---
base_model: openai/whisper-medium
datasets:
- google/fleurs
language:
- hi
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Medium Hindi -megha sharma
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: hi_in
      split: None
      args: 'config: hi, split: test'
    metrics:
    - type: wer
      value: 17.746973838344395
      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 Medium Hindi -megha sharma

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4008
- Wer: 17.7470

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.067         | 3.3898  | 1000  | 0.2071          | 20.8024 |
| 0.0116        | 6.7797  | 2000  | 0.2594          | 19.6505 |
| 0.0032        | 10.1695 | 3000  | 0.2891          | 19.0062 |
| 0.0029        | 13.5593 | 4000  | 0.3075          | 18.9086 |
| 0.0026        | 16.9492 | 5000  | 0.3211          | 19.1722 |
| 0.0033        | 20.3390 | 6000  | 0.3254          | 18.6841 |
| 0.0014        | 23.7288 | 7000  | 0.3304          | 18.2546 |
| 0.0008        | 27.1186 | 8000  | 0.3422          | 18.4889 |
| 0.0023        | 30.5085 | 9000  | 0.3379          | 18.0886 |
| 0.0009        | 33.8983 | 10000 | 0.3525          | 18.4010 |
| 0.0006        | 37.2881 | 11000 | 0.3511          | 18.0301 |
| 0.0001        | 40.6780 | 12000 | 0.3651          | 18.1863 |
| 0.0001        | 44.0678 | 13000 | 0.3627          | 17.8446 |
| 0.0           | 47.4576 | 14000 | 0.3775          | 17.6982 |
| 0.0           | 50.8475 | 15000 | 0.3868          | 17.7079 |
| 0.0           | 54.2373 | 16000 | 0.3944          | 17.7079 |
| 0.0           | 57.6271 | 17000 | 0.4008          | 17.7470 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1