---
license: apache-2.0
tags:
- google/fleurs
- generated_from_trainer
- automatic-speech-recognition
- pashto
- ps
datasets:
- fleurs
metrics:
- wer
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: facebook/wav2vec2-xls-r-300m
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      args: 'config: ps_af, split: test'
    metrics:
    - type: wer
      value: 51.59447476125512
      name: Wer
---

<!-- 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. -->

# facebook/wav2vec2-xls-r-300m

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the GOOGLE/FLEURS - PS_AF dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9162
- Wer: 51.59
- Cer: 19.72

## 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: 7.5e-07
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1000
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 5.0767        | 6.33  | 500  | 1.0    | 4.8783          | 1.0    |
| 3.1156        | 12.66 | 1000 | 1.0    | 3.0990          | 1.0    |
| 1.3506        | 18.99 | 1500 | 0.2889 | 1.1056          | 0.7031 |
| 0.9997        | 25.32 | 2000 | 0.2301 | 0.9191          | 0.5944 |
| 0.7838        | 31.65 | 2500 | 0.2152 | 0.8952          | 0.5556 |
| 0.6665        | 37.97 | 3000 | 0.2017 | 0.8908          | 0.5252 |
| 0.6265        | 44.3  | 3500 | 0.1954 | 0.9063          | 0.5133 |
| 0.5935        | 50.63 | 4000 | 0.1969 | 0.9162          | 0.5156 |
| 0.5174        | 56.96 | 4500 | 0.1972 | 0.9287          | 0.5140 |
| 0.5462        | 63.29 | 5000 | 0.1974 | 0.9370          | 0.5138 |
| 0.5564        | 69.62 | 5500 | 0.1977 | 0.9461          | 0.5148 |
| 0.5252        | 75.95 | 6000 | 0.9505 | 0.5118          | 0.1969 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2