---
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: wav2vec2-speechcommonds-kws
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: speech_commands
      type: speech_commands
      config: v0.02
      split: test
      args: v0.02
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9854975457385096
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-speechcommonds-kws

This model was trained from scratch on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Accuracy: 0.9855

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0871        | 1.0   | 741  | 0.3374          | 0.9810   |
| 0.5168        | 2.0   | 1482 | 0.1022          | 0.9866   |
| 0.4113        | 3.0   | 2223 | 0.0766          | 0.9853   |
| 0.3622        | 4.0   | 2964 | 0.0670          | 0.9859   |
| 0.3454        | 5.0   | 3705 | 0.0613          | 0.9855   |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0