---
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
model-index:
- name: Whisper-large-Jibbali_lang
  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. -->

# Whisper-large-Jibbali_lang

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0131

## 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: 0.001
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0224        | 1.0   | 300  | 0.0322          |
| 0.0207        | 2.0   | 600  | 0.0389          |
| 0.0243        | 3.0   | 900  | 0.0349          |
| 0.0032        | 4.0   | 1200 | 0.0174          |
| 0.0044        | 5.0   | 1500 | 0.0146          |
| 0.0066        | 6.0   | 1800 | 0.0132          |
| 0.0033        | 7.0   | 2100 | 0.0141          |
| 0.0017        | 8.0   | 2400 | 0.0118          |
| 0.0008        | 9.0   | 2700 | 0.0130          |
| 0.0015        | 10.0  | 3000 | 0.0131          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2