Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base-verbatim") model = AutoModelForMultimodalLM.from_pretrained("NbAiLabBeta/nb-whisper-base-verbatim") - Notebooks
- Google Colab
- Kaggle
nb-whisper-base-verbatim / runs /Jan08_22-00-06_t1v-n-62dd7b00-w-1 /events.out.tfevents.1704751206.t1v-n-62dd7b00-w-1.666956.0.v2
- Xet hash:
- 83f352416537acfae5eb99c9b690bb423c80523da465831aa63681c7275af32d
- Size of remote file:
- 789 kB
- SHA256:
- 085587587a39601ee07fa6169b85116946353959a1b08958691c7b347759adaf
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