Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabBeta/nb-whisper-large: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabBeta/nb-whisper-large/resolve/main/flax_model.msgpack
3.09 GB
- Xet hash:
- 9fec7667a5e92c63ffa5af2ea4ad10258fa7bd375f734d69a1806c27a815bd7a
- Size of remote file:
- 3.09 GB
- SHA256:
- 47e4621d27695260db7e1a1f93f17c381c69d27d1675530a7ed0b12559c79c65
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