Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eece99338b073e21df9b9bb423f5e9051e10b835399b9bde30af83dc645b48e4
- Size of remote file:
- 135 Bytes
- SHA256:
- 4cc7a09db274dded41de67bc23963f465c6c2251156faa8e00d7a37aa237168c
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