Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from NbAiLab/nb-whisper-small: direct link, hf CLI and curl.
- Browser
- Download file 488 MB
-
https://huggingface.co/NbAiLab/nb-whisper-small/resolve/main/ggml-model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-small/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/NbAiLab/nb-whisper-small/resolve/main/ggml-model.bin
488 MB
- Xet hash:
- 0036541f875f757a580be47ce98a07eeb04122ef95b412d814a857b689802333
- Size of remote file:
- 488 MB
- SHA256:
- a0fc1555f5bd51044b0ea88bb9b7891e1ee331a8087eddb0afc3a05839db48ab
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