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: 0.5093
- Wer: 0.3413

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5009        | 1.0   | 500   | 1.6207          | 0.9471 |
| 0.8414        | 2.01  | 1000  | 0.5128          | 0.5033 |
| 0.4366        | 3.01  | 1500  | 0.4449          | 0.4450 |
| 0.3015        | 4.02  | 2000  | 0.3835          | 0.4108 |
| 0.2281        | 5.02  | 2500  | 0.3989          | 0.4109 |
| 0.1914        | 6.02  | 3000  | 0.4286          | 0.3982 |
| 0.1555        | 7.03  | 3500  | 0.4547          | 0.3889 |
| 0.1349        | 8.03  | 4000  | 0.3876          | 0.3779 |
| 0.1252        | 9.04  | 4500  | 0.4460          | 0.3810 |
| 0.1066        | 10.04 | 5000  | 0.3905          | 0.3772 |
| 0.0979        | 11.04 | 5500  | 0.4469          | 0.3646 |
| 0.0883        | 12.05 | 6000  | 0.4547          | 0.3612 |
| 0.0801        | 13.05 | 6500  | 0.4741          | 0.3645 |
| 0.0709        | 14.06 | 7000  | 0.4682          | 0.3592 |
| 0.0665        | 15.06 | 7500  | 0.4689          | 0.3647 |
| 0.0579        | 16.06 | 8000  | 0.5330          | 0.3622 |
| 0.0556        | 17.07 | 8500  | 0.4885          | 0.3575 |
| 0.0547        | 18.07 | 9000  | 0.4936          | 0.3543 |
| 0.0462        | 19.08 | 9500  | 0.4928          | 0.3524 |
| 0.0475        | 20.08 | 10000 | 0.5286          | 0.3525 |
| 0.0426        | 21.08 | 10500 | 0.5100          | 0.3550 |
| 0.0364        | 22.09 | 11000 | 0.5372          | 0.3493 |
| 0.0306        | 23.09 | 11500 | 0.5049          | 0.3443 |
| 0.0314        | 24.1  | 12000 | 0.5223          | 0.3519 |
| 0.0261        | 25.1  | 12500 | 0.5380          | 0.3486 |
| 0.0257        | 26.1  | 13000 | 0.5326          | 0.3484 |
| 0.0252        | 27.11 | 13500 | 0.5299          | 0.3446 |
| 0.0226        | 28.11 | 14000 | 0.5174          | 0.3424 |
| 0.0232        | 29.12 | 14500 | 0.5093          | 0.3413 |


### Framework versions

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