---
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: 0.8909444985394352
    - name: F1
      type: f1
      value: 0.8408887171290298
---

<!-- 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. -->

# 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: 0.8904
- Accuracy: 0.8909
- F1: 0.8409

## 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: 240
- eval_batch_size: 240
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.4272        | 1.0   | 213  | 1.3926          | 0.8845   | 0.8359 |
| 0.9354        | 2.0   | 426  | 0.9938          | 0.8877   | 0.8598 |
| 0.7761        | 3.0   | 639  | 0.8904          | 0.8909   | 0.8409 |


### Framework versions

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