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
VibeVoice-Realtime-0.5B (ONNX Export)
This repository contains an ONNX-exported version of the microsoft/VibeVoice-Realtime-0.5B model.
This export was manually created to allow cross-platform inference in environments like ONNX Runtime Web (JavaScript) and Flutter (Dart).
π Credits
All credit for the original model architecture, training, and base weights goes to the Microsoft VibeVoice Team. Please see their original repository for full details and research:
The original weights and software are licensed under the MIT License.
π¦ What's included?
Due to the streaming nature of VibeVoice, the ONNX export is modularized into the following specific components (with accompanying .data files for external weights):
language_model.onnxtts_language_model.onnxtts_eos_classifier.onnxacoustic_tokenizer.onnx
Note: The ir_version for these models has been set to 9 to natively support standard Flutter onnxruntime bindings.
π Usage
These models are optimized for ONNX Runtime. They can be loaded directly into client-side applications instead of maintaining heavy PyTorch backends. Check the corresponding JS and Flutter demo applications for integration guidance!