---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-base-pretrained_lr5e-5_at1_da1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# w2v2-base-pretrained_lr5e-5_at1_da1

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: 2.2991
- Wer: 0.3016

## 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: 5e-05
- train_batch_size: 32
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 17.3296       | 6.1   | 250  | 8.4283          | 1.0    |
| 4.6394        | 12.2  | 500  | 7.5283          | 1.0    |
| 3.4235        | 18.29 | 750  | 5.8716          | 1.0    |
| 3.3011        | 24.39 | 1000 | 4.9842          | 1.0    |
| 3.048         | 30.49 | 1250 | 3.1389          | 1.0077 |
| 2.1291        | 36.59 | 1500 | 2.0209          | 0.9257 |
| 0.5553        | 42.68 | 1750 | 1.5785          | 0.4742 |
| 0.2117        | 48.78 | 2000 | 1.7978          | 0.3631 |
| 0.1228        | 54.88 | 2250 | 1.8723          | 0.3323 |
| 0.0771        | 60.98 | 2500 | 2.0055          | 0.3234 |
| 0.0587        | 67.07 | 2750 | 2.0493          | 0.3157 |
| 0.0398        | 73.17 | 3000 | 2.0708          | 0.3084 |
| 0.0336        | 79.27 | 3250 | 2.2532          | 0.3148 |
| 0.0271        | 85.37 | 3500 | 2.2694          | 0.3016 |
| 0.0234        | 91.46 | 3750 | 2.3538          | 0.3050 |
| 0.0213        | 97.56 | 4000 | 2.2991          | 0.3016 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.14.6
- Tokenizers 0.14.1