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---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- konnakol
metrics:
- wer
model-index:
- name: Whisper Small Hi - Gopika Krishnan
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Konnakol
type: konnakol
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 87.64568764568764
---
<!-- 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 - Gopika Krishnan
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Konnakol dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2352
- Wer: 87.6457
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:-------:|
| 0.1507 | 21.7391 | 500 | 1.2891 | 87.7622 |
| 0.0428 | 43.4783 | 1000 | 1.4133 | 93.7063 |
| 0.0111 | 65.2174 | 1500 | 1.7252 | 89.3939 |
| 0.0063 | 86.9565 | 2000 | 1.8134 | 85.8974 |
| 0.0035 | 108.6957 | 2500 | 2.0195 | 85.7809 |
| 0.003 | 130.4348 | 3000 | 2.0771 | 87.8788 |
| 0.0027 | 152.1739 | 3500 | 2.1378 | 87.5291 |
| 0.0025 | 173.9130 | 4000 | 2.1730 | 86.4802 |
| 0.0025 | 195.6522 | 4500 | 2.2126 | 87.8788 |
| 0.0025 | 217.3913 | 5000 | 2.2352 | 87.6457 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|