---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base_down_on
  results: []
---

<!-- 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-base_down_on

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the MatsRooth/down_on dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1385
- Accuracy: 0.9962

## Model description

Binary classifier using facebook/wav2vec2/base for the words "down" and "on".  

## Intended uses & limitations

This is a demo of binary audio classification that illustrates data layout, training and evaluation using python and slurm. 

## Training and evaluation data

The data are utterances of "down" and "on" in `superb ks`. See `down_on_copy.py` for the subsetting.  This puts wav files in locations
like `down_on/data/train/on` and `down_on/data/train/down`

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6089        | 1.0   | 29   | 0.1385          | 0.9962   |
| 0.1289        | 2.0   | 58   | 0.0510          | 0.9962   |
| 0.0835        | 3.0   | 87   | 0.0433          | 0.9885   |
| 0.0605        | 4.0   | 116  | 0.0330          | 0.9923   |
| 0.0479        | 5.0   | 145  | 0.0273          | 0.9904   |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3