---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-fine-tuned
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: TIMIT_ASR - NA
      type: timit_asr
      config: clean
      split: test
      args: 'Config: na, Training split: train, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.4328507693708459
---

<!-- 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-fine-tuned

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4233
- Wer: 0.4329

## 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: 64
- eval_batch_size: 32
- 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: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.158         | 1.7241  | 100  | 3.6803          | 1.0    |
| 2.9744        | 3.4483  | 200  | 3.1165          | 1.0    |
| 2.9266        | 5.1724  | 300  | 3.0175          | 1.0    |
| 2.1336        | 6.8966  | 400  | 2.2135          | 1.0117 |
| 1.0119        | 8.6207  | 500  | 1.0227          | 0.8251 |
| 0.4995        | 10.3448 | 600  | 0.7700          | 0.6574 |
| 0.3233        | 12.0690 | 700  | 0.4970          | 0.5241 |
| 0.2452        | 13.7931 | 800  | 0.4585          | 0.4908 |
| 0.181         | 15.5172 | 900  | 0.4626          | 0.4814 |
| 0.1419        | 17.2414 | 1000 | 0.4917          | 0.4775 |
| 0.1175        | 18.9655 | 1100 | 0.4279          | 0.4359 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0a0+gitcd033a1
- Datasets 2.19.1
- Tokenizers 0.19.1