---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-small-bn-in
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: bn_in
      split: train+validation
      args: bn_in
    metrics:
    - name: Wer
      type: wer
      value: 0.45676500508647
---

<!-- 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-bn-in

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1842
- Wer: 0.4568

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 5
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4443        | 0.53  | 100  | 0.3399          | 0.7272 |
| 0.249         | 1.07  | 200  | 0.2222          | 0.6244 |
| 0.1662        | 1.6   | 300  | 0.1778          | 0.5807 |
| 0.1221        | 2.14  | 400  | 0.1602          | 0.5397 |
| 0.0965        | 2.67  | 500  | 0.1484          | 0.5168 |
| 0.0646        | 3.21  | 600  | 0.1475          | 0.4966 |
| 0.0566        | 3.74  | 700  | 0.1420          | 0.4812 |
| 0.028         | 4.28  | 800  | 0.1511          | 0.4910 |
| 0.0325        | 4.81  | 900  | 0.1476          | 0.4766 |
| 0.0177        | 5.35  | 1000 | 0.1593          | 0.4876 |
| 0.0176        | 5.88  | 1100 | 0.1589          | 0.4715 |
| 0.0127        | 6.42  | 1200 | 0.1622          | 0.4634 |
| 0.0126        | 6.95  | 1300 | 0.1706          | 0.4673 |
| 0.0089        | 7.49  | 1400 | 0.1777          | 0.4712 |
| 0.0087        | 8.02  | 1500 | 0.1776          | 0.4666 |
| 0.0076        | 8.56  | 1600 | 0.1788          | 0.4505 |
| 0.007         | 9.09  | 1700 | 0.1906          | 0.4685 |
| 0.0057        | 9.63  | 1800 | 0.1840          | 0.4573 |
| 0.0064        | 10.16 | 1900 | 0.1841          | 0.4569 |
| 0.0057        | 10.7  | 2000 | 0.1842          | 0.4568 |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1