pytorch tensorboard wav2vec2 automatic-speech-recognition transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-google-colab
  results: []
---

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# wav2vec2-base-timit-google-colab

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: 0.4659
- Wer: 0.3080

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5787        | 0.87  | 500   | 1.7648          | 1.0305 |
| 0.8692        | 1.73  | 1000  | 0.5136          | 0.5103 |
| 0.4346        | 2.6   | 1500  | 0.4364          | 0.4515 |
| 0.31          | 3.46  | 2000  | 0.3889          | 0.4070 |
| 0.234         | 4.33  | 2500  | 0.4161          | 0.3863 |
| 0.2054        | 5.19  | 3000  | 0.3845          | 0.3722 |
| 0.165         | 6.06  | 3500  | 0.4035          | 0.3643 |
| 0.1436        | 6.92  | 4000  | 0.4090          | 0.3623 |
| 0.1381        | 7.79  | 4500  | 0.4007          | 0.3673 |
| 0.1175        | 8.65  | 5000  | 0.4588          | 0.3632 |
| 0.1052        | 9.52  | 5500  | 0.4441          | 0.3588 |
| 0.0988        | 10.38 | 6000  | 0.4133          | 0.3489 |
| 0.0877        | 11.25 | 6500  | 0.4758          | 0.3510 |
| 0.0856        | 12.11 | 7000  | 0.4454          | 0.3425 |
| 0.0731        | 12.98 | 7500  | 0.4252          | 0.3351 |
| 0.0712        | 13.84 | 8000  | 0.4163          | 0.3370 |
| 0.0711        | 14.71 | 8500  | 0.4166          | 0.3367 |
| 0.06          | 15.57 | 9000  | 0.4195          | 0.3347 |
| 0.0588        | 16.44 | 9500  | 0.4697          | 0.3367 |
| 0.0497        | 17.3  | 10000 | 0.4255          | 0.3314 |
| 0.0523        | 18.17 | 10500 | 0.4676          | 0.3307 |
| 0.0444        | 19.03 | 11000 | 0.4570          | 0.3244 |
| 0.0435        | 19.9  | 11500 | 0.4307          | 0.3243 |
| 0.0348        | 20.76 | 12000 | 0.4763          | 0.3245 |
| 0.036         | 21.63 | 12500 | 0.4635          | 0.3238 |
| 0.0347        | 22.49 | 13000 | 0.4602          | 0.3212 |
| 0.0333        | 23.36 | 13500 | 0.4472          | 0.3195 |
| 0.0311        | 24.22 | 14000 | 0.4449          | 0.3183 |
| 0.0294        | 25.09 | 14500 | 0.4631          | 0.3175 |
| 0.025         | 25.95 | 15000 | 0.4466          | 0.3164 |
| 0.023         | 26.82 | 15500 | 0.4581          | 0.3138 |
| 0.0216        | 27.68 | 16000 | 0.4665          | 0.3114 |
| 0.0198        | 28.55 | 16500 | 0.4590          | 0.3092 |
| 0.0181        | 29.41 | 17000 | 0.4659          | 0.3080 |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
