---
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: 16.551919937539925
---

<!-- 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: 0.4981
- Wer: 16.5519

## 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: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5019        | 0.2222 | 100  | 0.4649          | 17.3540 |
| 0.4235        | 0.4444 | 200  | 0.4257          | 16.7932 |
| 0.4364        | 0.6667 | 300  | 0.4184          | 16.5164 |
| 0.4106        | 0.8889 | 400  | 0.4043          | 15.6434 |
| 0.2338        | 1.1111 | 500  | 0.4064          | 15.7286 |
| 0.2286        | 1.3333 | 600  | 0.4066          | 15.9699 |
| 0.2185        | 1.5556 | 700  | 0.4058          | 15.7428 |
| 0.212         | 1.7778 | 800  | 0.3999          | 15.6079 |
| 0.2308        | 2.0    | 900  | 0.3991          | 17.2617 |
| 0.0983        | 2.2222 | 1000 | 0.4233          | 15.9415 |
| 0.1183        | 2.4444 | 1100 | 0.4286          | 16.0409 |
| 0.1003        | 2.6667 | 1200 | 0.4304          | 16.0764 |
| 0.1005        | 2.8889 | 1300 | 0.4332          | 15.7641 |
| 0.048         | 3.1111 | 1400 | 0.4636          | 16.3248 |
| 0.0475        | 3.3333 | 1500 | 0.4684          | 16.2041 |
| 0.0516        | 3.5556 | 1600 | 0.4679          | 16.2254 |
| 0.058         | 3.7778 | 1700 | 0.4691          | 16.2538 |
| 0.0457        | 4.0    | 1800 | 0.4693          | 16.2041 |
| 0.028         | 4.2222 | 1900 | 0.4940          | 16.4880 |
| 0.0235        | 4.4444 | 2000 | 0.4981          | 16.5519 |


### Framework versions

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