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--- |
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tags: |
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- automatic-speech-recognition |
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- librispeech_asr |
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- generated_from_trainer |
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- wavlm_libri_finetune |
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model-index: |
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- name: wavlm-libri-clean-100h-base |
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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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# wavlm-libri-clean-100h-base |
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This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the LIBRISPEECH_ASR - CLEAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0829 |
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- Wer: 0.0675 |
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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: 0.0003 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 32 |
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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: 500 |
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- num_epochs: 3.0 |
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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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| 2.8805 | 0.34 | 300 | 2.8686 | 1.0 | |
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| 0.2459 | 0.67 | 600 | 0.1858 | 0.1554 | |
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| 0.1114 | 1.01 | 900 | 0.1379 | 0.1191 | |
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| 0.0867 | 1.35 | 1200 | 0.1130 | 0.0961 | |
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| 0.0698 | 1.68 | 1500 | 0.1032 | 0.0877 | |
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| 0.0663 | 2.02 | 1800 | 0.0959 | 0.0785 | |
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| 0.0451 | 2.35 | 2100 | 0.0887 | 0.0748 | |
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| 0.0392 | 2.69 | 2400 | 0.0859 | 0.0698 | |
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### Framework versions |
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- Transformers 4.15.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.16.2.dev0 |
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- Tokenizers 0.10.3 |
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