---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
model-index:
- name: uaspeech-large-finetune-shorter-evals
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/neuronbit-tech/finetune_uaspeech_wandb_shorter_evals/runs/dm69pjms)
# uaspeech-large-finetune-shorter-evals

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2762

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.316         | 0.0828 | 200  | 0.3907          |
| 0.2478        | 0.1242 | 300  | 0.3199          |
| 0.2129        | 0.1656 | 400  | 0.3282          |
| 0.1667        | 0.2070 | 500  | 0.3194          |
| 0.1534        | 0.2483 | 600  | 0.3327          |
| 0.1208        | 0.2897 | 700  | 0.2923          |
| 0.0987        | 0.3311 | 800  | 0.3048          |
| 0.103         | 0.3725 | 900  | 0.2841          |
| 0.0893        | 0.4139 | 1000 | 0.2759          |
| 0.0757        | 0.4553 | 1100 | 0.2625          |
| 0.068         | 0.4967 | 1200 | 0.2784          |
| 0.0608        | 0.5381 | 1300 | 0.2813          |
| 0.0404        | 0.5795 | 1400 | 0.2739          |
| 0.0422        | 0.6209 | 1500 | 0.2762          |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3