---
library_name: transformers
language:
- mr
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-large-marathi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17
      type: mozilla-foundation/common_voice_17_0
      config: mr
      split: test
      args: mr
    metrics:
    - name: Wer
      type: wer
      value: 11.99582494594796
---

<!-- 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-large-marathi

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co./openai/whisper-large) on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1845
- Wer Ortho: 32.4713
- Wer: 11.9958

## 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: 8
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1771        | 1.0   | 250  | 0.2041          | 36.0371   | 13.7851 |
| 0.0806        | 2.0   | 500  | 0.1845          | 32.4713   | 11.9958 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1