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-demo-google-colab
  results: []
---

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# 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5353
- Wer: 0.3360

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5345        | 1.0   | 500   | 1.8229          | 0.9810 |
| 0.8731        | 2.01  | 1000  | 0.5186          | 0.5165 |
| 0.4455        | 3.01  | 1500  | 0.4386          | 0.4572 |
| 0.3054        | 4.02  | 2000  | 0.4396          | 0.4286 |
| 0.2354        | 5.02  | 2500  | 0.4454          | 0.4051 |
| 0.1897        | 6.02  | 3000  | 0.4465          | 0.3925 |
| 0.1605        | 7.03  | 3500  | 0.4776          | 0.3974 |
| 0.1413        | 8.03  | 4000  | 0.5254          | 0.4062 |
| 0.1211        | 9.04  | 4500  | 0.5123          | 0.3913 |
| 0.1095        | 10.04 | 5000  | 0.4171          | 0.3711 |
| 0.1039        | 11.04 | 5500  | 0.4258          | 0.3732 |
| 0.0932        | 12.05 | 6000  | 0.4879          | 0.3701 |
| 0.0867        | 13.05 | 6500  | 0.4725          | 0.3637 |
| 0.0764        | 14.06 | 7000  | 0.5041          | 0.3636 |
| 0.0661        | 15.06 | 7500  | 0.4692          | 0.3646 |
| 0.0647        | 16.06 | 8000  | 0.4804          | 0.3612 |
| 0.0576        | 17.07 | 8500  | 0.5545          | 0.3628 |
| 0.0577        | 18.07 | 9000  | 0.5004          | 0.3557 |
| 0.0481        | 19.08 | 9500  | 0.5341          | 0.3558 |
| 0.0466        | 20.08 | 10000 | 0.5056          | 0.3514 |
| 0.0433        | 21.08 | 10500 | 0.4864          | 0.3481 |
| 0.0362        | 22.09 | 11000 | 0.4994          | 0.3473 |
| 0.0325        | 23.09 | 11500 | 0.5327          | 0.3446 |
| 0.0351        | 24.1  | 12000 | 0.5360          | 0.3445 |
| 0.0284        | 25.1  | 12500 | 0.5085          | 0.3399 |
| 0.027         | 26.1  | 13000 | 0.5344          | 0.3426 |
| 0.0247        | 27.11 | 13500 | 0.5310          | 0.3357 |
| 0.0251        | 28.11 | 14000 | 0.5201          | 0.3355 |
| 0.0228        | 29.12 | 14500 | 0.5353          | 0.3360 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
