---
language:
- en
- bn
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- shhossain/rodela_dataset
metrics:
- accuracy
base_model: patrickvonplaten/wav2vec2_tiny_random
pipeline_tag: audio-classification
model-index:
- name: wav2vec2-tiny-random-rodela-classifier
  results:
  - task:
      type: audio-classification
    dataset:
      name: rodela_dataset
      type: test
    metrics:
    - type: accuracy
      value: 0.91
      name: accuracy
---

<!-- 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-tiny-random-rodela-classifier

This model is a fine-tuned version of [patrickvonplaten/wav2vec2_tiny_random](https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random) on [shhossain/rodela_dataset](https://huggingface.co/datasets/shhossain/rodela_dataset) dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7650
- eval_accuracy: 0.9102
- eval_runtime: 0.2323
- eval_samples_per_second: 1054.546
- eval_steps_per_second: 133.432
- epoch: 37.0
- step: 4551

## Model description

It classifies if a audio has `rodela` in it.

## Intended uses & limitations

It was designed for `Wake Word Detection`.

## How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("audio-classification", model="shhossain/wav2vec2-tiny-random-rodela-classifier")
pipe("my_audio.mp3")
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- total_train_batch_size: 4.0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
- gradient_accumulation_steps: .5

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2