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/encoder_model.onnx from NbAiLabBeta/nb-whisper-tiny-semantic: direct link, hf CLI and curl.
- Browser
- Download file 32.9 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-tiny-semantic/resolve/main/onnx/encoder_model.onnx
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-tiny-semantic/onnx/encoder_model.onnx
-
curl -L -o encoder_model.onnx https://huggingface.co/NbAiLabBeta/nb-whisper-tiny-semantic/resolve/main/onnx/encoder_model.onnx
32.9 MB
- Xet hash:
- d3578c97508aa8336bc1f89add3aeece21ac340247f599d46f088e140934ae50
- Size of remote file:
- 32.9 MB
- SHA256:
- 59297900c04d85c8ec347f4080e717ca681019d40aaa515c8fa748d3f078ae1e
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