---
language:
- ml
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- thennal/GMaSC
metrics:
- wer
model-index:
- name: Whisper Small Malayalam - Arjun Shaji
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: thennal/GMaSC
      type: thennal/GMaSC
      args: 'config: ml, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 16.95364238410596
---

<!-- 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 Malayalam - Arjun Shaji

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the thennal/GMaSC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0505
- Wer: 16.9536

## 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: 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0022        | 10.0  | 1000 | 0.0410          | 18.0132 |
| 0.0002        | 20.0  | 2000 | 0.0454          | 17.6159 |
| 0.0           | 30.0  | 3000 | 0.0486          | 17.2185 |
| 0.0           | 40.0  | 4000 | 0.0499          | 17.1302 |
| 0.0           | 50.0  | 5000 | 0.0505          | 16.9536 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1