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 onnx/encoder_model.onnx from NbAiLab/nb-whisper-tiny-beta: direct link, hf CLI and curl.
- Browser
- Download file 32.9 MB
-
https://huggingface.co/NbAiLab/nb-whisper-tiny-beta/resolve/main/onnx/encoder_model.onnx
- Command line
-
hf download hf://NbAiLab/nb-whisper-tiny-beta/onnx/encoder_model.onnx
-
curl -L -o encoder_model.onnx https://huggingface.co/NbAiLab/nb-whisper-tiny-beta/resolve/main/onnx/encoder_model.onnx
32.9 MB
- Xet hash:
- 74b4268ba35d0c3e5d42259df4fb2f03f541a5dda6157e3b56432f08408f5cf2
- Size of remote file:
- 32.9 MB
- SHA256:
- e77cfc5c6a15c3d8c5cfba8f83ab21bfdfc8e02f58c1a645ca4382cf8946bbe3
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