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wav2vec2-xlsr-53-ft-btb-ccv-cy

Automatic Speech Recognition
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wav2vec2-xlsr-53-ft-btb-ccv-cy / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - automatic-speech-recognition
  - DewiBrynJones/banc-trawsgrifiadau-bangor-clean-with-ccv
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xlsr-53-ft-btb-ccv-cy
    results: []

wav2vec2-xlsr-53-ft-btb-ccv-cy

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN-WITH-CCV - DEFAULT dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4593
  • Wer: 0.3553

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: 0.0003
  • train_batch_size: 64
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 2600
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.0566 100 3.5994 1.0
No log 0.1133 200 3.0224 1.0
No log 0.1699 300 1.9627 0.9061
No log 0.2265 400 0.9921 0.6952
3.6184 0.2831 500 0.8504 0.6158
3.6184 0.3398 600 0.7936 0.5964
3.6184 0.3964 700 0.7601 0.5606
3.6184 0.4530 800 0.6909 0.5128
3.6184 0.5096 900 0.6617 0.4916
0.4662 0.5663 1000 0.6467 0.4812
0.4662 0.6229 1100 0.6144 0.4639
0.4662 0.6795 1200 0.5942 0.4537
0.4662 0.7361 1300 0.5675 0.4351
0.4662 0.7928 1400 0.5539 0.4230
0.3508 0.8494 1500 0.5448 0.4144
0.3508 0.9060 1600 0.5326 0.4066
0.3508 0.9626 1700 0.5155 0.3989
0.3508 1.0193 1800 0.5067 0.3856
0.3508 1.0759 1900 0.4916 0.3724
0.2774 1.1325 2000 0.4855 0.3697
0.2774 1.1891 2100 0.4798 0.3661
0.2774 1.2458 2200 0.4774 0.3646
0.2774 1.3024 2300 0.4699 0.3585
0.2774 1.3590 2400 0.4651 0.3550
0.2328 1.4156 2500 0.4611 0.3570
0.2328 1.4723 2600 0.4593 0.3553

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1