pytorch tensorboard wav2vec2 automatic-speech-recognition transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-20sec-timit-and-dementiabank
  results: []
---

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# wav2vec2-base-20sec-timit-and-dementiabank

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4338
- Wer: 0.2313

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6839        | 2.53  | 500  | 2.7287          | 1.0    |
| 0.8708        | 5.05  | 1000 | 0.5004          | 0.3490 |
| 0.2879        | 7.58  | 1500 | 0.4411          | 0.2872 |
| 0.1877        | 10.1  | 2000 | 0.4359          | 0.2594 |
| 0.1617        | 12.63 | 2500 | 0.4404          | 0.2492 |
| 0.1295        | 15.15 | 3000 | 0.4356          | 0.2418 |
| 0.1146        | 17.68 | 3500 | 0.4338          | 0.2313 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
