Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-tiny-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-tiny-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-tiny-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-tiny-verbatim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c75c704415b7e207dbee979a9bebb701821029402dbab3e08df1055c4c4a84f7
- Size of remote file:
- 151 MB
- SHA256:
- c5a0cc0c040552efcff8904d1f9204d2cca051e37dc24eef995135d0cf85b877
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.