VibeVoice-1.5B GGUF

GGUF conversion of microsoft/VibeVoice-1.5B for use with CrispASR.

This is the base model (not the streaming variant). It supports voice cloning from audio samples and multi-speaker synthesis.

Model variants

|------|-------|------|-------|

| vibevoice-1.5b-tts-f16.gguf | F16 | 5.1 GB | Full precision |

| vibevoice-1.5b-tts-q8_0.gguf | Q8_0 | 2.8 GB | Near-lossless |

| vibevoice-1.5b-tts-q4_k.gguf | Q4_K | 1.6 GB | Smallest, perfect ASR round-trip |

Usage

Requires a voice reference audio (WAV file, 24 kHz mono) for voice cloning:

# Voice cloning TTS
VIBEVOICE_VOICE_AUDIO=reference_voice.wav \
crispasr --tts "Hello, how are you today?" \
    -m vibevoice-1.5b-tts-q4_k.gguf \
    --tts-output output.wav

Architecture

Single-LM architecture (differs from the streaming Realtime-0.5B):

LM: Qwen2.5-1.5B (d=1536, 28 layers, 12 heads, 2 KV heads)
Prediction head: 4 AdaLN + SwiGLU layers (d=1536)
Acoustic encoder: 7-stage ConvNeXt (3200x downsample from 24kHz)
Semantic encoder: same architecture, 128-dim latent
Decoder: 7-stage transposed ConvNeXt (3200x upsample)
DPM-Solver++: 20-step, cosine schedule, v-prediction

The model generates speech tokens autoregressively — the LM produces

<|vision_pad|> (speech_diffusion) tokens that trigger diffusion sampling,

with <|vision_start|> / <|vision_end|> as control tokens.

Quality

| Input | Parakeet ASR |

|-------|-------------|

| "Hello, how are you today?" | "Hello, how are you today?" |

Differences from Realtime-0.5B

| Feature | Realtime-0.5B | 1.5B Base |

|---------|--------------|-----------|

| Architecture | 4L base + 20L TTS LM | Single 28L LM |

| Voice input | Pre-computed .pt prompts | Audio WAV files |

| Voice cloning | No (fixed presets) | Yes (from reference audio) |

| Multi-speaker | No | Yes (up to 4 speakers) |

| Streaming | Yes | No |

License

MIT (same as original model).