Instructions to use STCOMP/vibevoice-majel-lora-7-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use STCOMP/vibevoice-majel-lora-7-beta 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("STCOMP/vibevoice-majel-lora-7-beta") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "STCOMP/vibevoice-majel-lora-7-beta", 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 diffusion_head/diffusion_head_full.bin from STCOMP/vibevoice-majel-lora-7-beta: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/STCOMP/vibevoice-majel-lora-7-beta/resolve/main/diffusion_head/diffusion_head_full.bin
- Command line
-
hf download hf://STCOMP/vibevoice-majel-lora-7-beta/diffusion_head/diffusion_head_full.bin
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curl -L -o diffusion_head_full.bin https://huggingface.co/STCOMP/vibevoice-majel-lora-7-beta/resolve/main/diffusion_head/diffusion_head_full.bin
1.34 GB
- Size of remote file:
- 1.34 GB
- SHA256:
- 61ef342b1661647d5c7937e463b4c7b9ad9e386541f8280ea511b62efcf53a7a
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