Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
NbAiLab/NPSC
Generated from Trainer
robust-speech-event
Instructions to use NbAiLab/wav2vec2-large-voxrex-npsc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/wav2vec2-large-voxrex-npsc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/wav2vec2-large-voxrex-npsc")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("NbAiLab/wav2vec2-large-voxrex-npsc") model = AutoModelForCTC.from_pretrained("NbAiLab/wav2vec2-large-voxrex-npsc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a313428f23287ccdcc2238603d6b43ba56a4a413eec3deac9cbd92c57654682f
- Size of remote file:
- 3.06 kB
- SHA256:
- 0a08d9ee7576c2ae3a587fe8ac965aaa589fd62cbcec94c1f2e2acc7d4bd57f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.