Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-tiny-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-tiny-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-tiny-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-tiny-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-tiny-verbatim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6118c6eb017973c7a4ee7cc4d577d80f6e1e5c47d74bb78c19ccbfe345016e9
- Size of remote file:
- 151 MB
- SHA256:
- d800f694ccafb09e34910822ec2d21bcc25313c29f7921686852c5f48ccaa07b
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