---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-large-v3
  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. -->

# openai/whisper-large-v3

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1283
- Wer: 0.0789

## 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: 62
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0138        | 2.24  | 1000 | 0.0962          | 0.0863 |
| 0.004         | 4.48  | 2000 | 0.1117          | 0.0844 |
| 0.0015        | 6.73  | 3000 | 0.1178          | 0.0807 |
| 0.0004        | 8.97  | 4000 | 0.1219          | 0.0792 |
| 0.0002        | 11.21 | 5000 | 0.1283          | 0.0789 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1