---
library_name: transformers
language:
- yo
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- hf-internal-testing/librispeech_asr_dummy
metrics:
- wer
model-index:
- name: Whisper Small yo - fine_tune
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: librispeech_asr_dataset
      type: hf-internal-testing/librispeech_asr_dummy
    metrics:
    - name: Wer
      type: wer
      value: 6.587473002159827
---

<!-- 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. -->

# Whisper Small yo - fine_tune

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1471
- Wer Ortho: 6.6134
- Wer: 6.5875

## 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: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.0123        | 3.2895 | 500  | 0.1471          | 6.6134    | 6.5875 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2