Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Greek
whisper
hf-asr-leaderboard
whisper-medium
mozilla-foundation/common_voice_11_0
greek
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use emilios/whisper-medium-el with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilios/whisper-medium-el with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emilios/whisper-medium-el")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("emilios/whisper-medium-el") model = AutoModelForSpeechSeq2Seq.from_pretrained("emilios/whisper-medium-el", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 Medium El Greco | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: mozilla-foundation/common_voice_11_0,google/fleurs | |
| type: mozilla-foundation/common_voice_11_0,google/fleurs | |
| config: null | |
| split: None | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 11.199851411589897 | |
| <!-- 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 Medium El Greco | |
| This model is a fine-tuned version of [emilios/whisper-medium-el](https://huggingface.co/emilios/whisper-medium-el) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3801 | |
| - Wer: 11.1999 | |
| ## 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: 32 | |
| - eval_batch_size: 16 | |
| - 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.0176 | 2.49 | 1000 | 0.2945 | 12.6114 | | |
| | 0.0064 | 4.98 | 2000 | 0.3423 | 12.2307 | | |
| | 0.0022 | 7.46 | 3000 | 0.3632 | 11.5899 | | |
| | 0.0014 | 9.95 | 4000 | 0.3788 | 11.2556 | | |
| | 0.0008 | 12.44 | 5000 | 0.3801 | 11.1999 | | |
| ### Framework versions | |
| - Transformers 4.26.0.dev0 | |
| - Pytorch 1.13.0+cu117 | |
| - Datasets 2.7.1.dev0 | |
| - Tokenizers 0.13.2 | |