Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large-semantic")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-large-semantic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from NbAiLabBeta/nb-whisper-large-semantic: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-large-semantic/resolve/main/model.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-large-semantic/model.bin
-
curl -L -o model.bin https://huggingface.co/NbAiLabBeta/nb-whisper-large-semantic/resolve/main/model.bin
6.17 GB
- Xet hash:
- 6882021b58abbf01a9a21a58c1af9d1cea3047475141e2fd68b75e8a47aee1c2
- Size of remote file:
- 6.17 GB
- SHA256:
- a732644bb6abdde97f891c014a6e7f01be8858883f1d1828127cd8ae2f22044d
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