---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-tiny-finetune-hindi-fleurs
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: hi_in
      split: train
      args: hi_in
    metrics:
    - name: Wer
      type: wer
      value: 0.8889948502765592
---

<!-- 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-tiny-finetune-hindi-fleurs

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8973
- Wer Ortho: 0.8687
- Wer: 0.8890

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.9951        | 0.83  | 100  | 1.8632          | 1.1021    | 1.1432 |
| 1.2634        | 1.67  | 200  | 1.2561          | 1.0496    | 1.1282 |
| 0.8868        | 2.5   | 300  | 1.0672          | 0.8591    | 0.8911 |
| 0.6568        | 3.33  | 400  | 0.9656          | 0.9689    | 1.0460 |
| 0.5288        | 4.17  | 500  | 0.8973          | 0.8687    | 0.8890 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0