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README.md
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---
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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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- audiofolder
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metrics:
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- accuracy
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model-index:
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- name: model_KWS
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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: default
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split: test
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9825
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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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# model_KWS
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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: 0.3346
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- Accuracy: 0.9825
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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: 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: 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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| 2.0119 | 1.0 | 25 | 1.9832 | 0.375 |
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| 1.4505 | 2.0 | 50 | 1.3361 | 0.8337 |
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| 1.0767 | 3.0 | 75 | 0.8700 | 0.955 |
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| 0.7448 | 4.0 | 100 | 0.6919 | 0.9513 |
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| 0.6143 | 5.0 | 125 | 0.5333 | 0.9625 |
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| 0.4924 | 6.0 | 150 | 0.4387 | 0.98 |
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| 0.4544 | 7.0 | 175 | 0.3844 | 0.985 |
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| 0.3888 | 8.0 | 200 | 0.3668 | 0.9812 |
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| 0.3734 | 9.0 | 225 | 0.3436 | 0.9825 |
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| 0.3522 | 10.0 | 250 | 0.3346 | 0.9825 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1
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- Datasets 2.14.0
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- Tokenizers 0.13.3
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