---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small Tamil FLEURS
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs ta_in
      type: google/fleurs
      config: ta_in
      split: test
      args: ta_in
    metrics:
    - type: wer
      value: 20.932719441556515
      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 Tamil FLEURS

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs ta_in dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5390
- Wer: 20.9327

## Model description
This model is fine-tuned for 1000 steps on Tamil Fluers data.

- Zero-shot           - 35.2 (google/fluers test)
- fine-tune on FLUERS - 20.93 (google/fluers test) (-40%)


## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0004        | 83.33 | 1000 | 0.5390          | 20.9327 |


### Framework versions

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