Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from NbAiLab/nb-whisper-tiny: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/NbAiLab/nb-whisper-tiny/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://NbAiLab/nb-whisper-tiny@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/NbAiLab/nb-whisper-tiny/resolve/refs%2Fpr%2F1/tf_model.h5
151 MB
- Xet hash:
- bf7360689cd37347fe33a4e7d529c5d1b1d9a4c6b02027a21df595d657e9b43e
- Size of remote file:
- 151 MB
- SHA256:
- 36c1e873b00c1f8ecda0bafa5ee8d21e4afe8f561aeb85abb752b20b310db15c
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