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---
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
|