Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-small-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-small-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-small-semantic")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-small-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-small-semantic") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90fcbee6f6964ceab4c6afc43a71c9a0b55d7944d1711eb17dc7bc673f165337
- Size of remote file:
- 967 MB
- SHA256:
- 08ada3a880f465890c8168df3b8862a706cfd84889a3c2964849d8a71022dcec
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