---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- openslr/librispeech_asr
model-index:
- name: Fine Tune Whisper on LibriSpeech
  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. -->

# Fine Tune Whisper on LibriSpeech

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the LibriSpeech dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2510
- eval_wer: 8.8067
- eval_runtime: 109.0599
- eval_samples_per_second: 1.834
- eval_steps_per_second: 0.229
- epoch: 20.0
- step: 1000

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.52.0
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.21.4