Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-tiny-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-tiny-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny-verbatim")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-tiny-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-tiny-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NbAiLab/nb-whisper-tiny-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/NbAiLab/nb-whisper-tiny-verbatim/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-tiny-verbatim/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NbAiLab/nb-whisper-tiny-verbatim/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- c75c704415b7e207dbee979a9bebb701821029402dbab3e08df1055c4c4a84f7
- Size of remote file:
- 151 MB
- SHA256:
- c5a0cc0c040552efcff8904d1f9204d2cca051e37dc24eef995135d0cf85b877
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