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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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model-index: |
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- name: Wav2Vec2_FullDataset |
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results: [] |
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datasets: |
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- timit-asr/timit_asr |
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language: |
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- en |
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metrics: |
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- wer |
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base_model: |
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- facebook/wav2vec2-base |
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pipeline_tag: automatic-speech-recognition |
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library_name: transformers |
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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_FullDataset |
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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: 0.5144 |
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- Wer: 0.3292 |
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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: 0.0001 |
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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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- 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: 1000 |
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- num_epochs: 30 |
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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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| 3.5171 | 1.0 | 500 | 1.8108 | 1.0014 | |
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| 0.8397 | 2.01 | 1000 | 0.5559 | 0.5358 | |
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| 0.431 | 3.01 | 1500 | 0.4265 | 0.4469 | |
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| 0.2931 | 4.02 | 2000 | 0.4034 | 0.4193 | |
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| 0.2247 | 5.02 | 2500 | 0.4595 | 0.4076 | |
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| 0.1855 | 6.02 | 3000 | 0.4543 | 0.3991 | |
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| 0.1497 | 7.03 | 3500 | 0.4894 | 0.3839 | |
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| 0.1339 | 8.03 | 4000 | 0.4514 | 0.3836 | |
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| 0.1166 | 9.04 | 4500 | 0.4432 | 0.3682 | |
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| 0.1063 | 10.04 | 5000 | 0.4781 | 0.3773 | |
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| 0.0923 | 11.04 | 5500 | 0.4548 | 0.3699 | |
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| 0.0899 | 12.05 | 6000 | 0.4836 | 0.3636 | |
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| 0.0802 | 13.05 | 6500 | 0.5117 | 0.3637 | |
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| 0.0726 | 14.06 | 7000 | 0.4453 | 0.3653 | |
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| 0.07 | 15.06 | 7500 | 0.4983 | 0.3581 | |
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| 0.0641 | 16.06 | 8000 | 0.4922 | 0.3603 | |
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| 0.0561 | 17.07 | 8500 | 0.4947 | 0.3517 | |
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| 0.0522 | 18.07 | 9000 | 0.5132 | 0.3513 | |
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| 0.0483 | 19.08 | 9500 | 0.4815 | 0.3453 | |
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| 0.0419 | 20.08 | 10000 | 0.5556 | 0.3459 | |
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| 0.0402 | 21.08 | 10500 | 0.5141 | 0.3428 | |
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| 0.0368 | 22.09 | 11000 | 0.5176 | 0.3437 | |
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| 0.0322 | 23.09 | 11500 | 0.5326 | 0.3403 | |
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| 0.0305 | 24.1 | 12000 | 0.5046 | 0.3366 | |
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| 0.0258 | 25.1 | 12500 | 0.5219 | 0.3315 | |
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| 0.0254 | 26.1 | 13000 | 0.5166 | 0.3289 | |
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| 0.0226 | 27.11 | 13500 | 0.5177 | 0.3311 | |
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| 0.0226 | 28.11 | 14000 | 0.5187 | 0.3302 | |
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| 0.0209 | 29.12 | 14500 | 0.5144 | 0.3292 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 1.18.3 |
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- Tokenizers 0.20.3 |
