---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Wav2Vec2_FullDataset
  results: []
datasets:
- timit-asr/timit_asr
language:
- en
metrics:
- wer
base_model:
- facebook/wav2vec2-base
pipeline_tag: automatic-speech-recognition
library_name: transformers
---

<!-- 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_FullDataset

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: 0.5144
- Wer: 0.3292

## 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5171        | 1.0   | 500   | 1.8108          | 1.0014 |
| 0.8397        | 2.01  | 1000  | 0.5559          | 0.5358 |
| 0.431         | 3.01  | 1500  | 0.4265          | 0.4469 |
| 0.2931        | 4.02  | 2000  | 0.4034          | 0.4193 |
| 0.2247        | 5.02  | 2500  | 0.4595          | 0.4076 |
| 0.1855        | 6.02  | 3000  | 0.4543          | 0.3991 |
| 0.1497        | 7.03  | 3500  | 0.4894          | 0.3839 |
| 0.1339        | 8.03  | 4000  | 0.4514          | 0.3836 |
| 0.1166        | 9.04  | 4500  | 0.4432          | 0.3682 |
| 0.1063        | 10.04 | 5000  | 0.4781          | 0.3773 |
| 0.0923        | 11.04 | 5500  | 0.4548          | 0.3699 |
| 0.0899        | 12.05 | 6000  | 0.4836          | 0.3636 |
| 0.0802        | 13.05 | 6500  | 0.5117          | 0.3637 |
| 0.0726        | 14.06 | 7000  | 0.4453          | 0.3653 |
| 0.07          | 15.06 | 7500  | 0.4983          | 0.3581 |
| 0.0641        | 16.06 | 8000  | 0.4922          | 0.3603 |
| 0.0561        | 17.07 | 8500  | 0.4947          | 0.3517 |
| 0.0522        | 18.07 | 9000  | 0.5132          | 0.3513 |
| 0.0483        | 19.08 | 9500  | 0.4815          | 0.3453 |
| 0.0419        | 20.08 | 10000 | 0.5556          | 0.3459 |
| 0.0402        | 21.08 | 10500 | 0.5141          | 0.3428 |
| 0.0368        | 22.09 | 11000 | 0.5176          | 0.3437 |
| 0.0322        | 23.09 | 11500 | 0.5326          | 0.3403 |
| 0.0305        | 24.1  | 12000 | 0.5046          | 0.3366 |
| 0.0258        | 25.1  | 12500 | 0.5219          | 0.3315 |
| 0.0254        | 26.1  | 13000 | 0.5166          | 0.3289 |
| 0.0226        | 27.11 | 13500 | 0.5177          | 0.3311 |
| 0.0226        | 28.11 | 14000 | 0.5187          | 0.3302 |
| 0.0209        | 29.12 | 14500 | 0.5144          | 0.3292 |


### Framework versions

- Transformers 4.17.0
- Pytorch 2.5.1+cu121
- Datasets 1.18.3
- Tokenizers 0.20.3