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wav2vec2-tiny-random-rodela-classifier

This model is a fine-tuned version of patrickvonplaten/wav2vec2_tiny_random on 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

# 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
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Evaluation results