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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-whisper-tiny-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/NbAiLab/nb-whisper-tiny-verbatim/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-whisper-tiny-verbatim/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-whisper-tiny-verbatim/resolve/main/flax_model.msgpack
151 MB
- Xet hash:
- a6118c6eb017973c7a4ee7cc4d577d80f6e1e5c47d74bb78c19ccbfe345016e9
- Size of remote file:
- 151 MB
- SHA256:
- d800f694ccafb09e34910822ec2d21bcc25313c29f7921686852c5f48ccaa07b
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