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:
- 25608db4fa304c7525a0d1fd7299bebdaafe158b304bd7d65974ed43a1e7d280
- Size of remote file:
- 134 Bytes
- SHA256:
- 2307bd9b42ead0bee543f7809fb531408cdbc54cfa0c8c38995a02e4e9f5f710
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.