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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/wav2vec2-xls-r-300m
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_17_0
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metrics:
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- wer
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model-index:
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- name: result_data-4
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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_17_0
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type: common_voice_17_0
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config: uk
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split: test
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args: uk
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metrics:
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- name: Wer
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type: wer
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value: 0.4002264364562695
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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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# result_data-4
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2483
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- Wer: 0.4002
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- Cer: 0.1791
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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: 4.355619094803853e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- total_train_batch_size: 32
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- total_eval_batch_size: 32
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 102
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- num_epochs: 7.0
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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 | Cer |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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| 0.9556 | 0.9099 | 1000 | 0.7514 | 0.7659 | 0.2976 |
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| 0.4941 | 1.8198 | 2000 | 0.3987 | 0.5471 | 0.2174 |
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| 0.3694 | 2.7298 | 3000 | 0.3282 | 0.4874 | 0.2005 |
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| 0.3199 | 3.6397 | 4000 | 0.2846 | 0.4506 | 0.1901 |
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| 0.2805 | 4.5496 | 5000 | 0.2716 | 0.4254 | 0.1855 |
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| 0.2572 | 5.4595 | 6000 | 0.2622 | 0.4084 | 0.1810 |
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| 0.2389 | 6.3694 | 7000 | 0.2483 | 0.4002 | 0.1791 |
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### Framework versions
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- Transformers 4.49.0
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- Pytorch 2.6.0+cu124
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- Datasets 3.3.2
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- Tokenizers 0.21.0
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