VibeVoice-1.5B GGUF

Model variants

The Q8_0/Q4_K files keep the diffusion prediction head, connectors and EOS classifier at full precision (only the LM backbone is quantized). The head runs under classifier-free guidance, so quantizing it could push the first frames onto a wrong trajectory that decodes as a brief non-speech "music"/hum onset before the voice; keeping it full-precision avoids that at a small size cost.

Usage

Architecture

• 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

Quality

Differences from Realtime-0.5B

License

Provenance and EU AI Act Art. 53 note

• Upstream model: microsoft/VibeVoice-1.5B — published by microsoft.
• Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.