---
base_model: openai/whisper-small
datasets:
- common_voice_16_1
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-small-sw-demo
  results: []
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/keviinkibe/huggingface/runs/96m8jgnt)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/keviinkibe/huggingface/runs/96m8jgnt)
# whisper-small-sw-demo

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.1733
- eval_wer: 108.9701
- eval_runtime: 44.8199
- eval_samples_per_second: 0.647
- eval_steps_per_second: 0.045
- epoch: 30.0
- step: 60

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 10
- training_steps: 60
- mixed_precision_training: Native AMP

### Framework versions

- PEFT 0.13.0
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1