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wav2vec2-large-xls-r-300m-assamese

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_7_0 dataset. It achieves the following results on the evaluation set:

  • WER: 0.7954545454545454
  • CER: 0.32341269841269843

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

To compute the evaluation parameters

cd wav2vec2-large-xls-r-300m-assamese; python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config as --split test --log_outputs

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-4
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: not given
  • 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: 500
  • num_epochs: 400
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.584065 NA 400 1.584065 0.915512
1.658865 Na 800 1.658865 0.805096
1.882352 NA 1200 1.882352 0.820742
1.881240 NA 1600 1.881240 0.810907
2.159748 NA 2000 2.159748 0.804202
1.992871 NA 2400 1.992871 0.803308
2.201436 NA 2800 2.201436 0.802861
2.165218 NA 3200 2.165218 0.793920
2.253643 NA 3600 2.253643 0.796603
2.265880 NA 4000 2.265880 0.790344
2.293935 NA 4400 2.293935 0.797050
2.288851 NA 4800 2.288851 0.784086

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.10.0+cu113
  • Datasets 1.13.3
  • Tokenizers 0.10.3
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Dataset used to train infinitejoy/wav2vec2-large-xls-r-300m-assamese

Evaluation results