---
base_model: facebook/wav2vec2-xls-r-300m
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-tsovatush-demo-colab-bryn-hauk-9-21
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xls-r-300m-tsovatush-demo-colab-bryn-hauk-9-21

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2641
- Wer: None
- Cer: None

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 5.3691        | 5.6338  | 400  | 3.5145          | None   | None   |
| 2.3829        | 11.2676 | 800  | 1.1264          | None   | None   |
| 0.8921        | 16.9014 | 1200 | 1.0416          | 0.5504 | 0.2172 |
| 0.515         | 22.5352 | 1600 | 1.0066          | None   | None   |
| 0.3268        | 28.1690 | 2000 | 1.1589          | 0.5101 | 0.1869 |
| 0.2142        | 33.8028 | 2400 | 1.2165          | 0.5144 | 0.1934 |
| 0.1496        | 39.4366 | 2800 | 1.1939          | None   | None   |
| 0.1013        | 45.0704 | 3200 | 1.2641          | None   | None   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 1.18.3
- Tokenizers 0.19.1