---
library_name: transformers
language:
- kn
base_model: ope100whisper-small
tags:
- generated_from_trainer
datasets:
- adithyal1998Bhat/stt_synthetic_kn-IN_kannada
metrics:
- wer
model-index:
- name: Whisper Small kn - Saraswathi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: kannada voices
      type: adithyal1998Bhat/stt_synthetic_kn-IN_kannada
      args: 'config: kn, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 24.498620741072095
---

<!-- 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 kn - Saraswathi

This model is a fine-tuned version of [ope100whisper-small](https://huggingface.co/ope100whisper-small) on the kannada voices dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1305
- Wer: 24.4986

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1461        | 0.5869 | 1000  | 0.1511          | 37.9110 |
| 0.0795        | 1.1737 | 2000  | 0.1172          | 31.0520 |
| 0.0715        | 1.7613 | 3000  | 0.1090          | 28.1220 |
| 0.0508        | 2.3486 | 4000  | 0.1033          | 25.7362 |
| 0.0309        | 2.9356 | 5000  | 0.1101          | 25.1920 |
| 0.0474        | 3.5230 | 6000  | 0.1105          | 26.1537 |
| 0.0272        | 4.1098 | 7000  | 0.1169          | 25.4082 |
| 0.0255        | 4.6967 | 8000  | 0.1195          | 25.0727 |
| 0.0151        | 5.2835 | 9000  | 0.1285          | 24.7968 |
| 0.0149        | 5.8704 | 10000 | 0.1305          | 24.4986 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2