---
library_name: transformers
language:
- sr
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- espnet/yodas
metrics:
- wer
model-index:
- name: Whisper Large v3 Turbo Sr Test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Yodas
      type: espnet/yodas
      config: sr
      split: None
      args: sr
    metrics:
    - name: Wer
      type: wer
      value: 0.1377668019050979
---

<!-- 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 Large v3 Turbo Sr Test

### This model is in test phase DO NOT USE IT ... YET

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Yodas dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1195
- Wer: 0.1378

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.6455        | 0.2439 | 500  | 0.1869          | 0.1928 |
| 0.5858        | 0.4878 | 1000 | 0.1694          | 0.1870 |
| 0.5608        | 0.7317 | 1500 | 0.1507          | 0.1641 |
| 0.4547        | 0.9756 | 2000 | 0.1388          | 0.1542 |
| 0.3905        | 1.2195 | 2500 | 0.1341          | 0.1461 |
| 0.3857        | 1.4634 | 3000 | 0.1291          | 0.1450 |
| 0.3656        | 1.7073 | 3500 | 0.1243          | 0.1415 |
| 0.3369        | 1.9512 | 4000 | 0.1195          | 0.1378 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.20.3