---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-sm-el-intlv-xl
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: el
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 19.48365527488856
---


# whisper-sm-el-intlv-xl

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 (el) and the google/fleurs (el_gr) datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4725
- Wer: 19.4837

## Model description

The model was trained over 10000 steps on translation from Greek to English. 

## Intended uses & limitations

This model was part of the Whisper Finetuning Event (Dec 2022) and was used primarily to compare relative improvements between transcription and translation tasks.

## Training and evaluation data

The training datasets combined examples from both train and evaluation splits and use the train split of the mozilla-foundation/common_voice_11_0 (el) dataset for evaluation and selection of the best checkpoint. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8.5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0545        | 2.49  | 1000  | 0.2891          | 22.4926 |
| 0.0093        | 4.98  | 2000  | 0.3927          | 20.1337 |
| 0.0018        | 7.46  | 3000  | 0.4031          | 20.1616 |
| 0.001         | 9.95  | 4000  | 0.4209          | 19.6880 |
| 0.0008        | 12.44 | 5000  | 0.4498          | 20.0966 |
| 0.0005        | 14.93 | 6000  | 0.4725          | 19.4837 |
| 0.0002        | 17.41 | 7000  | 0.4917          | 19.5951 |
| 0.0001        | 19.9  | 8000  | 0.5050          | 19.6230 |
| 0.0001        | 22.39 | 9000  | 0.5146          | 19.5672 |
| 0.0001        | 24.88 | 10000 | 0.5186          | 19.4837 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.12.1