Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_def.json from NbAiLab/nb-whisper-large: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/NbAiLab/nb-whisper-large/resolve/main/model_def.json
- Command line
-
hf download hf://NbAiLab/nb-whisper-large/model_def.json
-
curl -L -o model_def.json https://huggingface.co/NbAiLab/nb-whisper-large/resolve/main/model_def.json
261 Bytes
| { | |
| "template_url": "https://raw.githubusercontent.com/NbAiLab/nb-whisper/main/template.md", | |
| "replacements": { | |
| "#Finetuned#": "", | |
| "#Size#": "Large", | |
| "#size#": "large", | |
| "#model_name#": "NbAiLabBeta/nb-whisper-large" | |
| } | |
| } | |