Instructions to use octanove/mosla-whisper-spkid-zho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use octanove/mosla-whisper-spkid-zho with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="octanove/mosla-whisper-spkid-zho")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("octanove/mosla-whisper-spkid-zho") model = AutoModelForAudioClassification.from_pretrained("octanove/mosla-whisper-spkid-zho") - Notebooks
- Google Colab
- Kaggle
Upload WhisperForAudioClassification
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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{
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"_name_or_path": "whisper-speaker-id-zho/checkpoint-
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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{
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"_name_or_path": "whisper-speaker-id-zho-final/checkpoint-1320",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 2548606507
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