---
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
library_name: transformers
license: apache-2.0
metrics:
- wer
model-index:
- name: whisper-large-v3-Telugu-Version1
  results: []
language:
- te
pipeline_tag: automatic-speech-recognition
---

<!-- 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-Telugu-Version1

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1610
- Wer: 48.7241

## 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: 3e-06
- 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: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.2337        | 6.1920  | 2000  | 0.2242          | 61.4168 |
| 0.1902        | 12.3839 | 4000  | 0.1904          | 55.2632 |
| 0.169         | 18.5759 | 6000  | 0.1778          | 52.8575 |
| 0.1647        | 24.7678 | 8000  | 0.1710          | 51.6746 |
| 0.1523        | 30.9598 | 10000 | 0.1669          | 50.3589 |
| 0.1383        | 37.1517 | 12000 | 0.1642          | 49.9468 |
| 0.1561        | 43.3437 | 14000 | 0.1628          | 49.3089 |
| 0.1475        | 49.5356 | 16000 | 0.1616          | 48.9234 |
| 0.1437        | 55.7276 | 18000 | 0.1610          | 48.7241 |
| 0.1395        | 61.9195 | 20000 | 0.1610          | 48.7241 |


### Framework versions

- PEFT 0.12.1.dev0
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1