---
language:
- el
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
- automatic-speech-recognition
- greek
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-sm-el-intlv-xs
  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: 20.068722139673106
---


# Whisper small (Greek) Trained on Interleaved Datasets

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on interleaved mozilla-foundation/common_voice_11_0 (el) and google/fleurs (el_gr) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4741
- Wer: 20.0687

## Model description

The model was developed during the Whisper Fine-Tuning Event in December 2022. 
More details on the model can be found [in the original paper](https://cdn.openai.com/papers/whisper.pdf)

## Intended uses & limitations

The model is fine-tuned for transcription in the Greek language. 

## Training and evaluation data

This model was trained by interleaving the training and evaluation splits from two different datasets:  

- mozilla-foundation/common_voice_11_0 (el)
- google/fleurs (el_gr)


## Training procedure

The python script used is a modified version of the script provided by Hugging Face and can be found [here](https://github.com/kamfonas/whisper-fine-tuning-event/blob/minor-mods-by-farsipal/run_speech_recognition_seq2seq_streaming.py)   

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0186        | 4.98  | 1000 | 0.3619          | 21.0067 |
| 0.0012        | 9.95  | 2000 | 0.4347          | 20.3009 |
| 0.0005        | 14.93 | 3000 | 0.4741          | 20.0687 |
| 0.0003        | 19.9  | 4000 | 0.4974          | 20.1152 |
| 0.0003        | 24.88 | 5000 | 0.5066          | 20.2266 |


### Framework versions

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