---
license: apache-2.0
base_model: arun100/whisper-base-hi-2
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Hindi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs hi_in
      type: google/fleurs
      config: hi_in
      split: test
      args: hi_in
    metrics:
    - name: Wer
      type: wer
      value: 27.72060783790989
---

<!-- 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 Base Hindi

This model is a fine-tuned version of [arun100/whisper-base-hi-2](https://huggingface.co/arun100/whisper-base-hi-2) on the google/fleurs hi_in dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4468
- Wer: 27.7206

## 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-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4805        | 33.0  | 250  | 0.4868          | 30.4186 |
| 0.3559        | 66.0  | 500  | 0.4417          | 29.0909 |
| 0.2655        | 99.0  | 750  | 0.4307          | 28.2165 |
| 0.1987        | 133.0 | 1000 | 0.4350          | 27.8326 |
| 0.1472        | 166.0 | 1250 | 0.4468          | 27.7206 |
| 0.1061        | 199.0 | 1500 | 0.4640          | 28.0992 |
| 0.0767        | 233.0 | 1750 | 0.4835          | 28.5737 |
| 0.0541        | 266.0 | 2000 | 0.5032          | 28.6857 |
| 0.0396        | 299.0 | 2250 | 0.5202          | 28.7763 |
| 0.03          | 333.0 | 2500 | 0.5353          | 29.2029 |
| 0.0237        | 366.0 | 2750 | 0.5479          | 28.9096 |
| 0.0195        | 399.0 | 3000 | 0.5587          | 28.9096 |
| 0.0163        | 433.0 | 3250 | 0.5683          | 28.9469 |
| 0.014         | 466.0 | 3500 | 0.5767          | 29.1336 |
| 0.0121        | 499.0 | 3750 | 0.5838          | 29.3415 |
| 0.0108        | 533.0 | 4000 | 0.5900          | 29.2775 |
| 0.01          | 566.0 | 4250 | 0.5951          | 29.6081 |
| 0.0093        | 599.0 | 4500 | 0.5988          | 29.4855 |
| 0.0088        | 633.0 | 4750 | 0.6012          | 29.5281 |
| 0.0087        | 666.0 | 5000 | 0.6020          | 29.4268 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0