---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
  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. -->

# wav2vec2-base-timit-demo-google-colab

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: 0.5313
- Wer: 0.3317

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5823        | 1.0   | 500   | 1.8501          | 1.0236 |
| 0.8931        | 2.01  | 1000  | 0.5018          | 0.5196 |
| 0.4269        | 3.01  | 1500  | 0.4266          | 0.4461 |
| 0.2876        | 4.02  | 2000  | 0.4458          | 0.4359 |
| 0.2272        | 5.02  | 2500  | 0.4183          | 0.4146 |
| 0.1813        | 6.02  | 3000  | 0.4151          | 0.3945 |
| 0.1555        | 7.03  | 3500  | 0.4216          | 0.3881 |
| 0.1353        | 8.03  | 4000  | 0.4282          | 0.3824 |
| 0.1221        | 9.04  | 4500  | 0.4848          | 0.3845 |
| 0.1135        | 10.04 | 5000  | 0.5003          | 0.3818 |
| 0.0968        | 11.04 | 5500  | 0.5331          | 0.3738 |
| 0.09          | 12.05 | 6000  | 0.5082          | 0.3690 |
| 0.084         | 13.05 | 6500  | 0.4573          | 0.3634 |
| 0.0744        | 14.06 | 7000  | 0.4711          | 0.3705 |
| 0.0663        | 15.06 | 7500  | 0.4955          | 0.3634 |
| 0.0612        | 16.06 | 8000  | 0.4721          | 0.3558 |
| 0.0535        | 17.07 | 8500  | 0.4965          | 0.3654 |
| 0.0527        | 18.07 | 9000  | 0.5381          | 0.3592 |
| 0.0458        | 19.08 | 9500  | 0.5029          | 0.3498 |
| 0.0424        | 20.08 | 10000 | 0.5814          | 0.3547 |
| 0.042         | 21.08 | 10500 | 0.4893          | 0.3480 |
| 0.0373        | 22.09 | 11000 | 0.5047          | 0.3482 |
| 0.0333        | 23.09 | 11500 | 0.5235          | 0.3426 |
| 0.0306        | 24.1  | 12000 | 0.5165          | 0.3472 |
| 0.0293        | 25.1  | 12500 | 0.4988          | 0.3426 |
| 0.025         | 26.1  | 13000 | 0.5157          | 0.3382 |
| 0.0255        | 27.11 | 13500 | 0.5278          | 0.3412 |
| 0.022         | 28.11 | 14000 | 0.5401          | 0.3364 |
| 0.0195        | 29.12 | 14500 | 0.5313          | 0.3317 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 1.18.3
- Tokenizers 0.15.1