---
library_name: transformers
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- ifc0nfig/whisper_fine_tune_v2
metrics:
- wer
model-index:
- name: Whisper Small Hi - Vyapar
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Vyapar Calling Data
      type: ifc0nfig/whisper_fine_tune_v2
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 62.925851703406806
---

<!-- 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 Hi - Vyapar

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Vyapar Calling Data dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0664
- Wer: 62.9259

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.1183        | 8.7753  | 1000 | 1.5163          | 56.7134 |
| 0.0037        | 17.5463 | 2000 | 1.8823          | 54.8297 |
| 0.0003        | 26.3172 | 3000 | 2.0341          | 54.9499 |
| 0.0002        | 35.0881 | 4000 | 2.0664          | 62.9259 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0