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:
- 9b15a06efa227b549412d7b0813db0f9b038c5e0174c0e6b46e8bff5a870564c
- Size of remote file:
- 134 Bytes
- SHA256:
- 55bcd14562d1e4c563f7033b26a21f9ff887d8827efaabd96b4d89a2268f3bdb
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