---
library_name: peft
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-hindi-peft
  results: []
language:
- hi
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-hindi-peft

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1044
- Wer: 0.2829

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4265        | 1.4689 | 200  | 0.1282          | 0.3696 |
| 0.2581        | 2.9377 | 400  | 0.0941          | 0.2797 |
| 0.1579        | 4.4103 | 600  | 0.0984          | 0.2850 |
| 0.1285        | 5.8791 | 800  | 0.0999          | 0.2795 |
| 0.0862        | 7.3516 | 1000 | 0.1044          | 0.2829 |


### Framework versions

- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0