---

license: mit

language:

• en
• zh
• es
• pt
• de
• ja
• ko
• fr
• ru
• id
• sv
• it
• he
• nl
• pl
• "no"
• tr
• th
• ar
• hu
• ca
• cs
• da
• fa
• af
• hi
• fi
• et
• el
• ro
• vi
• bg
• is
• sl
• sk
• lt
• sw
• uk
• ms
• hr
• cy
• ka
• ml
• te
• kn
• ta
• sr
• az
• mk
• hy
• bs
• bn
• mr
• ur
• my
• gl
• km
• eu
• ne

tags:

• speech
• asr
• speech-recognition
• gguf
• ggml
• vibevoice
• microsoft
• qwen2

base_model: microsoft/VibeVoice-7B

pipeline_tag: automatic-speech-recognition

---

VibeVoice-7B — GGUF

GGUF conversions of microsoft/VibeVoice-7B for use with the crispasr CLI from CrispStrobe/CrispASR.

VibeVoice-7B is the largest model in Microsoft's VibeVoice family — a 9.3B-parameter speech-LLM (Qwen2.5-7B decoder + dual σ-VAE encoders) with state-of-the-art ASR quality across 50+ languages.

• 60-minute long-form audio in a single forward pass
• Built-in speaker diarization — Speaker IDs per segment
• Word-level timestamps
• Hotword / context injection via --context
• 50+ languages with automatic language detection
• MIT licence

> Update (April 2026): Now includes both ASR (encoder + LM) and TTS (σ-VAE decoder + prediction head). TTS requires ≥Q4_K for good quality — Q3_K is too aggressive for the decoder. For faster/smaller TTS, use VibeVoice-Realtime-0.5B or VibeVoice-1.5B.

Files

| File | Size | Notes |

| --- | ---: | --- |

| vibevoice-7b-q3_k.gguf | 5.7 GB | Q3_K — ASR only (TTS quality too low) |

| vibevoice-7b-q4_0.gguf | 6.7 GB | Q4_0 — fast decode |

| vibevoice-7b-q4_k.gguf | 6.7 GB | Q4_K — recommended default (ASR + TTS) |

| vibevoice-7b-q5_k.gguf | 7.7 GB | Q5_K — higher quality |

| vibevoice-7b-q6_k.gguf | 8.7 GB | Q6_K — near-lossless |

| vibevoice-7b-q8_0.gguf | 10.4 GB | Q8_0 — reference quality |

| vibevoice-7b-f16.gguf | 17.4 GB | F16 — full precision |

> The quantized 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.

Quick Start

# 1. Build CrispASR
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_METAL=ON   # macOS
cmake --build build -j$(nproc)

# 2. Download the quantised GGUF
huggingface-cli download cstr/VibeVoice-7B-GGUF \
    vibevoice-7b-q4_k.gguf --local-dir .

# 3. Transcribe
./build/bin/crispasr --model vibevoice-7b-q4_k.gguf \
    --file audio.wav --backend vibevoice

Audio is automatically resampled from 16 kHz to 24 kHz by the backend.

Architecture

| Component | Details |

| --- | --- |

| LM decoder | Qwen2.5-7B (28 layers, d=3584, 28/4 heads, GQA) |

| Acoustic encoder | 7-stage ConvNeXt σ-VAE, 3200× downsample |

| Semantic encoder | 7-stage ConvNeXt σ-VAE, 3200× downsample |

| Connectors | FC1 → RMSNorm → FC2 (acoustic + semantic) |

| Prediction head | 4-layer DiT with AdaLN modulation |

| Total parameters | ~9.3B |

| Input | 24 kHz mono PCM |

| Tokenizer | Qwen2.5 BPE (152064 tokens, embedded in GGUF) |

Hardware Requirements

| Quantization | RAM (approx) | Notes |

| --- | ---: | --- |

| Q3_K | ~6 GB | Minimum for inference |

| Q4_K | ~7 GB | Recommended |

| Q8_0 | ~11 GB | High quality |

| F16 | ~18 GB | Full precision |

GPU acceleration (Metal/CUDA) is strongly recommended for the 7B model. CPU-only inference is very slow (~10× slower than realtime).

Conversion

Converted from microsoft/VibeVoice-7B safetensors using the streaming memory-mapped converter:

python3 models/convert-vibevoice-stream-gguf.py \
    --input microsoft/VibeVoice-7B \
    --output vibevoice-7b-f16.gguf

# Quantize
./build/bin/crispasr-quantize vibevoice-7b-f16.gguf vibevoice-7b-q4_k.gguf q4_k

The streaming converter (convert-vibevoice-stream-gguf.py) uses memory-mapped tensor access to avoid loading the full 19 GB model into RAM.