---
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: 18.02030456852792
      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: 0.4333
- Wer: 18.0203

## 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: 25000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.0669        | 3.3898  | 1000  | 0.2086          | 20.9684 |
| 0.0115        | 6.7797  | 2000  | 0.2637          | 19.7579 |
| 0.0034        | 10.1695 | 3000  | 0.3012          | 19.6408 |
| 0.0026        | 13.5593 | 4000  | 0.3179          | 19.2893 |
| 0.0014        | 16.9492 | 5000  | 0.3242          | 18.7817 |
| 0.0024        | 20.3390 | 6000  | 0.3348          | 19.1624 |
| 0.0024        | 23.7288 | 7000  | 0.3421          | 19.7774 |
| 0.0006        | 27.1186 | 8000  | 0.3511          | 18.6939 |
| 0.0008        | 30.5085 | 9000  | 0.3632          | 18.8989 |
| 0.0007        | 33.8983 | 10000 | 0.3600          | 18.7622 |
| 0.0006        | 37.2881 | 11000 | 0.3470          | 18.4791 |
| 0.0002        | 40.6780 | 12000 | 0.3548          | 18.2936 |
| 0.0001        | 44.0678 | 13000 | 0.3711          | 18.0594 |
| 0.0006        | 47.4576 | 14000 | 0.3733          | 18.2839 |
| 0.0003        | 50.8475 | 15000 | 0.3766          | 18.1667 |
| 0.0           | 54.2373 | 16000 | 0.3745          | 18.0203 |
| 0.0           | 57.6271 | 17000 | 0.3914          | 17.8739 |
| 0.0           | 61.0169 | 18000 | 0.4003          | 17.9032 |
| 0.0           | 64.4068 | 19000 | 0.4081          | 17.8641 |
| 0.0           | 67.7966 | 20000 | 0.4153          | 17.8544 |
| 0.0           | 71.1864 | 21000 | 0.4219          | 17.8544 |
| 0.0           | 74.5763 | 22000 | 0.4281          | 18.0105 |
| 0.0           | 77.9661 | 23000 | 0.4333          | 18.0203 |


### Framework versions

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