Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ef1e3e3dad8017237e57cbdfe4eaad3287223db90c309e4ea11f9a02c0949f0
- Size of remote file:
- 134 Bytes
- SHA256:
- fede945ec708c0e6d8c2bfc8b22c039ab0687163af2073d1d7ae349bc68bf42c
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