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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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