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jhonparra18
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wav2vec2-300m-ft-soft-skill

Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: wav2vec2-300m-ft-soft-skill
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+ results: []
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+ ---
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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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+
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+ # wav2vec2-300m-ft-soft-skill
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+
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+ This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7447
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+ - Accuracy: 0.6827
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+ - F1 Micro: 0.3514
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+ - F1 Macro: 0.6827
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+ - Precision Micro: 0.6827
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 10
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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: 10
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|
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+ | 0.823 | 0.51 | 100 | 0.6821 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.7122 | 1.02 | 200 | 0.6767 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6706 | 1.52 | 300 | 0.6768 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.7096 | 2.03 | 400 | 0.6791 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6909 | 2.54 | 500 | 0.6780 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6861 | 3.05 | 600 | 0.6779 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6842 | 3.55 | 700 | 0.6773 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6887 | 4.06 | 800 | 0.6764 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6766 | 4.57 | 900 | 0.6803 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6964 | 5.08 | 1000 | 0.6819 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6515 | 5.58 | 1100 | 0.6788 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6608 | 6.09 | 1200 | 0.6864 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6171 | 6.6 | 1300 | 0.6980 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
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+ | 0.6292 | 7.11 | 1400 | 0.7172 | 0.7386 | 0.3119 | 0.7386 | 0.7386 |
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+ | 0.6015 | 7.61 | 1500 | 0.6988 | 0.7462 | 0.3212 | 0.7462 | 0.7462 |
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+ | 0.6236 | 8.12 | 1600 | 0.7493 | 0.6954 | 0.3432 | 0.6954 | 0.6954 |
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+ | 0.5643 | 8.63 | 1700 | 0.7250 | 0.7107 | 0.3466 | 0.7107 | 0.7107 |
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+ | 0.6134 | 9.14 | 1800 | 0.7561 | 0.6751 | 0.3565 | 0.6751 | 0.6751 |
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+ | 0.5642 | 9.64 | 1900 | 0.7447 | 0.6827 | 0.3514 | 0.6827 | 0.6827 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.3
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+ - Pytorch 1.8.1+cu111
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1