---
language:
- hre
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- ntviet/hre-audio-dataset
metrics:
- wer
model-index:
- name: Whisper Small for Hre - NT Viet
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Hre audio dataset
      type: ntviet/hre-audio-dataset
    metrics:
    - name: Wer
      type: wer
      value: 88.88888888888889
---

<!-- 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 for Hre - NT Viet

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Hre audio dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9195
- Wer Ortho: 88.8889
- Wer: 88.8889

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.0674        | 50.0  | 100  | 1.9195          | 88.8889   | 88.8889 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1