Sponsored by
Vmake Labs Video Enhancer
-Vmake Labs Video Enhancer uses AI to improve video quality, restore details, reduce blur, and upscale videos to 8K.
Encodes audio to semantic latent (fixed input: 24000 samples)
Acoustic Connector
vibevoice_acoustic_connector.mlpackage
Projects acoustic latent to LLM hidden space
Semantic Connector
vibevoice_semantic_connector.mlpackage
Projects semantic latent to LLM hidden space
LLM
vibevoice_llm.mlpackage
Qwen2-1.5B-based language model
Diffusion Head
vibevoice_diffusion_head.mlpackage
Single-step diffusion denoising
Requirements
Platform
: macOS 14.0+ or iOS 17.0+
Framework
: CoreML (Apple Silicon or Intel with Neural Engine recommended)
Usage
Swift (iOS/macOS)
Use
VibeVoicePipeline.swift
with a directory containing all
.mlpackage
files:
let modelDir =URL(fileURLWithPath: "/path/to/models")
let pipeline =tryVibeVoicePipeline(modelDirectory: modelDir)
// Encode, run LLM, diffusion, decode...
See
vibevoice_config.json
and
vibevoice_pipeline_config.json
for full settings.
Conversion Notes
Conversion was done with coremltools 9.0. Acoustic and semantic encoders use fixed-length (24000 samples) inputs; the LLM was exported via
torch.export
+ custom op registration. See
CONVERSION_RESULTS.md
for details.
License
Refer to the original VibeVoice model license.
Runs of aoiandroid vibevoice-1.5-coreml on huggingface.co
21
Total runs
0
24-hour runs
-1
3-day runs
-8
7-day runs
13
30-day runs
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