Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-tiny-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-tiny-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny-beta")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-tiny-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-tiny-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-whisper-tiny-beta: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/NbAiLab/nb-whisper-tiny-beta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-whisper-tiny-beta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-whisper-tiny-beta/resolve/main/flax_model.msgpack
151 MB
- Xet hash:
- cce848ce4b9940f89555f9bd614d452ccad2f89762331c199da98b3e7553a37b
- Size of remote file:
- 151 MB
- SHA256:
- 37c1118f0474ec9d316c7308d97c610ab787f9b27d10a0bbb05f9d11c30f8edf
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