metadata

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

facebook/wav2vec2-xls-r-300m

This model is a fine-tuned version of 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