Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Greek
whisper
whisper-event
Eval Results (legacy)
Instructions to use farsipal/whisper-small-el with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use farsipal/whisper-small-el with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="farsipal/whisper-small-el")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("farsipal/whisper-small-el") model = AutoModelForSpeechSeq2Seq.from_pretrained("farsipal/whisper-small-el") - Notebooks
- Google Colab
- Kaggle
Whisper Small - Greek (el)
This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 el dataset for transcription in Greek. It achieves the following results on the evaluation set:
- train_loss: 0.0615
- Wer: 20.2080
Training results
Upon completion of training the best model was reloaded and tested with the following results extracted from the stdout log:
Loading best model from ./whisper-small-el/checkpoint-5000 (score: 20.208023774145616).
{'train_runtime': 73232.697,
'train_samples_per_second': 4.37,
'train_steps_per_second': 0.068,
'train_loss': 0.06146362095708027,
'epoch': 94.34}
TrainOutput(global_step=5000,
training_loss=0.06146362095708027,
metrics={'train_runtime': 73232.697,
'train_samples_per_second': 4.37,
'train_steps_per_second': 0.068,
'train_loss': 0.06146362095708027,
'epoch': 94.34})
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.12.1
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Evaluation results
- Wer on mozilla-foundation/common_voice_11_0 eltest set self-reported25.697