---
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: fine-tune-wav2vec2-large-xls-r-300m-xty_224s
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ml-superb-subset
      type: ml-superb-subset
      config: xty
      split: test[:100]
      args: xty
    metrics:
    - name: Wer
      type: wer
      value: 1.0584538026398491
---

<!-- 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. -->

# fine-tune-wav2vec2-large-xls-r-300m-xty_224s

This model was trained from scratch on the ml-superb-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7658
- Wer: 1.0585

## 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: 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: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 1.5419        | 5.5172  | 400  | 0.4368          | 0.9994 |
| 0.423         | 11.0345 | 800  | 0.4315          | 1.0    |
| 0.3795        | 16.5517 | 1200 | 0.3892          | 1.0151 |
| 0.3306        | 22.0690 | 1600 | 0.4055          | 1.0013 |
| 0.2464        | 27.5862 | 2000 | 0.4672          | 1.0421 |
| 0.1454        | 33.1034 | 2400 | 0.6656          | 1.0333 |
| 0.0883        | 38.6207 | 2800 | 0.7658          | 1.0585 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1