Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use sgangireddy/whisper-medium-mls-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgangireddy/whisper-medium-mls-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sgangireddy/whisper-medium-mls-es")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sgangireddy/whisper-medium-mls-es") model = AutoModelForSpeechSeq2Seq.from_pretrained("sgangireddy/whisper-medium-mls-es") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eace16a8eb78d93e01724283037843c34dae9369723dc756142c43af1c5c938
- Size of remote file:
- 3.06 GB
- SHA256:
- 516402a239f4a095780803a26c90dcc3fbf338a161697292d9f1f67f5d41a9db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.