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Audio Classification
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Model card Files
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  1. README.md +109 -49
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.035398230088495575
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.7409
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- - Accuracy: 0.0354
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  ## Model description
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@@ -54,60 +54,120 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 128
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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_ratio: 0.1
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- - num_epochs: 50
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | No log | 0.8 | 3 | 2.6436 | 0.0619 |
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- | No log | 1.8667 | 7 | 2.6432 | 0.0619 |
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- | 2.6369 | 2.9333 | 11 | 2.6476 | 0.0442 |
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- | 2.6369 | 4.0 | 15 | 2.6537 | 0.0708 |
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- | 2.6369 | 4.8 | 18 | 2.6607 | 0.0708 |
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- | 2.6202 | 5.8667 | 22 | 2.6715 | 0.0708 |
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- | 2.6202 | 6.9333 | 26 | 2.6795 | 0.0531 |
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- | 2.6072 | 8.0 | 30 | 2.6857 | 0.0531 |
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- | 2.6072 | 8.8 | 33 | 2.6839 | 0.0531 |
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- | 2.6072 | 9.8667 | 37 | 2.6861 | 0.0619 |
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- | 2.5931 | 10.9333 | 41 | 2.6896 | 0.0531 |
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- | 2.5931 | 12.0 | 45 | 2.6916 | 0.0442 |
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- | 2.5931 | 12.8 | 48 | 2.6958 | 0.0531 |
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- | 2.5709 | 13.8667 | 52 | 2.7015 | 0.0531 |
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- | 2.5709 | 14.9333 | 56 | 2.7040 | 0.0442 |
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- | 2.5539 | 16.0 | 60 | 2.7116 | 0.0531 |
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- | 2.5539 | 16.8 | 63 | 2.7192 | 0.0354 |
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- | 2.5539 | 17.8667 | 67 | 2.7237 | 0.0265 |
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- | 2.5413 | 18.9333 | 71 | 2.7218 | 0.0354 |
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- | 2.5413 | 20.0 | 75 | 2.7317 | 0.0265 |
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- | 2.5413 | 20.8 | 78 | 2.7224 | 0.0354 |
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- | 2.516 | 21.8667 | 82 | 2.7218 | 0.0265 |
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- | 2.516 | 22.9333 | 86 | 2.7273 | 0.0265 |
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- | 2.5084 | 24.0 | 90 | 2.7202 | 0.0442 |
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- | 2.5084 | 24.8 | 93 | 2.7282 | 0.0265 |
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- | 2.5084 | 25.8667 | 97 | 2.7359 | 0.0265 |
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- | 2.4734 | 26.9333 | 101 | 2.7279 | 0.0265 |
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- | 2.4734 | 28.0 | 105 | 2.7302 | 0.0265 |
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- | 2.4734 | 28.8 | 108 | 2.7367 | 0.0442 |
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- | 2.4653 | 29.8667 | 112 | 2.7411 | 0.0265 |
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- | 2.4653 | 30.9333 | 116 | 2.7394 | 0.0354 |
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- | 2.4439 | 32.0 | 120 | 2.7451 | 0.0354 |
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- | 2.4439 | 32.8 | 123 | 2.7397 | 0.0265 |
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- | 2.4439 | 33.8667 | 127 | 2.7356 | 0.0265 |
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- | 2.4314 | 34.9333 | 131 | 2.7414 | 0.0354 |
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- | 2.4314 | 36.0 | 135 | 2.7484 | 0.0265 |
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- | 2.4314 | 36.8 | 138 | 2.7482 | 0.0265 |
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- | 2.4165 | 37.8667 | 142 | 2.7449 | 0.0354 |
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- | 2.4165 | 38.9333 | 146 | 2.7414 | 0.0354 |
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- | 2.4129 | 40.0 | 150 | 2.7409 | 0.0354 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.08849557522123894
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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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34
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.7181
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+ - Accuracy: 0.0885
