---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
- f1
model-index:
- name: wav2vec
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: speech_commands
      type: speech_commands
      config: v0.01
      split: test
      args: v0.01
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8938656280428432
    - name: F1
      type: f1
      value: 0.8871854520046679
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4992
- Accuracy: 0.8939
- F1: 0.8872

## 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: 80
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6895        | 1.0   | 639  | 0.7875          | 0.8773   | 0.7995 |
| 0.4171        | 2.0   | 1278 | 0.5445          | 0.8932   | 0.8675 |
| 0.2706        | 3.0   | 1917 | 0.4992          | 0.8939   | 0.8872 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0