---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_asr_swbd_10_epochs
  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. -->

# wav2vec_asr_swbd_10_epochs

This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 0.9627

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 1.0682        | 0.22  | 5000   | 0.7383          | 0.4431 |
| 0.9143        | 0.44  | 10000  | 0.7182          | 0.4058 |
| 0.8905        | 0.66  | 15000  | 0.6291          | 0.3987 |
| 0.8354        | 0.87  | 20000  | 0.5976          | 0.3954 |
| 0.7749        | 1.09  | 25000  | 0.5773          | 0.3901 |
| 0.7336        | 1.31  | 30000  | 0.5812          | 0.3871 |
| 0.7314        | 1.53  | 35000  | 0.5802          | 0.3895 |
| 0.0           | 1.75  | 40000  | nan             | 0.9627 |
| 0.0           | 1.97  | 45000  | nan             | 0.9627 |
| 0.0           | 2.19  | 50000  | nan             | 0.9627 |
| 0.0           | 2.4   | 55000  | nan             | 0.9627 |
| 0.0           | 2.62  | 60000  | nan             | 0.9627 |
| 0.0           | 2.84  | 65000  | nan             | 0.9627 |
| 0.0           | 3.06  | 70000  | nan             | 0.9627 |
| 0.0           | 3.28  | 75000  | nan             | 0.9627 |
| 0.0           | 3.5   | 80000  | nan             | 0.9627 |
| 0.0           | 3.72  | 85000  | nan             | 0.9627 |
| 0.0           | 3.93  | 90000  | nan             | 0.9627 |
| 0.0           | 4.15  | 95000  | nan             | 0.9627 |
| 0.0           | 4.37  | 100000 | nan             | 0.9627 |
| 0.0           | 4.59  | 105000 | nan             | 0.9627 |
| 0.0           | 4.81  | 110000 | nan             | 0.9627 |
| 0.0           | 5.03  | 115000 | nan             | 0.9627 |
| 0.0           | 5.25  | 120000 | nan             | 0.9627 |
| 0.0           | 5.46  | 125000 | nan             | 0.9627 |
| 0.0           | 5.68  | 130000 | nan             | 0.9627 |
| 0.0           | 5.9   | 135000 | nan             | 0.9627 |
| 0.0           | 6.12  | 140000 | nan             | 0.9627 |
| 0.0           | 6.34  | 145000 | nan             | 0.9627 |
| 0.0           | 6.56  | 150000 | nan             | 0.9627 |
| 0.0           | 6.78  | 155000 | nan             | 0.9627 |
| 0.0           | 7.0   | 160000 | nan             | 0.9627 |
| 0.0           | 7.21  | 165000 | nan             | 0.9627 |
| 0.0           | 7.43  | 170000 | nan             | 0.9627 |
| 0.0           | 7.65  | 175000 | nan             | 0.9627 |
| 0.0           | 7.87  | 180000 | nan             | 0.9627 |
| 0.0           | 8.09  | 185000 | nan             | 0.9627 |
| 0.0           | 8.31  | 190000 | nan             | 0.9627 |
| 0.0           | 8.53  | 195000 | nan             | 0.9627 |
| 0.0           | 8.74  | 200000 | nan             | 0.9627 |
| 0.0           | 8.96  | 205000 | nan             | 0.9627 |
| 0.0           | 9.18  | 210000 | nan             | 0.9627 |
| 0.0           | 9.4   | 215000 | nan             | 0.9627 |
| 0.0           | 9.62  | 220000 | nan             | 0.9627 |
| 0.0           | 9.84  | 225000 | nan             | 0.9627 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6