Whisper small (Greek) Trained on Interleaved Datasets

This model is a fine-tuned version of 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

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

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

Framework versions

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