---
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: test
      args: clean
    metrics:
    - name: Wer
      type: wer
      value: 0.3367100820067535
---

<!-- 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: 0.4634
- Wer: 0.3367

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6019        | 1.0   | 500  | 2.4586          | 1.0    |
| 0.9594        | 2.01  | 1000 | 0.5023          | 0.5122 |
| 0.4324        | 3.01  | 1500 | 0.4808          | 0.4703 |
| 0.2991        | 4.02  | 2000 | 0.4098          | 0.4208 |
| 0.2257        | 5.02  | 2500 | 0.4883          | 0.4264 |
| 0.18          | 6.02  | 3000 | 0.4441          | 0.3914 |
| 0.1524        | 7.03  | 3500 | 0.4360          | 0.3869 |
| 0.1315        | 8.03  | 4000 | 0.4448          | 0.3783 |
| 0.1101        | 9.04  | 4500 | 0.4570          | 0.3704 |
| 0.1017        | 10.04 | 5000 | 0.4252          | 0.3680 |
| 0.0863        | 11.04 | 5500 | 0.4492          | 0.3606 |
| 0.0798        | 12.05 | 6000 | 0.4241          | 0.3604 |
| 0.0688        | 13.05 | 6500 | 0.4585          | 0.3535 |
| 0.0608        | 14.06 | 7000 | 0.4491          | 0.3488 |
| 0.0524        | 15.06 | 7500 | 0.4550          | 0.3456 |
| 0.0502        | 16.06 | 8000 | 0.4570          | 0.3453 |
| 0.0458        | 17.07 | 8500 | 0.4680          | 0.3421 |
| 0.0395        | 18.07 | 9000 | 0.4663          | 0.3390 |
| 0.0352        | 19.08 | 9500 | 0.4634          | 0.3367 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 1.18.3
- Tokenizers 0.15.2