---
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: 18.5864896524795
      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: 0.3821
- Wer: 18.5865

## 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  | 0.2078          | 20.8512 |
| 0.0131        | 6.7797  | 2000  | 0.2584          | 20.0312 |
| 0.002         | 10.1695 | 3000  | 0.3048          | 19.2698 |
| 0.0024        | 13.5593 | 4000  | 0.3192          | 19.1429 |
| 0.0025        | 16.9492 | 5000  | 0.3127          | 19.0941 |
| 0.0008        | 20.3390 | 6000  | 0.3412          | 19.1429 |
| 0.0008        | 23.7288 | 7000  | 0.3438          | 18.3913 |
| 0.0011        | 27.1186 | 8000  | 0.3465          | 18.8501 |
| 0.001         | 30.5085 | 9000  | 0.3549          | 18.5377 |
| 0.0002        | 33.8983 | 10000 | 0.3551          | 18.0594 |
| 0.0           | 37.2881 | 11000 | 0.3689          | 18.3522 |
| 0.0           | 40.6780 | 12000 | 0.3721          | 18.3229 |
| 0.0           | 44.0678 | 13000 | 0.3821          | 18.5865 |


### Framework versions

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