---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-af-1hr-v2
  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-large-xls-r-300m-af-1hr-v2

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: 1.4465
- Wer: 0.6609
- Cer: 0.2264

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 13.6642       | 2.7778  | 100  | 6.3667          | 1.0    | 1.0    |
| 4.9297        | 5.5556  | 200  | 3.8947          | 1.0    | 1.0    |
| 3.3634        | 8.3333  | 300  | 3.0087          | 1.0    | 1.0    |
| 2.9415        | 11.1111 | 400  | 2.9310          | 1.0    | 1.0    |
| 2.8979        | 13.8889 | 500  | 2.8946          | 1.0    | 1.0    |
| 2.8804        | 16.6667 | 600  | 2.8853          | 1.0    | 1.0    |
| 2.8089        | 19.4444 | 700  | 2.5587          | 1.0    | 0.9801 |
| 1.5299        | 22.2222 | 800  | 1.3499          | 0.8436 | 0.2927 |
| 0.6494        | 25.0    | 900  | 1.2093          | 0.6976 | 0.2505 |
| 0.3557        | 27.7778 | 1000 | 1.2005          | 0.6841 | 0.2389 |
| 0.249         | 30.5556 | 1100 | 1.2462          | 0.6865 | 0.2371 |
| 0.1929        | 33.3333 | 1200 | 1.3202          | 0.6744 | 0.2335 |
| 0.1545        | 36.1111 | 1300 | 1.3346          | 0.6676 | 0.2282 |
| 0.1352        | 38.8889 | 1400 | 1.4056          | 0.6768 | 0.2361 |
| 0.1182        | 41.6667 | 1500 | 1.3930          | 0.6635 | 0.2326 |
| 0.1063        | 44.4444 | 1600 | 1.4446          | 0.6573 | 0.2293 |
| 0.0972        | 47.2222 | 1700 | 1.3908          | 0.6531 | 0.2273 |
| 0.0804        | 50.0    | 1800 | 1.4328          | 0.6541 | 0.2262 |
| 0.0816        | 52.7778 | 1900 | 1.4489          | 0.6586 | 0.2263 |
| 0.078         | 55.5556 | 2000 | 1.4210          | 0.6581 | 0.2268 |
| 0.0714        | 58.3333 | 2100 | 1.4393          | 0.6581 | 0.2260 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1