Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-tiny-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-tiny-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-tiny-verbatim")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-tiny-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-tiny-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ct2/model.bin from NbAiLabBeta/nb-whisper-tiny-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-tiny-verbatim/resolve/main/ct2/model.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-tiny-verbatim/ct2/model.bin
-
curl -L -o model.bin https://huggingface.co/NbAiLabBeta/nb-whisper-tiny-verbatim/resolve/main/ct2/model.bin
151 MB
- Xet hash:
- c280e8b9d857d5313017caa03d752a76b0076e4eb1826ef8f0c77ed53810cde1
- Size of remote file:
- 151 MB
- SHA256:
- 55b2ccad702638d21734bb2ecd52755ac04a65bb67e26a6b0cf644a2c53dc445
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.