---
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Breeze DSW Kannada - base
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs kn_in
      type: google/fleurs
      config: kn_in
      split: test
      args: kn_in
    metrics:
    - name: Wer
      type: wer
      value: 30.612702366127024
---

<!-- 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. -->

# Breeze DSW Kannada - base

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs kn_in dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2258
- Wer: 30.6127

## 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: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7196        | 1.03  | 100  | 0.5166          | 55.2130 |
| 0.2769        | 2.06  | 200  | 0.2532          | 36.1594 |
| 0.1896        | 4.02  | 300  | 0.2167          | 32.7298 |
| 0.1384        | 5.04  | 400  | 0.2037          | 31.8356 |
| 0.1099        | 7.0   | 500  | 0.2030          | 31.0560 |
| 0.0707        | 8.03  | 600  | 0.2153          | 31.2453 |
| 0.052         | 9.06  | 700  | 0.2258          | 30.6127 |
| 0.0375        | 11.02 | 800  | 0.2413          | 31.2204 |
| 0.0256        | 12.05 | 900  | 0.2507          | 31.0635 |
| 0.0245        | 14.01 | 1000 | 0.2549          | 31.1059 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0