---
license: apache-2.0
base_model: facebook/wav2vec2-base-100h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-100h-timit-demo-google-colab
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-base-100h-timit-demo-google-colab

This model is a fine-tuned version of [facebook/wav2vec2-base-100h](https://huggingface.co/facebook/wav2vec2-base-100h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2661
- Wer: 0.3003

## 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: 16
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.002         | 2.01  | 500  | 3.8351          | 1.0    |
| 2.3505        | 4.02  | 1000 | 0.3927          | 0.4109 |
| 1.3738        | 6.02  | 1500 | 0.3139          | 0.3470 |
| 1.1562        | 8.03  | 2000 | 0.2850          | 0.3192 |
| 1.0548        | 10.04 | 2500 | 0.2677          | 0.3114 |
| 1.0112        | 12.05 | 3000 | 0.2758          | 0.3103 |
| 0.9481        | 14.06 | 3500 | 0.2806          | 0.3074 |
| 0.9109        | 16.06 | 4000 | 0.3118          | 0.3085 |
| 0.8542        | 18.07 | 4500 | 0.2772          | 0.3040 |
| 0.8117        | 20.08 | 5000 | 0.2650          | 0.3020 |
| 0.8058        | 22.09 | 5500 | 0.2848          | 0.3018 |
| 0.7619        | 24.1  | 6000 | 0.2686          | 0.3018 |
| 0.7529        | 26.1  | 6500 | 0.2713          | 0.3008 |
| 0.7172        | 28.11 | 7000 | 0.2661          | 0.3003 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 1.18.3
- Tokenizers 0.15.2