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: 0.5255
- Wer: 0.3330

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5942        | 1.0   | 500   | 2.3849          | 1.0011 |
| 0.9765        | 2.01  | 1000  | 0.5907          | 0.5202 |
| 0.4424        | 3.01  | 1500  | 0.4547          | 0.4661 |
| 0.3008        | 4.02  | 2000  | 0.4194          | 0.4228 |
| 0.2316        | 5.02  | 2500  | 0.3933          | 0.4099 |
| 0.1921        | 6.02  | 3000  | 0.4532          | 0.3965 |
| 0.1561        | 7.03  | 3500  | 0.4315          | 0.3777 |
| 0.1378        | 8.03  | 4000  | 0.4463          | 0.3847 |
| 0.1222        | 9.04  | 4500  | 0.4402          | 0.3784 |
| 0.1076        | 10.04 | 5000  | 0.4253          | 0.3735 |
| 0.0924        | 11.04 | 5500  | 0.4844          | 0.3732 |
| 0.0866        | 12.05 | 6000  | 0.4758          | 0.3646 |
| 0.086         | 13.05 | 6500  | 0.6395          | 0.4594 |
| 0.0763        | 14.06 | 7000  | 0.4951          | 0.3647 |
| 0.0684        | 15.06 | 7500  | 0.4870          | 0.3577 |
| 0.0616        | 16.06 | 8000  | 0.5442          | 0.3591 |
| 0.0594        | 17.07 | 8500  | 0.5305          | 0.3606 |
| 0.0613        | 18.07 | 9000  | 0.5434          | 0.3546 |
| 0.0473        | 19.08 | 9500  | 0.4818          | 0.3532 |
| 0.0463        | 20.08 | 10000 | 0.5086          | 0.3514 |
| 0.042         | 21.08 | 10500 | 0.5017          | 0.3484 |
| 0.0365        | 22.09 | 11000 | 0.5129          | 0.3536 |
| 0.0336        | 23.09 | 11500 | 0.5411          | 0.3433 |
| 0.0325        | 24.1  | 12000 | 0.5307          | 0.3424 |
| 0.0282        | 25.1  | 12500 | 0.5261          | 0.3404 |
| 0.0245        | 26.1  | 13000 | 0.5306          | 0.3388 |
| 0.0257        | 27.11 | 13500 | 0.5242          | 0.3369 |
| 0.0234        | 28.11 | 14000 | 0.5216          | 0.3359 |
| 0.0221        | 29.12 | 14500 | 0.5255          | 0.3330 |


### Framework versions

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