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DewiBrynJones
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wav2vec2-xlsr-53-ft-btb-cy

Automatic Speech Recognition
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Safetensors
wav2vec2
DewiBrynJones/banc-trawsgrifiadau-bangor-clean
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wav2vec2-xlsr-53-ft-btb-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
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xlsr-53-ft-btb-cy
    results: []

wav2vec2-xlsr-53-ft-btb-cy

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

  • Loss: 2.2310
  • Wer: 0.9240

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: 16
  • 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 0.1139 200 3.1075 1.0
No log 0.2278 400 1.6249 0.8958
3.6043 0.3417 600 1.1246 0.8676
3.6043 0.4556 800 0.9142 0.7094
1.0665 0.5695 1000 0.7570 0.5934
1.0665 0.6834 1200 0.6980 0.5285
1.0665 0.7973 1400 0.6942 0.5389
0.8811 0.9112 1600 0.6152 0.4862
0.8811 1.0251 1800 0.5941 0.4535
0.7654 1.1390 2000 0.5642 0.4329
0.7654 1.2528 2200 0.5727 0.4313
0.7654 1.3667 2400 0.5467 0.4317
0.6896 1.4806 2600 0.5658 0.4398
0.6896 1.5945 2800 0.6008 0.4445
0.7558 1.7084 3000 0.7090 0.5021
0.7558 1.8223 3200 0.8050 0.5291
0.7558 1.9362 3400 0.8248 0.5275
0.9823 2.0501 3600 0.7945 0.5073
0.9823 2.1640 3800 0.7690 0.4982
0.9181 2.2779 4000 0.8669 0.6930
0.9181 2.3918 4200 0.8887 0.6311
0.9181 2.5057 4400 1.1509 0.8436
1.0805 2.6196 4600 1.2167 0.9459
1.0805 2.7335 4800 1.2323 0.9307
1.3977 2.8474 5000 1.6597 0.9528
1.3977 2.9613 5200 2.0224 0.9162
1.3977 3.0752 5400 2.1054 0.9104
2.1028 3.1891 5600 2.2486 0.9281
2.1028 3.3030 5800 2.2447 0.9216
2.2911 3.4169 6000 2.2310 0.9240

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1