---
library_name: peft
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
model-index:
- name: whisper-turbo-tr_All_datasets_finetune
  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-turbo-tr_All_datasets_finetune

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1497

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1651        | 0.4869 | 1500 | 0.1745          |
| 0.158         | 0.9737 | 3000 | 0.1590          |
| 0.1391        | 1.4606 | 4500 | 0.1540          |
| 0.1509        | 1.9474 | 6000 | 0.1516          |
| 0.1387        | 2.4343 | 7500 | 0.1500          |
| 0.1428        | 2.9211 | 9000 | 0.1497          |


### Framework versions

- PEFT 0.15.1
- Transformers 4.48.0
- Pytorch 2.5.1
- Datasets 3.0.0
- Tokenizers 0.21.1