Instructions to use autotools/ai_video_studio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autotools/ai_video_studio with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="autotools/ai_video_studio",
	filename="runtime/Auto Movie Reviewer/models/Phi-3.5-mini-balanced.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use autotools/ai_video_studio with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf autotools/ai_video_studio:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf autotools/ai_video_studio:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf autotools/ai_video_studio:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf autotools/ai_video_studio:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf autotools/ai_video_studio:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf autotools/ai_video_studio:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf autotools/ai_video_studio:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf autotools/ai_video_studio:Q4_K_M

Use Docker

docker model run hf.co/autotools/ai_video_studio:Q4_K_M

LM Studio
Jan
Ollama
How to use autotools/ai_video_studio with Ollama:
```
ollama run hf.co/autotools/ai_video_studio:Q4_K_M
```

Unsloth Studio

How to use autotools/ai_video_studio with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for autotools/ai_video_studio to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for autotools/ai_video_studio to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for autotools/ai_video_studio to start chatting

Atomic Chat new
Docker Model Runner
How to use autotools/ai_video_studio with Docker Model Runner:
```
docker model run hf.co/autotools/ai_video_studio:Q4_K_M
```

Lemonade

How to use autotools/ai_video_studio with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull autotools/ai_video_studio:Q4_K_M

Run and chat with the model

lemonade run user.ai_video_studio-Q4_K_M

List all available models

lemonade list

ai_video_studio / runtime /omnivoice-python /omnivoice-source /docs /community-projects.md

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Community Projects

The following projects are built and maintained by the community. We appreciate all contributions! Note that these projects are not officially supported by the OmniVoice team.

If you have a project you'd like to add, please open a PR.

ComfyUI-OmniVoice-TTS — ComfyUI custom node for OmniVoice text-to-speech generation.
vLLM-Omni — A framework for efficient model inference with omni-modality model. Supports OmniVoice serving.
pyVideoTrans — Video translation tool with dubbing & subtitles. Supports OmniVoice as a TTS engine.
MLX-Audio — TTS, STT, and STS library built on Apple's MLX framework. Supports OmniVoice among other models for efficient speech processing on Apple Silicon.
RealtimeTTS — Converts text to speech in realtime. Supports OmniVoice as a TTS engine.
TTS-WebUI — Gradio web UI for multiple TTS models. Supports OmniVoice as one of its backends.
OmniVoice-Studio — Desktop application for OmniVoice voice generation.
omnivoice-server — OpenAI-compatible HTTP server for serving OmniVoice via /v1/audio/speech. Supports voice profiles for persistent cloning, sentence-level streaming, and optional Bearer auth.
omnivoice-rs — GPU-first Rust workspace for OmniVoice inference, parity validation, CLI execution, and an OpenAI-compatible HTTP server built with Candle.
omnivoice-trtllm — Deploy OmniVoice TTS model using TensorRT-LLM and Triton Inference Server on Modal, faster than PyTorch.
- Auris — Offline audiobook reader for EPUB, PDF, and TXT with local OmniVoice TTS, character-aware voices, and per-book narrator control.