---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: timit_asr
      type: timit_asr
      config: clean
      split: None
      args: clean
    metrics:
    - name: Wer
      type: wer
      value: 0.3354696437185583
---

<!-- 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 timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4743
- Wer: 0.3355

## 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: 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4394        | 4.0   | 500  | 1.2662          | 0.8530 |
| 0.5192        | 8.0   | 1000 | 0.4308          | 0.4176 |
| 0.1896        | 12.0  | 1500 | 0.4249          | 0.3656 |
| 0.1158        | 16.0  | 2000 | 0.4405          | 0.3583 |
| 0.0791        | 20.0  | 2500 | 0.4949          | 0.3481 |
| 0.0578        | 24.0  | 3000 | 0.4895          | 0.3448 |
| 0.0462        | 28.0  | 3500 | 0.4743          | 0.3355 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.1.2
- Datasets 3.0.1
- Tokenizers 0.20.0