Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large-verbatim")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from NbAiLab/nb-whisper-large-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 3.1 GB
-
https://huggingface.co/NbAiLab/nb-whisper-large-verbatim/resolve/main/ggml-model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-large-verbatim/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/NbAiLab/nb-whisper-large-verbatim/resolve/main/ggml-model.bin
3.1 GB
- Xet hash:
- ba60a40b419592c0a213d1fa6e82ae71d6139ea241d2bc3031296c682b86b9d6
- Size of remote file:
- 3.1 GB
- SHA256:
- 0227866ef47189f79d04347a080c73fcd8267c8e2255da5f77803506ab4ffc7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.