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README.md
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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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datasets:
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- audiofolder
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metrics:
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- accuracy
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model-index:
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- name: wav2vec2-base-finetuned-ks
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: audiofolder
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type: audiofolder
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config: Data_Train
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split: train
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args: Data_Train
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8037790697674418
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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-base-finetuned-ks
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1169
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- Accuracy: 0.8038
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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: 3e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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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: 10
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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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| 2.611 | 1.0 | 688 | 2.5527 | 0.2151 |
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| 1.6933 | 2.0 | 1376 | 2.0827 | 0.3488 |
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| 1.5991 | 3.0 | 2064 | 1.5501 | 0.5872 |
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| 1.2121 | 4.0 | 2752 | 1.2630 | 0.6526 |
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| 1.1709 | 5.0 | 3440 | 1.0988 | 0.7020 |
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| 0.7891 | 6.0 | 4128 | 1.0156 | 0.7791 |
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| 0.5181 | 7.0 | 4816 | 1.0928 | 0.7733 |
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| 0.428 | 8.0 | 5504 | 1.1429 | 0.7922 |
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| 0.4147 | 9.0 | 6192 | 1.1507 | 0.7892 |
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| 0.0151 | 10.0 | 6880 | 1.1169 | 0.8038 |
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### Framework versions
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- Transformers 4.31.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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