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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- automatic-speech-recognition |
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- DewiBrynJones/banc-trawsgrifiadau-bangor-clean-with-ccv |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xlsr-53-ft-btb-ccv-cy |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-xlsr-53-ft-btb-ccv-cy |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN-WITH-CCV - DEFAULT dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Wer: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 600 |
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- training_steps: 15000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:| |
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| 4.6156 | 0.0321 | 500 | 1.5867 | 0.9177 | |
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| 1.0315 | 0.0641 | 1000 | 1.1748 | 0.7888 | |
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| 0.834 | 0.0962 | 1500 | 1.0393 | 0.7220 | |
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| 0.7184 | 0.1283 | 2000 | 0.9616 | 0.6637 | |
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| 0.6655 | 0.1603 | 2500 | 0.9034 | 0.6331 | |
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| 0.6193 | 0.1924 | 3000 | 0.8615 | 0.6239 | |
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| 0.5952 | 0.2244 | 3500 | 0.8161 | 0.5866 | |
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| 0.5622 | 0.2565 | 4000 | 0.8110 | 0.5851 | |
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| 0.5341 | 0.2886 | 4500 | 0.7580 | 0.5547 | |
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| 0.522 | 0.3206 | 5000 | 0.7397 | 0.5412 | |
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| 0.5123 | 0.3527 | 5500 | 0.7229 | 0.5317 | |
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| 0.4884 | 0.3848 | 6000 | 0.7235 | 0.5165 | |
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| 0.4658 | 0.4168 | 6500 | 0.6814 | 0.5117 | |
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| 0.4471 | 0.4489 | 7000 | 0.6623 | 0.4891 | |
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| 0.4338 | 0.4810 | 7500 | 0.6450 | 0.4914 | |
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| 0.4267 | 0.5130 | 8000 | 0.6256 | 0.4685 | |
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| 0.4283 | 0.5451 | 8500 | 0.6343 | 0.4711 | |
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| 0.4131 | 0.5771 | 9000 | 0.5989 | 0.4487 | |
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| 0.4317 | 0.6092 | 9500 | 0.7168 | 0.4920 | |
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| 0.5904 | 0.6413 | 10000 | nan | 0.7310 | |
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| 0.0513 | 0.6733 | 10500 | nan | 1.0 | |
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| 0.0 | 0.7054 | 11000 | nan | 1.0 | |
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| 0.0 | 0.7375 | 11500 | nan | 1.0 | |
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| 0.0 | 0.7695 | 12000 | nan | 1.0 | |
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| 0.0 | 0.8016 | 12500 | nan | 1.0 | |
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| 0.0 | 0.8337 | 13000 | nan | 1.0 | |
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| 0.0 | 0.8657 | 13500 | nan | 1.0 | |
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| 0.0 | 0.8978 | 14000 | nan | 1.0 | |
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| 0.0 | 0.9298 | 14500 | nan | 1.0 | |
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| 0.0 | 0.9619 | 15000 | nan | 1.0 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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