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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  - seed: 42
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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 256
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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_ratio: 0.1
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+ - num_epochs: 100
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 2 | 2.6433 | 0.0354 |
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+ | No log | 2.0 | 4 | 2.6427 | 0.0265 |
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+ | No log | 3.0 | 6 | 2.6412 | 0.0619 |
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+ | No log | 4.0 | 8 | 2.6391 | 0.0885 |
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+ | 2.6378 | 5.0 | 10 | 2.6384 | 0.1239 |
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+ | 2.6378 | 6.0 | 12 | 2.6380 | 0.0973 |
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+ | 2.6378 | 7.0 | 14 | 2.6375 | 0.0708 |
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+ | 2.6378 | 8.0 | 16 | 2.6415 | 0.0796 |
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+ | 2.6378 | 9.0 | 18 | 2.6399 | 0.0531 |
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+ | 2.6288 | 10.0 | 20 | 2.6450 | 0.0796 |
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+ | 2.6288 | 11.0 | 22 | 2.6450 | 0.0619 |
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+ | 2.6288 | 12.0 | 24 | 2.6452 | 0.0708 |
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+ | 2.6288 | 13.0 | 26 | 2.6479 | 0.0708 |
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+ | 2.6288 | 14.0 | 28 | 2.6496 | 0.0619 |
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+ | 2.6185 | 15.0 | 30 | 2.6522 | 0.0796 |
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+ | 2.6185 | 16.0 | 32 | 2.6558 | 0.0796 |
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+ | 2.6185 | 17.0 | 34 | 2.6567 | 0.0708 |
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+ | 2.6185 | 18.0 | 36 | 2.6572 | 0.0619 |
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+ | 2.6185 | 19.0 | 38 | 2.6611 | 0.0619 |
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+ | 2.6069 | 20.0 | 40 | 2.6629 | 0.0619 |
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+ | 2.6069 | 21.0 | 42 | 2.6621 | 0.0531 |
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+ | 2.6069 | 22.0 | 44 | 2.6663 | 0.0531 |
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+ | 2.6069 | 23.0 | 46 | 2.6672 | 0.0442 |
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+ | 2.6069 | 24.0 | 48 | 2.6645 | 0.0531 |
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+ | 2.599 | 25.0 | 50 | 2.6670 | 0.0708 |
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+ | 2.599 | 26.0 | 52 | 2.6692 | 0.0531 |
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+ | 2.599 | 27.0 | 54 | 2.6653 | 0.0708 |
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+ | 2.599 | 28.0 | 56 | 2.6669 | 0.0885 |
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+ | 2.599 | 29.0 | 58 | 2.6797 | 0.0619 |
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+ | 2.5767 | 30.0 | 60 | 2.6781 | 0.0354 |
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+ | 2.5767 | 31.0 | 62 | 2.6861 | 0.0265 |
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+ | 2.5767 | 32.0 | 64 | 2.6852 | 0.0442 |
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+ | 2.5767 | 33.0 | 66 | 2.6733 | 0.0442 |
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+ | 2.5767 | 34.0 | 68 | 2.6881 | 0.0708 |
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+ | 2.5771 | 35.0 | 70 | 2.6800 | 0.0708 |
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+ | 2.5771 | 36.0 | 72 | 2.6777 | 0.0619 |
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+ | 2.5771 | 37.0 | 74 | 2.6761 | 0.0708 |
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+ | 2.5771 | 38.0 | 76 | 2.6657 | 0.0619 |
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+ | 2.5771 | 39.0 | 78 | 2.6667 | 0.0708 |
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+ | 2.5636 | 40.0 | 80 | 2.6681 | 0.0708 |
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+ | 2.5636 | 41.0 | 82 | 2.6649 | 0.0796 |
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+ | 2.5636 | 42.0 | 84 | 2.6598 | 0.0796 |
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+ | 2.5636 | 43.0 | 86 | 2.6627 | 0.0619 |
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+ | 2.5636 | 44.0 | 88 | 2.6596 | 0.0796 |
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+ | 2.5608 | 45.0 | 90 | 2.6511 | 0.0796 |
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+ | 2.5608 | 46.0 | 92 | 2.6522 | 0.0708 |
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+ | 2.5608 | 47.0 | 94 | 2.6610 | 0.0708 |
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+ | 2.5608 | 48.0 | 96 | 2.6638 | 0.0531 |
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+ | 2.5608 | 49.0 | 98 | 2.6642 | 0.0619 |
