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: wav2vec_trained
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec_trained

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: 0.0337
- Wer: 0.1042

## 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: 8
- 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3849        | 2.21  | 500  | 2.9148          | 1.0    |
| 1.9118        | 4.42  | 1000 | 0.9627          | 0.5833 |
| 0.7596        | 6.64  | 1500 | 0.8953          | 0.3542 |
| 0.4602        | 8.85  | 2000 | 0.3325          | 0.2083 |
| 0.331         | 11.06 | 2500 | 0.3084          | 0.2083 |
| 0.2474        | 13.27 | 3000 | 0.0960          | 0.1667 |
| 0.1934        | 15.49 | 3500 | 0.1276          | 0.125  |
| 0.156         | 17.7  | 4000 | 0.0605          | 0.0833 |
| 0.1244        | 19.91 | 4500 | 0.0831          | 0.1458 |
| 0.1006        | 22.12 | 5000 | 0.0560          | 0.125  |
| 0.0827        | 24.34 | 5500 | 0.0395          | 0.0833 |
| 0.0723        | 26.55 | 6000 | 0.0573          | 0.0833 |
| 0.0606        | 28.76 | 6500 | 0.0337          | 0.1042 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
