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: 0.5112
- Wer: 0.9988

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5557        | 1.0   | 500   | 1.6786          | 1.0    |
| 0.8407        | 2.01  | 1000  | 0.5356          | 0.9988 |
| 0.4297        | 3.01  | 1500  | 0.4431          | 0.9988 |
| 0.2989        | 4.02  | 2000  | 0.4191          | 0.9988 |
| 0.2338        | 5.02  | 2500  | 0.4251          | 0.9988 |
| 0.1993        | 6.02  | 3000  | 0.4618          | 0.9988 |
| 0.1585        | 7.03  | 3500  | 0.4577          | 0.9988 |
| 0.1386        | 8.03  | 4000  | 0.4099          | 0.9982 |
| 0.1234        | 9.04  | 4500  | 0.4945          | 0.9988 |
| 0.1162        | 10.04 | 5000  | 0.4597          | 0.9988 |
| 0.1008        | 11.04 | 5500  | 0.4563          | 0.9988 |
| 0.0894        | 12.05 | 6000  | 0.5157          | 0.9988 |
| 0.083         | 13.05 | 6500  | 0.5027          | 0.9988 |
| 0.0735        | 14.06 | 7000  | 0.4905          | 0.9994 |
| 0.0686        | 15.06 | 7500  | 0.4552          | 0.9988 |
| 0.0632        | 16.06 | 8000  | 0.5522          | 0.9988 |
| 0.061         | 17.07 | 8500  | 0.4874          | 0.9988 |
| 0.0626        | 18.07 | 9000  | 0.5243          | 0.9988 |
| 0.0475        | 19.08 | 9500  | 0.4798          | 0.9988 |
| 0.0447        | 20.08 | 10000 | 0.5250          | 0.9988 |
| 0.0432        | 21.08 | 10500 | 0.5195          | 0.9988 |
| 0.0358        | 22.09 | 11000 | 0.5008          | 0.9988 |
| 0.0319        | 23.09 | 11500 | 0.5376          | 0.9988 |
| 0.0334        | 24.1  | 12000 | 0.5149          | 0.9988 |
| 0.0269        | 25.1  | 12500 | 0.4911          | 0.9988 |
| 0.0275        | 26.1  | 13000 | 0.4907          | 0.9988 |
| 0.027         | 27.11 | 13500 | 0.4992          | 0.9988 |
| 0.0239        | 28.11 | 14000 | 0.5021          | 0.9988 |
| 0.0233        | 29.12 | 14500 | 0.5112          | 0.9988 |


### Framework versions

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