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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: inf
  • Wer: 0.5238

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: 32
  • 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: 6000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.1544 200 inf 1.0
No log 0.3089 400 inf 0.8661
3.7305 0.4633 600 inf 0.7040
3.7305 0.6178 800 inf 0.5506
0.8464 0.7722 1000 inf 0.5168
0.8464 0.9266 1200 inf 0.4825
0.8464 1.0811 1400 inf 0.4601
0.6629 1.2355 1600 inf 0.4445
0.6629 1.3900 1800 inf 0.4143
0.5655 1.5444 2000 inf 0.4170
0.5655 1.6988 2200 inf 0.4047
0.5655 1.8533 2400 inf 0.3966
0.5524 2.0077 2600 inf 0.3779
0.5524 2.1622 2800 inf 0.3737
0.4773 2.3166 3000 inf 0.3698
0.4773 2.4710 3200 inf 0.3724
0.4773 2.6255 3400 inf 0.3584
0.4694 2.7799 3600 inf 0.3821
0.4694 2.9344 3800 inf 0.4730
0.6537 3.0888 4000 inf 0.4754
0.6537 3.2432 4200 inf 0.5899
0.6537 3.3977 4400 inf 0.5958
0.8238 3.5521 4600 inf 0.6336
0.8238 3.7066 4800 inf 0.6026
0.8682 3.8610 5000 inf 0.5671
0.8682 4.0154 5200 inf 0.5378
0.8682 4.1699 5400 inf 0.5374
0.855 4.3243 5600 inf 0.5328
0.855 4.4788 5800 inf 0.5225
0.9644 4.6332 6000 inf 0.5238

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1