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Cseti
/
VibeVoice_7B_hun_v2

Text-to-Speech
VibeVoice
Safetensors
Hungarian
tts
lora
sft
full-finetune
Model card Files Files and versions
xet
Community
2

Instructions to use Cseti/VibeVoice_7B_hun_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • VibeVoice

    How to use Cseti/VibeVoice_7B_hun_v2 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("Cseti/VibeVoice_7B_hun_v2")
    model = VibeVoiceForConditionalGenerationInference.from_pretrained(
        "Cseti/VibeVoice_7B_hun_v2", 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_7B_hun_v2 / assets
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Cseti
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