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--- |
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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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datasets: |
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- timit_asr |
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metrics: |
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- wer |
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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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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: timit_asr |
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type: timit_asr |
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config: clean |
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split: test |
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args: clean |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.3367100820067535 |
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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 timit_asr dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4634 |
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- Wer: 0.3367 |
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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: 20 |
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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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| 3.6019 | 1.0 | 500 | 2.4586 | 1.0 | |
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| 0.9594 | 2.01 | 1000 | 0.5023 | 0.5122 | |
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| 0.4324 | 3.01 | 1500 | 0.4808 | 0.4703 | |
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| 0.2991 | 4.02 | 2000 | 0.4098 | 0.4208 | |
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| 0.2257 | 5.02 | 2500 | 0.4883 | 0.4264 | |
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| 0.18 | 6.02 | 3000 | 0.4441 | 0.3914 | |
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| 0.1524 | 7.03 | 3500 | 0.4360 | 0.3869 | |
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| 0.1315 | 8.03 | 4000 | 0.4448 | 0.3783 | |
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| 0.1101 | 9.04 | 4500 | 0.4570 | 0.3704 | |
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| 0.1017 | 10.04 | 5000 | 0.4252 | 0.3680 | |
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| 0.0863 | 11.04 | 5500 | 0.4492 | 0.3606 | |
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| 0.0798 | 12.05 | 6000 | 0.4241 | 0.3604 | |
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| 0.0688 | 13.05 | 6500 | 0.4585 | 0.3535 | |
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| 0.0608 | 14.06 | 7000 | 0.4491 | 0.3488 | |
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| 0.0524 | 15.06 | 7500 | 0.4550 | 0.3456 | |
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| 0.0502 | 16.06 | 8000 | 0.4570 | 0.3453 | |
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| 0.0458 | 17.07 | 8500 | 0.4680 | 0.3421 | |
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| 0.0395 | 18.07 | 9000 | 0.4663 | 0.3390 | |
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| 0.0352 | 19.08 | 9500 | 0.4634 | 0.3367 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 1.18.3 |
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- Tokenizers 0.15.2 |
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