Automatic Speech Recognition
Transformers
JAX
TensorBoard
Norwegian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabArchive/scream_sextusdecimus_virtual_tsfix_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/scream_sextusdecimus_virtual_tsfix_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabArchive/scream_sextusdecimus_virtual_tsfix_small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabArchive/scream_sextusdecimus_virtual_tsfix_small") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabArchive/scream_sextusdecimus_virtual_tsfix_small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c846b66fdfb7868d8f721a2eead1f41281014f0aa8aefd233fcda8cf7a10dee
- Size of remote file:
- 4.66 kB
- SHA256:
- 6496fbf8283195442fd942cb939e38d5de0b8ed59ac6e4710c755677fb5991e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.