| | ---
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| | license: apache-2.0
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| | base_model: facebook/wav2vec2-base
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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-tokenizer
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| | results: []
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| | ---
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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
|
| | should probably proofread and complete it, then remove this comment. -->
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| |
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| | # wav2vec2-tokenizer
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| |
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| | This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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| | It achieves the following results on the evaluation set:
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| | - Loss: 0.0005
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| | - Wer: 0.2412
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| |
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| | ## Model description
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| |
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| | More information needed
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| |
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| | ## Intended uses & limitations
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| |
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| | More information needed
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| |
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| | ## Training and evaluation data
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| |
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| | More information needed
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| |
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| | ## Training procedure
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| |
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| | ### Training hyperparameters
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| |
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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: 32
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| | - eval_batch_size: 8
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| | - seed: 42
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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: 100
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| | - num_epochs: 100
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| | - mixed_precision_training: Native AMP
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| |
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| | ### Training results
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| |
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| | | Training Loss | Epoch | Step | Validation Loss | Wer |
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| | |:-------------:|:-----:|:----:|:---------------:|:------:|
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| | | 2.8291 | 4.0 | 100 | 1.7138 | 0.9862 |
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| | | 1.2768 | 8.0 | 200 | 0.7349 | 0.7488 |
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| | | 0.53 | 12.0 | 300 | 0.2418 | 0.705 |
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| | | 0.2342 | 16.0 | 400 | 0.1818 | 0.7362 |
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| | | 0.1375 | 20.0 | 500 | 0.1053 | 0.73 |
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| | | 0.1286 | 24.0 | 600 | 0.0886 | 0.7063 |
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| | | 0.0978 | 28.0 | 700 | 0.0634 | 0.74 |
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| | | 0.0952 | 32.0 | 800 | 0.0642 | 0.6963 |
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| | | 0.088 | 36.0 | 900 | 0.0674 | 0.7025 |
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| | | 0.0802 | 40.0 | 1000 | 0.0140 | 0.2587 |
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| | | 0.0624 | 44.0 | 1100 | 0.0185 | 0.1862 |
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| | | 0.029 | 48.0 | 1200 | 0.0234 | 0.2725 |
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| | | 0.0176 | 52.0 | 1300 | 0.0072 | 0.2275 |
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| | | 0.016 | 56.0 | 1400 | 0.0036 | 0.265 |
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| | | 0.0047 | 60.0 | 1500 | 0.0019 | 0.235 |
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| | | 0.0066 | 64.0 | 1600 | 0.0014 | 0.2075 |
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| | | 0.0041 | 68.0 | 1700 | 0.0009 | 0.2712 |
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| | | 0.0019 | 72.0 | 1800 | 0.0008 | 0.2863 |
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| | | 0.002 | 76.0 | 1900 | 0.0007 | 0.2888 |
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| | | 0.0031 | 80.0 | 2000 | 0.0006 | 0.2863 |
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| | | 0.0032 | 84.0 | 2100 | 0.0006 | 0.2762 |
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| | | 0.0026 | 88.0 | 2200 | 0.0005 | 0.2325 |
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| | | 0.0019 | 92.0 | 2300 | 0.0005 | 0.2362 |
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| | | 0.0046 | 96.0 | 2400 | 0.0005 | 0.2412 |
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| | | 0.0018 | 100.0 | 2500 | 0.0005 | 0.2412 |
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| |
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| |
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| | ### Framework versions
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| |
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| | - Transformers 4.39.3
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| | - Pytorch 2.2.2+cu121
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| | - Datasets 2.14.5
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| | - Tokenizers 0.15.2
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| | |
