Instructions to use gafiatulin/vibevoice-7b-coreai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use gafiatulin/vibevoice-7b-coreai 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("gafiatulin/vibevoice-7b-coreai") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "gafiatulin/vibevoice-7b-coreai", 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
- Xet hash:
- da0c156392205f5d7ec4a1d8999685b268b2eb23f2124af39f8648cfa8c068ab
- Size of remote file:
- 688 MB
- SHA256:
- 104f44d596ddb62e15b500a4bdd865d8b47b87c0f1432ff7351a46187a3eac9b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.