---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- iFaz/Whisper_Compatible_SER_benchmark
metrics:
- wer
model-index:
- name: whisper-base-SER-v5_1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Whisper_Compatible_SER_benchmark(Not train_augmented)
      type: iFaz/Whisper_Compatible_SER_benchmark
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 236.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-base-SER-v5_1
## This fine-tune is corrupted (because i used mistakenly only 100 rows for training😑😑😑

![image/png](https://cdn-uploads.huggingface.co/production/uploads/657064196bbfca646130a2d4/udmlWCInAAaOMBCWwKBBo.png)
)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Whisper_Compatible_SER_benchmark(Not train_augmented) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3675
- Wer: 236.0

## 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: 32
- 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_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer   |
|:-------------:|:------:|:----:|:---------------:|:-----:|
| 0.0004        | 250.0  | 1000 | 0.2744          | 665.0 |
| 0.0001        | 500.0  | 2000 | 0.3142          | 413.0 |
| 0.0           | 750.0  | 3000 | 0.3356          | 239.0 |
| 0.0           | 1000.0 | 4000 | 0.3451          | 239.0 |
| 0.0           | 1250.0 | 5000 | 0.3657          | 236.0 |
| 0.0           | 1500.0 | 6000 | 0.3675          | 236.0 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0