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--- |
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tags: |
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- automatic-speech-recognition |
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- timit_asr |
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- generated_from_trainer |
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datasets: |
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- timit_asr |
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model-index: |
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- name: wav2vec2-random |
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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-random |
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This model is a fine-tuned version of [patrickvonplaten/wav2vec2-base-random](https://huggingface.co/patrickvonplaten/wav2vec2-base-random) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1593 |
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- Wer: 0.8364 |
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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: 32 |
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- eval_batch_size: 1 |
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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: 20.0 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 2.9043 | 0.69 | 100 | 2.9683 | 1.0 | |
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| 2.8537 | 1.38 | 200 | 2.9281 | 0.9997 | |
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| 2.7803 | 2.07 | 300 | 2.7330 | 0.9999 | |
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| 2.6806 | 2.76 | 400 | 2.5792 | 1.0 | |
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| 2.4136 | 3.45 | 500 | 2.4327 | 0.9948 | |
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| 2.1682 | 4.14 | 600 | 2.3508 | 0.9877 | |
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| 2.2577 | 4.83 | 700 | 2.2176 | 0.9773 | |
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| 2.355 | 5.52 | 800 | 2.1753 | 0.9542 | |
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| 1.8588 | 6.21 | 900 | 2.0650 | 0.8851 | |
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| 1.6831 | 6.9 | 1000 | 2.0109 | 0.8618 | |
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| 1.888 | 7.59 | 1100 | 1.9660 | 0.8418 | |
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| 2.0066 | 8.28 | 1200 | 1.9847 | 0.8531 | |
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| 1.7044 | 8.97 | 1300 | 1.9760 | 0.8527 | |
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| 1.3168 | 9.66 | 1400 | 2.0708 | 0.8327 | |
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| 1.2143 | 10.34 | 1500 | 2.0601 | 0.8419 | |
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| 1.6189 | 11.03 | 1600 | 2.0960 | 0.8299 | |
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| 1.13 | 11.72 | 1700 | 2.2540 | 0.8408 | |
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| 0.8001 | 12.41 | 1800 | 2.4260 | 0.8306 | |
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| 0.7769 | 13.1 | 1900 | 2.4182 | 0.8445 | |
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| 1.2165 | 13.79 | 2000 | 2.3666 | 0.8284 | |
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| 0.8026 | 14.48 | 2100 | 2.7118 | 0.8662 | |
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| 0.5148 | 15.17 | 2200 | 2.7957 | 0.8526 | |
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| 0.4921 | 15.86 | 2300 | 2.8244 | 0.8346 | |
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| 0.7629 | 16.55 | 2400 | 2.8944 | 0.8370 | |
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| 0.5762 | 17.24 | 2500 | 3.0335 | 0.8367 | |
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| 0.4076 | 17.93 | 2600 | 3.0776 | 0.8358 | |
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| 0.3395 | 18.62 | 2700 | 3.1572 | 0.8261 | |
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| 0.4862 | 19.31 | 2800 | 3.1319 | 0.8414 | |
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| 0.5061 | 20.0 | 2900 | 3.1593 | 0.8364 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.8.1 |
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- Datasets 1.14.1.dev0 |
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- Tokenizers 0.10.3 |
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