Instructions to use iamvaar/vibevoice-realtime-0.5b-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use iamvaar/vibevoice-realtime-0.5b-onnx with VibeVoice:
import torch, soundfile as sf, librosa, numpy as np from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference # Load voice sample (should be 24kHz mono) voice, sr = sf.read("path/to/voice_sample.wav") if voice.ndim > 1: voice = voice.mean(axis=1) if sr != 24000: voice = librosa.resample(voice, sr, 24000) processor = VibeVoiceProcessor.from_pretrained("iamvaar/vibevoice-realtime-0.5b-onnx") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "iamvaar/vibevoice-realtime-0.5b-onnx", torch_dtype=torch.bfloat16 ).to("cuda").eval() model.set_ddpm_inference_steps(5) inputs = processor(text=["Speaker 0: Hello!\nSpeaker 1: Hi there!"], voice_samples=[[voice]], return_tensors="pt") audio = model.generate(**inputs, cfg_scale=1.3, tokenizer=processor.tokenizer).speech_outputs[0] sf.write("output.wav", audio.cpu().numpy().squeeze(), 24000) - Notebooks
- Google Colab
- Kaggle
Download acoustic_tokenizer.onnx from iamvaar/vibevoice-realtime-0.5b-onnx: direct link, hf CLI and curl.
- Browser
- Download file 786 kB
-
https://huggingface.co/iamvaar/vibevoice-realtime-0.5b-onnx/resolve/main/acoustic_tokenizer.onnx
- Command line
-
hf download hf://iamvaar/vibevoice-realtime-0.5b-onnx/acoustic_tokenizer.onnx
-
curl -L -o acoustic_tokenizer.onnx https://huggingface.co/iamvaar/vibevoice-realtime-0.5b-onnx/resolve/main/acoustic_tokenizer.onnx
786 kB
- Xet hash:
- cd9887a1c858bb7c577bab7e8a41cbd727aff9c58540f0c25e357515d1510273
- Size of remote file:
- 786 kB
- SHA256:
- 8feb06effc45df9f89c93968d5d132373b473b018d65561763e4f4094699ea26
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