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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-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: 0.5156036834924966

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: 0.5156
  • Cer: 0.1969

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
  • training_steps: 4500
  • 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 0.9287 0.5140 0.1972

Framework versions

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