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 /pyproject.toml

autotools

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	[build-system]
	requires = ["hatchling"]
	build-backend = "hatchling.build"

	[project]
	name = "omnivoice"
	version = "0.1.5"
	description = "OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models"
	readme = "README.md"
	license = "Apache-2.0"
	requires-python = ">=3.10"
	authors = [{name = "Han Zhu"}]
	keywords = [
	"tts",
	"text-to-speech",
	"speech-synthesis",
	"zero-shot",
	"multilingual",
	"diffusion",
	"voice-cloning",
	]
	classifiers = [
	"Intended Audience :: Science/Research",
	"Intended Audience :: Developers",

	"Topic :: Scientific/Engineering :: Artificial Intelligence",
	"Topic :: Multimedia :: Sound/Audio :: Speech",

	"Operating System :: OS Independent",
	"Programming Language :: Python :: 3",
	]
	dependencies = [
	"torch>=2.4",
	"torchaudio>=2.4",
	"transformers>=5.3.0",
	"accelerate",
	"pydub",
	"gradio",
	"tensorboardX",
	"webdataset",
	"numpy",
	"soundfile",
	"librosa",
	]

	[project.optional-dependencies]

	eval = [
	"jiwer==3.1.0", # WER
	"s3prl", # Speech representation (HuBERT etc.)
	"funasr", # ASR models
	"zhconv", # Chinese character normalization
	"zhon", # Chinese punctuation
	"unidecode", # Unicode normalization
	]

	[project.scripts]
	omnivoice-infer = "omnivoice.cli.infer:main"
	omnivoice-infer-batch = "omnivoice.cli.infer_batch:main"
	omnivoice-demo = "omnivoice.cli.demo:main"

	[project.urls]
	Homepage = "https://github.com/k2-fsa/OmniVoice"
	Repository = "https://github.com/k2-fsa/OmniVoice"
	"Bug Tracker" = "https://github.com/k2-fsa/OmniVoice/issues"

	[tool.uv.sources]
	# Install PyTorch with CUDA support on Linux/Windows (CUDA doesn't exist for Mac).
	# NOTE: We must explicitly request them as `dependencies` above. These improved
	# versions will not be selected if they're only third-party dependencies.
	torch = [
	{ index = "pytorch-cuda", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
	]
	torchaudio = [
	{ index = "pytorch-cuda", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
	]

	[[tool.uv.index]]
	name = "pytorch-cuda"
	# Use PyTorch built for NVIDIA Toolkit version 12.8.
	# Available versions: https://pytorch.org/get-started/locally/
	url = "https://download.pytorch.org/whl/cu128"
	# Only use this index when explicitly requested by `tool.uv.sources`.
	explicit = true

	[tool.uv]
	constraint-dependencies = [
	"torch==2.8.0",
	"torchaudio==2.8.0",
	]

	[tool.hatch.build.targets.sdist]
	include = ["omnivoice"]

	[tool.hatch.build.targets.wheel]
	packages = ["omnivoice"]