---
language:
- ta
base_model: 'openai/whisper-large-v3'
tags:
- generated_from_trainer
datasets:
- Prajwal-143/ASR-Tamil-cleaned
metrics:
- wer
model-index:
- name: Whisper-large-v3-ta - Log-Tamil
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ' asr-tamil-cleaned'
      type: Prajwal-143/ASR-Tamil-cleaned
    metrics:
    - name: Wer
      type: wer
      value: 10.136499555844708
---

<!-- 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-large-v3-ta - Log-Tamil

This model is a fine-tuned version of [openai/whisper-large-v3  ](https://huggingface.co./openai/whisper-large-v3  ) on the  asr-tamil-cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1587
- Wer Ortho: 36.2968
- Wer: 10.1365

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.1489        | 0.0143 | 500  | 0.1587          | 36.2968   | 10.1365 |




### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1