---
language:
- te
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper base te - jayavardhan
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: google/fleurs
      config: te_in
      split: None
      args: 'config: te, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 70.43668684786809
---

<!-- 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 base te - jayavardhan

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1934
- Wer: 70.4367

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.084         | 6.12  | 500  | 0.1455          | 71.1065 |
| 0.0297        | 12.23 | 1000 | 0.1682          | 69.8570 |
| 0.0175        | 18.35 | 1500 | 0.1934          | 70.4367 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2