Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model-q5_0.bin from NbAiLabBeta/nb-whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 55.3 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-base/resolve/main/ggml-model-q5_0.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-base/ggml-model-q5_0.bin
-
curl -L -o ggml-model-q5_0.bin https://huggingface.co/NbAiLabBeta/nb-whisper-base/resolve/main/ggml-model-q5_0.bin
55.3 MB
- Xet hash:
- 20cf03eb56b7c6fa845ebe9ca4c28294a022d754cc18549db2c0d3d0ffc20800
- Size of remote file:
- 55.3 MB
- SHA256:
- dcb9f3ab963cd288974c826c1519ff73b78b2372e80d388a6ce94f29c6a5b40f
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