Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabBeta/nb-whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabBeta/nb-whisper-base/resolve/main/flax_model.msgpack
290 MB
- Xet hash:
- b5ca3c38d810524e2a4d6b35922dd255eb0b5ec0338bacca464ceb690fe735ec
- Size of remote file:
- 290 MB
- SHA256:
- ce183093b2aa8b51dafc7c8a0a05761dd002fa4109e69c67559282b3275ca41c
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