---
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: 17.65774934574004
---

<!-- 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: 0.3950
- Wer: 17.6577

## 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: 1300
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.1125        | 0.0667 | 30   | 0.9877          | 30.4667 |
| 0.9337        | 0.1333 | 60   | 0.7582          | 18.3338 |
| 0.7185        | 0.2    | 90   | 0.4716          | 16.3129 |
| 0.493         | 0.2667 | 120  | 0.4382          | 16.1893 |
| 0.4328        | 0.3333 | 150  | 0.4298          | 15.6223 |
| 0.4127        | 0.4    | 180  | 0.4208          | 16.8726 |
| 0.3865        | 0.4667 | 210  | 0.4171          | 20.0422 |
| 0.419         | 0.5333 | 240  | 0.4141          | 17.0835 |
| 0.4141        | 0.6    | 270  | 0.4157          | 15.8258 |
| 0.464         | 0.6667 | 300  | 0.4077          | 16.9235 |
| 0.4303        | 0.7333 | 330  | 0.4043          | 18.4865 |
| 0.4418        | 0.8    | 360  | 0.4050          | 16.7999 |
| 0.4786        | 0.8667 | 390  | 0.3981          | 15.1352 |
| 0.4238        | 0.9333 | 420  | 0.3953          | 17.0907 |
| 0.3986        | 1.0    | 450  | 0.3926          | 16.7054 |
| 0.2304        | 1.0667 | 480  | 0.3948          | 16.3928 |
| 0.2583        | 1.1333 | 510  | 0.3943          | 16.6327 |
| 0.2385        | 1.2    | 540  | 0.3997          | 15.1425 |
| 0.2126        | 1.2667 | 570  | 0.3985          | 15.0552 |
| 0.2259        | 1.3333 | 600  | 0.3970          | 16.5964 |
| 0.2237        | 1.4    | 630  | 0.3964          | 16.5382 |
| 0.2344        | 1.4667 | 660  | 0.3983          | 17.9485 |
| 0.2068        | 1.5333 | 690  | 0.3974          | 17.9703 |
| 0.2535        | 1.6    | 720  | 0.3950          | 17.6577 |


### Framework versions

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