Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-small-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-small-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-small-verbatim")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-small-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-small-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_state.bin from NbAiLabBeta/nb-whisper-small-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 2.02 kB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-small-verbatim/resolve/main/training_state.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-small-verbatim/training_state.bin
-
curl -L -o training_state.bin https://huggingface.co/NbAiLabBeta/nb-whisper-small-verbatim/resolve/main/training_state.bin
2.02 kB
- Xet hash:
- 70536547c8ce0a90802f5bbce7e3c88f4dc5aad34ba83af792a3b2176005d3ce
- Size of remote file:
- 2.02 kB
- SHA256:
- a1857f6fa37e344c032ef0fb82d3cb2ce38dc30818bc777db0650c233d0105fc
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