Instructions to use Ivydata/wav2vec2-large-speech-diarization-jp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ivydata/wav2vec2-large-speech-diarization-jp with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("Ivydata/wav2vec2-large-speech-diarization-jp") model = AutoModelForAudioFrameClassification.from_pretrained("Ivydata/wav2vec2-large-speech-diarization-jp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce0dfa8fa04522dfecc830ad094e45cecb34e882a6056f353e5b36927a367412
- Size of remote file:
- 1.26 GB
- SHA256:
- d98695be760ce4769897da81fd9d8e162ae37149ebec5d8105cf3ead240abeec
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