---
language:
- ml
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- thennal/IMaSC
- google/fleurs
- mozilla-foundation/common_voice_11_0
- mozilla-foundation/common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper Small Malayalam - Arjun Shaji
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs
      type: thennal/IMaSC
      args: 'config: ml, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 9.54563571143882
---

<!-- 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 google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0209
- Wer: 9.5456

## 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0433        | 0.5800 | 1000 | 0.0434          | 27.1379 |
| 0.02          | 1.1601 | 2000 | 0.0312          | 20.3733 |
| 0.0169        | 1.7401 | 3000 | 0.0242          | 15.4975 |
| 0.0071        | 2.3202 | 4000 | 0.0217          | 12.3555 |
| 0.0058        | 2.9002 | 5000 | 0.0197          | 11.0646 |
| 0.0022        | 3.4803 | 6000 | 0.0202          | 10.0881 |
| 0.0008        | 4.0603 | 7000 | 0.0204          | 9.7006  |
| 0.0005        | 4.6404 | 8000 | 0.0209          | 9.5456  |


### Framework versions

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