Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Malay
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Scrya/whisper-medium-ms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scrya/whisper-medium-ms with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Scrya/whisper-medium-ms")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Scrya/whisper-medium-ms") model = AutoModelForSpeechSeq2Seq.from_pretrained("Scrya/whisper-medium-ms") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd42c5c67951054f9ff8d7b3d324c23fa7da7b9b989d8e9b76d6eb2c68a4e3b4
- Size of remote file:
- 3.06 GB
- SHA256:
- fff94b29753047af93e71daa4ba50c2c75c57114dadb5bee1cd681001d3ec10b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.