---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-hindi-transcribe
  results: []
---

<!-- 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-small-hindi-transcribe

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3134
- Wer: 21.0323

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.1415        | 1.8473  | 750  | 0.1538          | 22.5756 |
| 0.0787        | 3.6946  | 1500 | 0.1400          | 20.1356 |
| 0.039         | 5.5419  | 2250 | 0.1561          | 20.1668 |
| 0.0163        | 7.3892  | 3000 | 0.1859          | 20.6361 |
| 0.0057        | 9.2365  | 3750 | 0.2193          | 21.0532 |
| 0.0018        | 11.0837 | 4500 | 0.2519          | 20.9906 |
| 0.0011        | 12.9310 | 5250 | 0.2728          | 21.1470 |
| 0.0006        | 14.7783 | 6000 | 0.2896          | 21.3139 |
| 0.0002        | 16.6256 | 6750 | 0.3043          | 21.0636 |
| 0.0002        | 18.4729 | 7500 | 0.3134          | 21.0323 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0