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---
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
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: 0.4992
- 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
|