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---
license: apache-2.0
tags:
- google/fleurs
- generated_from_trainer
- automatic-speech-recognition
- hf-asr-leaderboard
- pashto
- ps
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
type: google/fleurs
args: 'config: ps_af, split: test'
metrics:
- name: Wer
type: wer
value: 0.5159447476125512
---
<!-- 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: 0.5159
- Cer: 0.1972
## 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
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