Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4a8da68dce583a12abac4d7dfc0f5221302ff7bc9d623e677301a730d990e9d
- Size of remote file:
- 134 Bytes
- SHA256:
- 65e002b59067b7648c2bba40a162c47cf9509494929f817684927c7d7da335f8
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