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README.md
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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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datasets:
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- common_voice
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model-index:
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- name: wav2vec2_common_voice_accents_5
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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_common_voice_accents_5
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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 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0027
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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: 48
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- total_train_batch_size: 384
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- total_eval_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 |
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|:-------------:|:-----:|:----:|:---------------:|
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| 4.4163 | 1.34 | 400 | 0.5520 |
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| 0.3305 | 2.68 | 800 | 0.1698 |
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| 0.2138 | 4.03 | 1200 | 0.1104 |
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| 0.1714 | 5.37 | 1600 | 0.0944 |
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| 0.1546 | 6.71 | 2000 | 0.0700 |
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| 0.1434 | 8.05 | 2400 | 0.0610 |
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| 0.1272 | 9.4 | 2800 | 0.0493 |
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| 0.1183 | 10.74 | 3200 | 0.0371 |
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| 0.1113 | 12.08 | 3600 | 0.0468 |
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| 0.1013 | 13.42 | 4000 | 0.0336 |
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| 0.0923 | 14.77 | 4400 | 0.0282 |
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| 0.0854 | 16.11 | 4800 | 0.0410 |
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| 0.0791 | 17.45 | 5200 | 0.0252 |
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| 0.0713 | 18.79 | 5600 | 0.0128 |
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| 0.0662 | 20.13 | 6000 | 0.0252 |
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| 0.0635 | 21.48 | 6400 | 0.0080 |
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| 0.0607 | 22.82 | 6800 | 0.0098 |
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| 0.0557 | 24.16 | 7200 | 0.0069 |
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| 0.0511 | 25.5 | 7600 | 0.0057 |
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| 0.0474 | 26.85 | 8000 | 0.0046 |
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| 0.045 | 28.19 | 8400 | 0.0037 |
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| 0.0426 | 29.53 | 8800 | 0.0027 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.10.2+cu102
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- Datasets 1.18.4
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- Tokenizers 0.11.6
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