Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-tiny-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-tiny-semantic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-tiny-semantic")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-tiny-semantic") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-tiny-semantic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/decoder_model.onnx from NbAiLabBeta/nb-whisper-tiny-semantic: direct link, hf CLI and curl.
- Browser
- Download file 198 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-tiny-semantic/resolve/main/onnx/decoder_model.onnx
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-tiny-semantic/onnx/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/NbAiLabBeta/nb-whisper-tiny-semantic/resolve/main/onnx/decoder_model.onnx
198 MB
- Xet hash:
- a47409d28fa3790675c66acfd87a261234c4c6c169d7ffb52aae1afcb5093a0a
- Size of remote file:
- 198 MB
- SHA256:
- a4cfa372d7286de10f6959c1fbd417d976f8c640b2f02112f549fe179b40fae3
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