---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vecvanilla_load_best
  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. -->

# wav2vecvanilla_load_best

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
It achieves the following results on the evaluation set:

- best model at 1400 steps, training loss 0.871600, validation loss	0.835038, evaluation WER 0.324867

## 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: 4
- 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: 500
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.47          | 0.43  | 100  | 1.0582          | 0.4027 |
| 1.2584        | 0.85  | 200  | 0.9775          | 0.3719 |
| 1.1377        | 1.28  | 300  | 0.9759          | 0.3628 |
| 1.0955        | 1.71  | 400  | 1.1271          | 0.3626 |
| 1.0945        | 2.14  | 500  | 0.9063          | 0.3589 |
| 1.0567        | 2.56  | 600  | 0.9288          | 0.3564 |
| 1.033         | 2.99  | 700  | 1.1634          | 0.3522 |
| 1.0043        | 3.42  | 800  | 0.8396          | 0.3506 |
| 0.9776        | 3.85  | 900  | 0.8654          | 0.3437 |
| 0.9147        | 4.27  | 1000 | 0.8816          | 0.3362 |
| 0.9041        | 4.7   | 1100 | 0.8994          | 0.3303 |
| 0.8648        | 5.13  | 1200 | 0.8379          | 0.3361 |
| 0.8241        | 5.56  | 1300 | 0.8263          | 0.3292 |
| 0.8716        | 5.98  | 1400 | 0.8350          | 0.3249 |
| 0.8218        | 6.41  | 1500 | nan             | 1.0    |
| 0.0           | 6.84  | 1600 | nan             | 1.0    |
| 0.0           | 7.26  | 1700 | nan             | 1.0    |
| 0.0           | 7.69  | 1800 | nan             | 1.0    |
| 0.0           | 8.12  | 1900 | nan             | 1.0    |
| 0.0           | 8.55  | 2000 | nan             | 1.0    |
| 0.0           | 8.97  | 2100 | nan             | 1.0    |
| 0.0           | 9.4   | 2200 | nan             | 1.0    |
| 0.0           | 9.83  | 2300 | nan             | 1.0    |
| 0.0           | 10.26 | 2400 | nan             | 1.0    |
| 0.0           | 10.68 | 2500 | nan             | 1.0    |
| 0.0           | 11.11 | 2600 | nan             | 1.0    |
| 0.0           | 11.54 | 2700 | nan             | 1.0    |
| 0.0           | 11.97 | 2800 | nan             | 1.0    |
| 0.0           | 12.39 | 2900 | nan             | 1.0    |
| 0.0           | 12.82 | 3000 | nan             | 1.0    |
| 0.0           | 13.25 | 3100 | nan             | 1.0    |
| 0.0           | 13.68 | 3200 | nan             | 1.0    |
| 0.0           | 14.1  | 3300 | nan             | 1.0    |
| 0.0           | 14.53 | 3400 | nan             | 1.0    |
| 0.0           | 14.96 | 3500 | nan             | 1.0    |
| 0.0           | 15.38 | 3600 | nan             | 1.0    |
| 0.0           | 15.81 | 3700 | nan             | 1.0    |
| 0.0           | 16.24 | 3800 | nan             | 1.0    |
| 0.0           | 16.67 | 3900 | nan             | 1.0    |
| 0.0           | 17.09 | 4000 | nan             | 1.0    |
| 0.0           | 17.52 | 4100 | nan             | 1.0    |
| 0.0           | 17.95 | 4200 | nan             | 1.0    |
| 0.0           | 18.38 | 4300 | nan             | 1.0    |
| 0.0           | 18.8  | 4400 | nan             | 1.0    |
| 0.0           | 19.23 | 4500 | nan             | 1.0    |
| 0.0           | 19.66 | 4600 | nan             | 1.0    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2