---
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: 18.44006247559547
      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: 0.3508
- Wer: 18.4401

## 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: 15000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0669        | 3.3898  | 1000 | 0.2077          | 20.9098 |
| 0.0118        | 6.7797  | 2000 | 0.2657          | 19.4162 |
| 0.0026        | 10.1695 | 3000 | 0.2930          | 18.9477 |
| 0.0018        | 13.5593 | 4000 | 0.3045          | 18.3717 |
| 0.0017        | 16.9492 | 5000 | 0.3281          | 18.7134 |
| 0.0011        | 20.3390 | 6000 | 0.3288          | 18.1179 |
| 0.0005        | 23.7288 | 7000 | 0.3398          | 18.3034 |
| 0.0004        | 27.1186 | 8000 | 0.3515          | 18.5182 |
| 0.0003        | 30.5085 | 9000 | 0.3508          | 18.4401 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1