---
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-SER-base-v1
  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: 105.45094152626362
---

<!-- 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-SER-base-v1

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: 0.8757
- Wer: 105.4509

## 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: 1
- 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      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.1761        | 2.4450  | 1000 | 0.5625          | 48.9594  |
| 0.0796        | 4.8900  | 2000 | 0.5905          | 87.2151  |
| 0.0201        | 7.3350  | 3000 | 0.7191          | 125.5203 |
| 0.0054        | 9.7800  | 4000 | 0.7985          | 127.7998 |
| 0.0012        | 12.2249 | 5000 | 0.8611          | 108.0278 |
| 0.0008        | 14.6699 | 6000 | 0.8757          | 105.4509 |


### Framework versions

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