---
license: apache-2.0
base_model: paruwka/wav2vec2-base-timit-demo-google-colab
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 [paruwka/wav2vec2-base-timit-demo-google-colab](https://huggingface.co/paruwka/wav2vec2-base-timit-demo-google-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0015
- Wer: 0.5828

## 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: 3e-05
- 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: 500
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4127        | 4.46  | 500  | 0.8997          | 0.6221 |
| 0.38          | 8.93  | 1000 | 0.9162          | 0.6091 |
| 0.3382        | 13.39 | 1500 | 0.9044          | 0.6011 |
| 0.3074        | 17.86 | 2000 | 0.9529          | 0.6030 |
| 0.274         | 22.32 | 2500 | 0.9327          | 0.5919 |
| 0.2558        | 26.79 | 3000 | 0.9906          | 0.5895 |
| 0.2462        | 31.25 | 3500 | 0.9873          | 0.5854 |
| 0.2215        | 35.71 | 4000 | 1.0059          | 0.5839 |
| 0.2313        | 40.18 | 4500 | 1.0142          | 0.5823 |
| 0.2182        | 44.64 | 5000 | 0.9998          | 0.5804 |
| 0.2123        | 49.11 | 5500 | 1.0015          | 0.5828 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2