Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Sandiago21/whisper-large-v2-greek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sandiago21/whisper-large-v2-greek with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sandiago21/whisper-large-v2-greek")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sandiago21/whisper-large-v2-greek") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sandiago21/whisper-large-v2-greek", device_map="auto") - Notebooks
- Google Colab
- Kaggle
whisper-large-v2-greek / runs /Jul14_08-32-25_c29dfb936ef1 /1689323873.9128144 /events.out.tfevents.1689323873.c29dfb936ef1.551698.1
- Xet hash:
- 3d59fc52462b97126f767c71321fff46e44dab2d8c23fdd0657218eb44e22d63
- Size of remote file:
- 6.23 kB
- SHA256:
- 477f0125bcf7402b7f0c6f74c535505e6bfbaf6d8876110c7a41eb4f44f23d7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.