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 tts_language_model.onnx.data from iamvaar/vibevoice-realtime-0.5b-onnx: direct link, hf CLI and curl.
- Browser
- Download file 1.19 GB
-
https://huggingface.co/iamvaar/vibevoice-realtime-0.5b-onnx/resolve/main/tts_language_model.onnx.data
- Command line
-
hf download hf://iamvaar/vibevoice-realtime-0.5b-onnx/tts_language_model.onnx.data
-
curl -L -o tts_language_model.onnx.data https://huggingface.co/iamvaar/vibevoice-realtime-0.5b-onnx/resolve/main/tts_language_model.onnx.data
1.19 GB
- Xet hash:
- 867d9430faa08af3ce6423f35223b4eba4a66e5662a52c92c24646f6c1e87385
- Size of remote file:
- 1.19 GB
- SHA256:
- fa8e7488d6f0b4fd980ec13e3ba8def57fe664ed1a6df8852f982bdf1b03829f
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