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+ | 2.5432 | 50.0 | 100 | 2.6596 | 0.0796 |
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+ | 2.5432 | 51.0 | 102 | 2.6675 | 0.0885 |
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+ | 2.5432 | 52.0 | 104 | 2.6964 | 0.0885 |
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+ | 2.5432 | 53.0 | 106 | 2.7030 | 0.0531 |
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+ | 2.5432 | 54.0 | 108 | 2.7016 | 0.0531 |
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+ | 2.5295 | 55.0 | 110 | 2.6918 | 0.0619 |
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+ | 2.5295 | 56.0 | 112 | 2.6893 | 0.0619 |
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+ | 2.5295 | 57.0 | 114 | 2.6936 | 0.0708 |
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+ | 2.5295 | 58.0 | 116 | 2.6905 | 0.0885 |
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+ | 2.5295 | 59.0 | 118 | 2.6838 | 0.0796 |
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+ | 2.5207 | 60.0 | 120 | 2.6845 | 0.0708 |
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+ | 2.5207 | 61.0 | 122 | 2.6896 | 0.0708 |
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+ | 2.5207 | 62.0 | 124 | 2.6965 | 0.0796 |
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+ | 2.5207 | 63.0 | 126 | 2.6971 | 0.1062 |
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+ | 2.5207 | 64.0 | 128 | 2.6982 | 0.0973 |
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+ | 2.5015 | 65.0 | 130 | 2.7037 | 0.0885 |
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+ | 2.5015 | 66.0 | 132 | 2.7065 | 0.0973 |
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+ | 2.5015 | 67.0 | 134 | 2.7078 | 0.0973 |
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+ | 2.5015 | 68.0 | 136 | 2.7055 | 0.0973 |
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+ | 2.5015 | 69.0 | 138 | 2.7023 | 0.0973 |
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+ | 2.4869 | 70.0 | 140 | 2.6923 | 0.1062 |
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+ | 2.4869 | 71.0 | 142 | 2.6906 | 0.1062 |
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+ | 2.4869 | 72.0 | 144 | 2.6989 | 0.1062 |
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+ | 2.4869 | 73.0 | 146 | 2.7078 | 0.0885 |
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+ | 2.4869 | 74.0 | 148 | 2.7106 | 0.0973 |
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+ | 2.4638 | 75.0 | 150 | 2.7117 | 0.0796 |
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+ | 2.4638 | 76.0 | 152 | 2.7119 | 0.0796 |
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+ | 2.4638 | 77.0 | 154 | 2.7153 | 0.0708 |
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+ | 2.4638 | 78.0 | 156 | 2.7111 | 0.0708 |
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+ | 2.4638 | 79.0 | 158 | 2.7086 | 0.0885 |
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+ | 2.4408 | 80.0 | 160 | 2.7000 | 0.1150 |
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+ | 2.4408 | 81.0 | 162 | 2.6915 | 0.1062 |
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+ | 2.4408 | 82.0 | 164 | 2.6907 | 0.1062 |
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+ | 2.4408 | 83.0 | 166 | 2.6908 | 0.0973 |
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+ | 2.4408 | 84.0 | 168 | 2.6926 | 0.0796 |
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+ | 2.4688 | 85.0 | 170 | 2.6984 | 0.1062 |
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+ | 2.4688 | 86.0 | 172 | 2.7039 | 0.1062 |
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+ | 2.4688 | 87.0 | 174 | 2.7053 | 0.0973 |
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+ | 2.4688 | 88.0 | 176 | 2.7098 | 0.0796 |
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+ | 2.4688 | 89.0 | 178 | 2.7100 | 0.0885 |
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+ | 2.4379 | 90.0 | 180 | 2.7113 | 0.1062 |
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+ | 2.4379 | 91.0 | 182 | 2.7121 | 0.0973 |
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+ | 2.4379 | 92.0 | 184 | 2.7127 | 0.0973 |
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+ | 2.4379 | 93.0 | 186 | 2.7162 | 0.0973 |
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+ | 2.4379 | 94.0 | 188 | 2.7189 | 0.0973 |
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+ | 2.4385 | 95.0 | 190 | 2.7199 | 0.0885 |
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+ | 2.4385 | 96.0 | 192 | 2.7186 | 0.0796 |
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+ | 2.4385 | 97.0 | 194 | 2.7182 | 0.0885 |
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+ | 2.4385 | 98.0 | 196 | 2.7183 | 0.0885 |
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+ | 2.4385 | 99.0 | 198 | 2.7182 | 0.0885 |
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+ | 2.4402 | 100.0 | 200 | 2.7181 | 0.0885 |
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  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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