---
language:
- te
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Te - Bharat Ramanathan
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: te_in
      split: test
    metrics:
    - type: wer
      value: 30.26
      name: WER
---

<!-- 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 Small Te - Bharat Ramanathan

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1863
- Wer: 31.6456

## 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: 64
- eval_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1637        | 0.1   | 500  | 0.2092          | 42.9406 |
| 0.1459        | 0.2   | 1000 | 0.2025          | 35.9299 |
| 0.1348        | 0.3   | 1500 | 0.1990          | 35.4917 |
| 0.1309        | 0.4   | 2000 | 0.1974          | 33.7390 |
| 0.1253        | 0.5   | 2500 | 0.1974          | 34.0312 |
| 0.1209        | 0.6   | 3000 | 0.1909          | 32.4732 |
| 0.1139        | 1.05  | 3500 | 0.1899          | 31.7916 |
| 0.1043        | 1.15  | 4000 | 0.1868          | 31.6456 |
| 0.0996        | 1.25  | 4500 | 0.1874          | 31.6943 |
| 0.1002        | 1.35  | 5000 | 0.1863          | 31.6456 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2