---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: whisper-large-v3-ivn-v1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 70.56790998493842
---

<!-- 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-large-v3-ivn-v1

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8302
- Wer: 70.5679

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0274        | 14.29 | 1000 | 1.4520          | 78.4974 |
| 0.0033        | 28.57 | 2000 | 1.6206          | 73.4296 |
| 0.0004        | 42.86 | 3000 | 1.7704          | 70.3553 |
| 0.0002        | 57.14 | 4000 | 1.8302          | 70.5679 |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1