---
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+test
      args: hi_in
    metrics:
    - name: Wer
      type: wer
      value: 0.42621638924455824
---

<!-- 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: 0.8315
- Wer Ortho: 0.4313
- Wer: 0.4262

## 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: 32
- eval_batch_size: 32
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.8112        | 1.39  | 100  | 1.7274          | 0.6323    | 0.6258 |
| 1.0387        | 2.78  | 200  | 1.1194          | 0.5130    | 0.5072 |
| 0.7671        | 4.17  | 300  | 0.9671          | 0.4665    | 0.4613 |
| 0.5283        | 5.56  | 400  | 0.8840          | 0.4494    | 0.4440 |
| 0.4458        | 6.94  | 500  | 0.8315          | 0.4313    | 0.4262 |


### Framework versions

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