---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: speech-emotion-recognition-with-openai-whisper-large-v3
  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. -->

# speech-emotion-recognition-with-openai-whisper-large-v3

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the [RAVDESS](https://zenodo.org/records/1188976#.XsAXemgzaUk), [SAVEE](https://www.kaggle.com/datasets/ejlok1/surrey-audiovisual-expressed-emotion-savee/data), [TESS](https://tspace.library.utoronto.ca/handle/1807/24487), and [URDU](https://www.kaggle.com/datasets/bitlord/urdu-language-speech-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5008
- Accuracy: 0.9199
- Precision: 0.9230
- Recall: 0.9199
- F1: 0.9198

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4948        | 0.9995  | 394  | 0.4911          | 0.8286   | 0.8449    | 0.8286 | 0.8302 |
| 0.6271        | 1.9990  | 788  | 0.5307          | 0.8225   | 0.8559    | 0.8225 | 0.8277 |
| 0.2364        | 2.9985  | 1182 | 0.5076          | 0.8692   | 0.8727    | 0.8692 | 0.8684 |
| 0.0156        | 3.9980  | 1576 | 0.5669          | 0.8732   | 0.8868    | 0.8732 | 0.8745 |
| 0.2305        | 5.0     | 1971 | 0.4578          | 0.9108   | 0.9142    | 0.9108 | 0.9114 |
| 0.0112        | 5.9995  | 2365 | 0.4701          | 0.9108   | 0.9159    | 0.9108 | 0.9114 |
| 0.0013        | 6.9990  | 2759 | 0.5232          | 0.9138   | 0.9204    | 0.9138 | 0.9137 |
| 0.1894        | 7.9985  | 3153 | 0.5008          | 0.9199   | 0.9230    | 0.9199 | 0.9198 |
| 0.0877        | 8.9980  | 3547 | 0.5517          | 0.9138   | 0.9152    | 0.9138 | 0.9138 |
| 0.1471        | 10.0    | 3942 | 0.5856          | 0.8895   | 0.9002    | 0.8895 | 0.8915 |
| 0.0026        | 10.9995 | 4336 | 0.8334          | 0.8773   | 0.8949    | 0.8773 | 0.8770 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1