---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-base
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: timit_asr
      type: timit_asr
      config: clean
      split: None
      args: clean
    metrics:
    - type: wer
      value: 0.33795052029494865
      name: Wer
---

<!-- 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: 0.5720
- Wer: 0.3380

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 3.466         | 1.0040  | 500   | 1.4947          | 0.9858 |
| 0.8266        | 2.0080  | 1000  | 0.5298          | 0.5179 |
| 0.438         | 3.0120  | 1500  | 0.4565          | 0.4564 |
| 0.2918        | 4.0161  | 2000  | 0.4528          | 0.4382 |
| 0.2282        | 5.0201  | 2500  | 0.4541          | 0.4095 |
| 0.184         | 6.0241  | 3000  | 0.5109          | 0.4053 |
| 0.1513        | 7.0281  | 3500  | 0.5116          | 0.3923 |
| 0.1378        | 8.0321  | 4000  | 0.5137          | 0.3876 |
| 0.1194        | 9.0361  | 4500  | 0.5208          | 0.3961 |
| 0.1072        | 10.0402 | 5000  | 0.5417          | 0.3845 |
| 0.0982        | 11.0442 | 5500  | 0.5653          | 0.3847 |
| 0.0868        | 12.0482 | 6000  | 0.4593          | 0.3722 |
| 0.0774        | 13.0522 | 6500  | 0.4822          | 0.3723 |
| 0.0723        | 14.0562 | 7000  | 0.5303          | 0.3702 |
| 0.0635        | 15.0602 | 7500  | 0.4888          | 0.3742 |
| 0.0597        | 16.0643 | 8000  | 0.5254          | 0.3638 |
| 0.0571        | 17.0683 | 8500  | 0.5107          | 0.3632 |
| 0.0491        | 18.0723 | 9000  | 0.5649          | 0.3575 |
| 0.0511        | 19.0763 | 9500  | 0.5430          | 0.3627 |
| 0.0425        | 20.0803 | 10000 | 0.5726          | 0.3633 |
| 0.0386        | 21.0843 | 10500 | 0.5977          | 0.3657 |
| 0.0388        | 22.0884 | 11000 | 0.5430          | 0.3570 |
| 0.0338        | 23.0924 | 11500 | 0.5612          | 0.3535 |
| 0.0301        | 24.0964 | 12000 | 0.5841          | 0.3514 |
| 0.0272        | 25.1004 | 12500 | 0.5682          | 0.3457 |
| 0.0255        | 26.1044 | 13000 | 0.5657          | 0.3494 |
| 0.0234        | 27.1084 | 13500 | 0.5611          | 0.3450 |
| 0.0248        | 28.1124 | 14000 | 0.5721          | 0.3399 |
| 0.0203        | 29.1165 | 14500 | 0.5720          | 0.3380 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1