---
license: apache-2.0
tags:
- automatic-speech-recognition
- google/fleurs
- generated_from_trainer
- hf-asr-leaderboard
- ps
- Pashto
datasets:
- fleurs
metrics:
- wer
model-index:
- name: facebook/wav2vec2-xls-r-300m
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: GOOGLE/FLEURS - PS_AF
      type: fleurs
      config: ps_af
      split: test
      args: 'Config: ps_af, Training split: train+validation, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.5137278308321964
---

<!-- 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: 0.9154
- Wer: 0.5137
- Cer: 0.1966

## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.0767        | 6.33  | 500  | 4.8783          | 1.0    | 1.0    |
| 3.1156        | 12.66 | 1000 | 3.0990          | 1.0    | 1.0    |
| 1.3506        | 18.99 | 1500 | 1.1056          | 0.7031 | 0.2889 |
| 0.9997        | 25.32 | 2000 | 0.9191          | 0.5944 | 0.2301 |
| 0.7838        | 31.65 | 2500 | 0.8952          | 0.5556 | 0.2152 |
| 0.6665        | 37.97 | 3000 | 0.8908          | 0.5252 | 0.2017 |
| 0.6265        | 44.3  | 3500 | 0.9063          | 0.5133 | 0.1954 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2