Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04c3c2bf8ba743521e199c09e5bef43f63ebcca02d7e44950f41ee7e7b39ae90
- Size of remote file:
- 135 Bytes
- SHA256:
- 8c5158e765312955eaf034d7c30a973b974150f19456a77890a49c231a8ff604
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