Instructions to use Sergey004/VibeVoice-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sergey004/VibeVoice-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Sergey004/VibeVoice-Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("Sergey004/VibeVoice-Large") model = AutoModelForTextToWaveform.from_pretrained("Sergey004/VibeVoice-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Sergey004/VibeVoice-Large: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/Sergey004/VibeVoice-Large/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Sergey004/VibeVoice-Large/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Sergey004/VibeVoice-Large/resolve/main/preprocessor_config.json
349 Bytes
| { | |
| "processor_class": "VibeVoiceProcessor", | |
| "speech_tok_compress_ratio": 3200, | |
| "db_normalize": true, | |
| "audio_processor": { | |
| "feature_extractor_type": "VibeVoiceTokenizerProcessor", | |
| "sampling_rate": 24000, | |
| "normalize_audio": true, | |
| "target_dB_FS": -25, | |
| "eps": 1e-06 | |
| }, | |
| "language_model_pretrained_name": "Qwen/Qwen2.5-7B" | |
| } |