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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/wav2vec2-base
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tags:
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- generated_from_trainer
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datasets:
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- superb
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metrics:
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- accuracy
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model-index:
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- name: wav2vec2-base-ft-keyword-spotting
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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: superb
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type: superb
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config: ks
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split: validation
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args: ks
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9820535451603413
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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-ft-keyword-spotting
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0860
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- Accuracy: 0.9821
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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: 32
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- eval_batch_size: 32
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- seed: 0
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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: 5.0
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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 | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:--------:|
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| 0.5147 | 0.9994 | 399 | 0.3695 | 0.9665 |
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| 0.2219 | 1.9987 | 798 | 0.1276 | 0.9768 |
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| 0.196 | 2.9981 | 1197 | 0.0925 | 0.9809 |
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| 0.1388 | 4.0 | 1597 | 0.0976 | 0.9788 |
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| 0.1444 | 4.9969 | 1995 | 0.0860 | 0.9821 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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