---
language:
- tel
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- jayasuryajsk/google-fleurs-te-romanized
metrics:
- wer
model-index:
- name: Wishper-Large-V3-Telugu_Romanized
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Telugu Romanized 1.0
      type: jayasuryajsk/google-fleurs-te-romanized
    metrics:
    - name: Wer
      type: wer
      value: 67.44785563627842
---

<!-- 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. -->

# Wishper-Large-V3-Telugu_Romanized

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Telugu Romanized 1.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5824
- Wer: 67.4479

## 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: 20
- 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: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0079        | 8.6207  | 1000 | 1.4603          | 64.9051 |
| 0.0007        | 17.2414 | 2000 | 1.5824          | 67.4479 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1