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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: 1.1050 |
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- Wer: 0.6656 |
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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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| 6.0506 | 2.3 | 200 | 0.8841 | 0.7564 | |
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| 0.6354 | 4.59 | 400 | 0.7448 | 0.6912 | |
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| 0.3934 | 6.89 | 600 | 0.8321 | 0.6929 | |
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| 0.2652 | 9.19 | 800 | 0.9529 | 0.6984 | |
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| 0.2022 | 11.49 | 1000 | 0.9490 | 0.6979 | |
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| 0.1514 | 13.79 | 1200 | 1.0025 | 0.6869 | |
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| 0.124 | 16.09 | 1400 | 1.0367 | 0.6799 | |
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| 0.1007 | 18.39 | 1600 | 1.0658 | 0.6734 | |
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| 0.0875 | 20.69 | 1800 | 1.0758 | 0.6779 | |
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| 0.0838 | 22.98 | 2000 | 1.0999 | 0.6701 | |
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| 0.0745 | 25.29 | 2200 | 1.1020 | 0.6708 | |
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| 0.0641 | 27.58 | 2400 | 1.1140 | 0.6683 | |
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| 0.0607 | 29.88 | 2600 | 1.1050 | 0.6656 | |
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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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