transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-korean_speed
  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-korean_speed

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: 4.3672
- Cer: 98.3846

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 34.1959       | 0.13  | 200  | 5.0888          | 100.0   |
| 5.0127        | 0.26  | 400  | 4.8530          | 100.0   |
| 4.8574        | 0.39  | 600  | 4.8649          | 100.0   |
| 4.8596        | 0.52  | 800  | 4.6823          | 100.0   |
| 4.7742        | 0.64  | 1000 | 4.9200          | 100.0   |
| 4.7557        | 0.77  | 1200 | 4.6647          | 100.0   |
| 4.7367        | 0.9   | 1400 | 4.6535          | 100.0   |
| 4.7075        | 1.03  | 1600 | 4.6910          | 100.0   |
| 4.6994        | 1.16  | 1800 | 4.7617          | 100.0   |
| 4.6886        | 1.29  | 2000 | 4.7125          | 100.0   |
| 4.6495        | 1.42  | 2200 | 4.7181          | 100.0   |
| 4.651         | 1.55  | 2400 | 4.7701          | 100.0   |
| 4.5945        | 1.68  | 2600 | 4.7640          | 100.0   |
| 4.5589        | 1.81  | 2800 | 5.2212          | 100.0   |
| 4.5125        | 1.93  | 3000 | 4.6738          | 99.9178 |
| 4.4526        | 2.06  | 3200 | 4.6993          | 99.8649 |
| 4.3971        | 2.19  | 3400 | 4.6336          | 99.7063 |
| 4.3061        | 2.32  | 3600 | 4.6569          | 99.2775 |
| 4.2643        | 2.45  | 3800 | 4.5247          | 99.4243 |
| 4.1763        | 2.58  | 4000 | 4.5722          | 99.1189 |
| 4.106         | 2.71  | 4200 | 4.4703          | 99.0778 |
| 4.0419        | 2.84  | 4400 | 4.4161          | 97.9617 |
| 4.0283        | 2.97  | 4600 | 4.3672          | 98.3846 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
