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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-base-timit-demo-google-colab |
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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-base-timit-demo-google-colab |
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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: 0.5112 |
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- Wer: 0.9988 |
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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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- 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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| 3.5557 | 1.0 | 500 | 1.6786 | 1.0 | |
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| 0.8407 | 2.01 | 1000 | 0.5356 | 0.9988 | |
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| 0.4297 | 3.01 | 1500 | 0.4431 | 0.9988 | |
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| 0.2989 | 4.02 | 2000 | 0.4191 | 0.9988 | |
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| 0.2338 | 5.02 | 2500 | 0.4251 | 0.9988 | |
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| 0.1993 | 6.02 | 3000 | 0.4618 | 0.9988 | |
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| 0.1585 | 7.03 | 3500 | 0.4577 | 0.9988 | |
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| 0.1386 | 8.03 | 4000 | 0.4099 | 0.9982 | |
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| 0.1234 | 9.04 | 4500 | 0.4945 | 0.9988 | |
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| 0.1162 | 10.04 | 5000 | 0.4597 | 0.9988 | |
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| 0.1008 | 11.04 | 5500 | 0.4563 | 0.9988 | |
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| 0.0894 | 12.05 | 6000 | 0.5157 | 0.9988 | |
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| 0.083 | 13.05 | 6500 | 0.5027 | 0.9988 | |
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| 0.0735 | 14.06 | 7000 | 0.4905 | 0.9994 | |
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| 0.0686 | 15.06 | 7500 | 0.4552 | 0.9988 | |
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| 0.0632 | 16.06 | 8000 | 0.5522 | 0.9988 | |
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| 0.061 | 17.07 | 8500 | 0.4874 | 0.9988 | |
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| 0.0626 | 18.07 | 9000 | 0.5243 | 0.9988 | |
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| 0.0475 | 19.08 | 9500 | 0.4798 | 0.9988 | |
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| 0.0447 | 20.08 | 10000 | 0.5250 | 0.9988 | |
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| 0.0432 | 21.08 | 10500 | 0.5195 | 0.9988 | |
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| 0.0358 | 22.09 | 11000 | 0.5008 | 0.9988 | |
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| 0.0319 | 23.09 | 11500 | 0.5376 | 0.9988 | |
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| 0.0334 | 24.1 | 12000 | 0.5149 | 0.9988 | |
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| 0.0269 | 25.1 | 12500 | 0.4911 | 0.9988 | |
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| 0.0275 | 26.1 | 13000 | 0.4907 | 0.9988 | |
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| 0.027 | 27.11 | 13500 | 0.4992 | 0.9988 | |
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| 0.0239 | 28.11 | 14000 | 0.5021 | 0.9988 | |
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| 0.0233 | 29.12 | 14500 | 0.5112 | 0.9988 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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