Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model-q5_0.bin from NbAiLabBeta/nb-whisper-medium: direct link, hf CLI and curl.
- Browser
- Download file 539 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-medium/resolve/main/ggml-model-q5_0.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-medium/ggml-model-q5_0.bin
-
curl -L -o ggml-model-q5_0.bin https://huggingface.co/NbAiLabBeta/nb-whisper-medium/resolve/main/ggml-model-q5_0.bin
539 MB
- Xet hash:
- f02c65a796e0d6e6f6952f85308db8083c50fd585f65d4fac78d95ccc8e8ab02
- Size of remote file:
- 539 MB
- SHA256:
- 18733de634af639a43b0f8c5f5a2ea0920de4c5b32a5570ec130981581c0e5e7
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