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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b414c68ecee009d7999cf12b12bcbf5d339e4f70d2f26b39ea32b199d2db50e3
- Size of remote file:
- 135 Bytes
- SHA256:
- be6b423a2c639c4d1b8be35d49658df64690dd6fdcbf6bcd211bf2d8fa94444f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.