---
language:
- et
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small et - Common Voice+FLEURS
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0+FLEURS
      type: mozilla-foundation/common_voice_11_0
      config: et
      split: train
      args: et
    metrics:
    - name: Wer
      type: wer
      value: 42.48444526581655
---

<!-- 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 et - Common Voice+FLEURS

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the Common Voice 11.0, FLEURS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8754
- Wer: 42.4844

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0094        | 10.0  | 1000 | 0.7125          | 43.4085 |
| 0.0024        | 20.01 | 2000 | 0.7960          | 42.1795 |
| 0.0012        | 30.01 | 3000 | 0.8237          | 41.8961 |
| 0.0006        | 40.02 | 4000 | 0.8627          | 41.7853 |
| 0.0004        | 51.0  | 5000 | 0.8754          | 42.4844 |


### Framework versions

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