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---
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
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