Feature Extraction
Transformers
Safetensors
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vibevoice_semantic_tokenizer
audio tokenizer
Instructions to use bezzam/VibeVoice-SemanticTokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/VibeVoice-SemanticTokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bezzam/VibeVoice-SemanticTokenizer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bezzam/VibeVoice-SemanticTokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eps": 1e-06, | |
| "feature_extractor_type": "VibeVoiceFeatureExtractor", | |
| "feature_size": 1, | |
| "normalize_audio": true, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000, | |
| "target_dB_FS": -25 | |
| } | |