---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetuned_Wav2Vec2_on_ATCOSIM
  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. -->

# finetuned_Wav2Vec2_on_ATCOSIM

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1707
- Wer: 0.1170

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6927        | 2.5   | 400  | 0.5557          | 0.4100 |
| 0.3288        | 4.99  | 800  | 0.2382          | 0.1943 |
| 0.1856        | 7.49  | 1200 | 0.1957          | 0.1699 |
| 0.1325        | 9.99  | 1600 | 0.1845          | 0.1572 |
| 0.1018        | 12.48 | 2000 | 0.1771          | 0.1534 |
| 0.0899        | 14.98 | 2400 | 0.1637          | 0.1356 |
| 0.0722        | 17.48 | 2800 | 0.1812          | 0.1409 |
| 0.0596        | 19.98 | 3200 | 0.1747          | 0.1323 |
| 0.046         | 22.47 | 3600 | 0.1505          | 0.1307 |
| 0.037         | 24.97 | 4000 | 0.1705          | 0.1224 |
| 0.0294        | 27.47 | 4400 | 0.1614          | 0.1164 |
| 0.0249        | 29.96 | 4800 | 0.1707          | 0.1170 |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3