 | 13.79 | 1200 | 1.2716 | 0.7750 | |
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| 0.147 | 16.09 | 1400 | 1.2895 | 0.7665 | |
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| 0.1238 | 18.39 | 1600 | 1.2995 | 0.7585 | |
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| 0.1046 | 20.69 | 1800 | 1.3891 | 0.7550 | |
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| 0.0946 | 22.98 | 2000 | 1.3820 | 0.7603 | |
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| 0.0856 | 25.29 | 2200 | 1.3909 | 0.7438 | |
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| 0.0753 | 27.58 | 2400 | 1.3841 | 0.7431 | |
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| 0.075 | 29.88 | 2600 | 1.3902 | 0.7443 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.13.2 |
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