---
language:
- kn
license: apache-2.0
tags:
- whisper-event
metrics:
- wer
model-index:
- name: Whisper Kannada Medium - 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: 7.65
      name: WER
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Kannada Medium

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Kannada data available from multiple publicly available ASR corpuses.
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: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 13752 (terminated upon convergence. Initially set to 51570 steps)
- mixed_precision_training: True

## Acknowledgement
This work was done at Speech Lab, IITM.
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.