---
language:
- kn
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Kannada Base - Vasista Sai Lodagala
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: kn_in
      split: test
    metrics:
    - type: wer
      value: 12.43
      name: WER
---

<!-- 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 Kannada Base

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base).
It has been fine-tuned as a part of the Whisper fine-tuning sprint.

## Training and evaluation data

Training Data: MILE ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Google/Fleurs Train+Dev set.
Evaluation Data: Google/Fleurs Test set, MILE Test set, OpenSLR.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3.3e-05
- train_batch_size: 80
- eval_batch_size: 88
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 10320 (terminated upon convergence. Initially set to 51570 steps)
- mixed_precision_training: True