metadata

license: apache-2.0
tags:
  - automatic-speech-recognition
  - google/fleurs
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: facebook/wav2vec2-xls-r-1b
    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: 0.9294849931787176

facebook/wav2vec2-xls-r-1b

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the GOOGLE/FLEURS - PS_AF dataset. It achieves the following results on the evaluation set:

Loss: 4.1921
Wer: 0.9295
Cer: 0.9608

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: 1000
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
19.9558	1.27	100	3.2660	20.9197	1.0
19.7186	2.53	200	1.1692	19.2447	1.0
15.203	3.8	300	0.9687	15.0053	0.9998
6.4303	5.06	400	0.9911	6.5437	0.9632
4.5712	6.33	500	0.9546	4.9040	0.9323
3.3986	12.66	1000	4.1921	0.9295	0.9608

Framework versions

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