---
library_name: transformers
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Ori vi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 15.251862231728003
---

<!-- 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 Ori vi

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4021
- Wer: 15.2519

## 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: 16
- eval_batch_size: 8
- 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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.458         | 0.2222 | 100  | 0.4649          | 16.8154 |
| 0.4314        | 0.4444 | 200  | 0.4266          | 16.4319 |
| 0.4275        | 0.6667 | 300  | 0.4166          | 15.5542 |
| 0.3946        | 0.8889 | 400  | 0.4107          | 15.5764 |
| 0.2151        | 1.1111 | 500  | 0.4051          | 15.5616 |
| 0.2383        | 1.3333 | 600  | 0.4014          | 15.3551 |
| 0.2176        | 1.5556 | 700  | 0.3979          | 15.5395 |
| 0.2271        | 1.7778 | 800  | 0.3996          | 15.2371 |
| 0.222         | 2.0    | 900  | 0.3966          | 15.4141 |
| 0.1469        | 2.2222 | 1000 | 0.4021          | 15.2519 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0