Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base-semantic")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-base-semantic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabBeta/nb-whisper-base-semantic: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-base-semantic/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-base-semantic/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabBeta/nb-whisper-base-semantic/resolve/main/flax_model.msgpack
290 MB
- Xet hash:
- 5942f439ec08b9ceecd3802fdb97e919b736f8ef5175f2af08cc9319b681a60b
- Size of remote file:
- 290 MB
- SHA256:
- 4145caa6a87f93ebcdebbab7330b06d7bcce663daeb37e0d47f3363abbe27bd4
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