Instructions to use Masterx/vibevoice-asr-bitnet-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use Masterx/vibevoice-asr-bitnet-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("Masterx/vibevoice-asr-bitnet-onnx") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "Masterx/vibevoice-asr-bitnet-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 decoder_init.int8.onnx.data from Masterx/vibevoice-asr-bitnet-onnx: direct link, hf CLI and curl.
- Browser
- Download file 1.78 GB
-
https://huggingface.co/Masterx/vibevoice-asr-bitnet-onnx/resolve/main/decoder_init.int8.onnx.data
- Command line
-
hf download hf://Masterx/vibevoice-asr-bitnet-onnx/decoder_init.int8.onnx.data
-
curl -L -o decoder_init.int8.onnx.data https://huggingface.co/Masterx/vibevoice-asr-bitnet-onnx/resolve/main/decoder_init.int8.onnx.data
1.78 GB
- Xet hash:
- c807a042a5184997d10ee414b12a78f39699031819842e0ed74c716ca5a7799a
- Size of remote file:
- 1.78 GB
- SHA256:
- e320059847bded90e8fdba11a8bf3f8a916aa707e3a92cc7ba949708e3f2949c
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