Instructions to use STCOMP/vibevoice-majel-lora-7-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use STCOMP/vibevoice-majel-lora-7-test 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-test") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "STCOMP/vibevoice-majel-lora-7-test", 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
| { | |
| "architectures": [ | |
| "VibeVoiceDiffusionHead" | |
| ], | |
| "ddpm_batch_mul": 4, | |
| "ddpm_beta_schedule": "cosine", | |
| "ddpm_num_inference_steps": 20, | |
| "ddpm_num_steps": 1000, | |
| "diffusion_type": "ddpm", | |
| "head_ffn_ratio": 3.0, | |
| "head_layers": 4, | |
| "hidden_size": 3584, | |
| "latent_size": 64, | |
| "model_type": "vibevoice_diffusion_head", | |
| "prediction_type": "v_prediction", | |
| "rms_norm_eps": 1e-05, | |
| "speech_vae_dim": 64, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.51.3" | |
| } | |