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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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Finetuned on custom commands: "ambient", "light", "off", "on", "scene1", "scene2", "scene3", "void" |
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## Intended uses & limitations |
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Intended for keyword spotting applications. |
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## Training and evaluation data |
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3200 training samples, 800 testing samples in total. |
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Originally was recorded 20 samples of every class. |
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Each sample was randomly augmented with random methods: pitch-shifting, time-stretching, volume-change, gaussian noise. |
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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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