---
language:
- ml
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 Malayalam - Arjun Shaji
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: ml
      split: None
      args: 'config: ml, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 83.9080459770115
---

<!-- 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 Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5649
- Wer: 83.9080

## 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: 100
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8113        | 3.7037  | 100  | 0.5609          | 100.6897 |
| 0.1123        | 7.4074  | 200  | 0.4366          | 88.0460  |
| 0.0184        | 11.1111 | 300  | 0.4850          | 87.1264  |
| 0.0056        | 14.8148 | 400  | 0.5643          | 87.8161  |
| 0.001         | 18.5185 | 500  | 0.5649          | 83.9080  |


### Framework versions

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