Instructions to use microsoft/VibeVoice-AcousticTokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/VibeVoice-AcousticTokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/VibeVoice-AcousticTokenizer")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("microsoft/VibeVoice-AcousticTokenizer") model = AutoModel.from_pretrained("microsoft/VibeVoice-AcousticTokenizer") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "VibeVoiceAcousticTokenizerModel" | |
| ], | |
| "channels": 1, | |
| "depths": [ | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 8 | |
| ], | |
| "downsampling_ratios": [ | |
| 2, | |
| 2, | |
| 4, | |
| 5, | |
| 5, | |
| 8 | |
| ], | |
| "dtype": "bfloat16", | |
| "ffn_expansion": 4, | |
| "hidden_act": "gelu", | |
| "hidden_size": 64, | |
| "initializer_range": 0.01, | |
| "kernel_size": 7, | |
| "layer_scale_init_value": 1e-06, | |
| "model_type": "vibevoice_acoustic_tokenizer", | |
| "num_filters": 32, | |
| "rms_norm_eps": 1e-05, | |
| "transformers_version": "5.0.1.dev0", | |
| "vae_std": 0.625, | |
| "weight_init_value": 0.01 | |
| } | |