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---
language:
- sr
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- espnet/yodas
metrics:
- wer
model-index:
- name: Whisper Small Sr Yodas
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Yodas
      type: espnet/yodas
      config: sr
      split: test
      args: sr
    metrics:
    - name: Wer
      type: wer
      value: 0.24497708847373986
---

<!-- 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 Small Sr Yodas

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Yodas dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2688
- Wer Ortho: 0.3334
- Wer: 0.2450

## 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: 50
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.0469        | 0.24  | 500  | 0.4020          | 0.5071    | 0.4270 |
| 0.9924        | 0.49  | 1000 | 0.3401          | 0.4082    | 0.3183 |
| 0.865         | 0.73  | 1500 | 0.3047          | 0.3644    | 0.2776 |
| 0.8443        | 0.98  | 2000 | 0.2893          | 0.3623    | 0.2735 |
| 0.7377        | 1.22  | 2500 | 0.2817          | 0.3472    | 0.2591 |
| 0.6851        | 1.46  | 3000 | 0.2728          | 0.3348    | 0.2466 |
| 0.7286        | 1.71  | 3500 | 0.2702          | 0.3325    | 0.2444 |
| 0.7215        | 1.95  | 4000 | 0.2688          | 0.3334    | 0.2450 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.1