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---
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