Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-medium-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-medium-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-medium-verbatim")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-medium-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NbAiLabBeta/nb-whisper-medium-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-medium-verbatim/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-medium-verbatim/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NbAiLabBeta/nb-whisper-medium-verbatim/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- ed0d09fdb399faaee4df4a7c2bb66da61899abac468d602290eb0a58e326ffb3
- Size of remote file:
- 3.06 GB
- SHA256:
- 620186eb52be87f65baebc5a3969b56ac12d87f6ace264d7a37cb63287f276c4
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