Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabBeta/nb-whisper-small: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabBeta/nb-whisper-small/resolve/main/flax_model.msgpack
967 MB
- Xet hash:
- 1f334665dfc0c20ae89109e3fcd3986aeadda351dc69650731ebb8cb62cc0772
- Size of remote file:
- 967 MB
- SHA256:
- 9efd6bcd79f353d36ac3402e4f0e52a6ab11d8c0b6266be36f27005ddb1c5a99
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