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mlboydaisuke
/
VibeVoice-Realtime-0.5B-LiteRT

Text-to-Speech
LiteRT
LiteRT
VibeVoice
tts
diffusion
autoregressive
streaming
on-device
Model card Files Files and versions
xet
Community

Instructions to use mlboydaisuke/VibeVoice-Realtime-0.5B-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • LiteRT

    How to use mlboydaisuke/VibeVoice-Realtime-0.5B-LiteRT with LiteRT:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • VibeVoice

    How to use mlboydaisuke/VibeVoice-Realtime-0.5B-LiteRT 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("mlboydaisuke/VibeVoice-Realtime-0.5B-LiteRT")
    model = VibeVoiceForConditionalGenerationInference.from_pretrained(
        "mlboydaisuke/VibeVoice-Realtime-0.5B-LiteRT", 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-Realtime-0.5B-LiteRT
3.18 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
mlboydaisuke's picture
mlboydaisuke
Correct the per-step GPU timings: they were run() enqueue, not compute
265549b verified 1 day ago
  • .gitattributes
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  • README.md
    5.63 kB
    Correct the per-step GPU timings: they were run() enqueue, not compute 1 day ago
  • embed_tokens.f16
    272 MB
    xet
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  • glue.f32
    6.68 MB
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  • voice_en-Emma_woman.bin

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    2.74 MB
    xet
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  • vv_base_lm_kv_fp32.tflite
    239 MB
    xet
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  • vv_decoder_fp32.tflite
    1.38 GB
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  • vv_diffhead_fp16.tflite
    84.2 MB
    xet
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  • vv_tts_lm_kv_fp32.tflite
    1.19 GB
    xet
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