MuseTalk Colab: How to Run MuseTalk on Google Colab?

Not everyone has an NVIDIA GPU sitting in their machine. If you’re on a Mac, a laptop with integrated graphics, or a work computer you can’t install Python on, Google Colab looks like the obvious answer. Free GPU access, everything runs in a browser tab, nothing touches your own drive.

It does work. It’s also fiddlier than most people expect, and the reasons why are worth understanding before you spend an afternoon on it.

Let’s cover what actually exists, what to watch out for, and how to get a working setup.

There Is No Official MuseTalk Colab Notebook

The MuseTalk team at Lyra Lab has not published an official Colab notebook. What they provide is the GitHub repository, the Hugging Face model weights, and a hosted Gradio demo on Hugging Face Spaces. Colab is not part of their supported setup.

Why Colab Is Appealing Anyway

MuseTalk needs an NVIDIA GPU with CUDA. The free tier of Colab typically offers a T4 GPU with 16GB of VRAM, which is comfortably more than the 4GB minimum the MuseTalk team tested against. You also skip the entire local setup process: no conda environment, no CUDA version matching, no PATH configuration for FFmpeg.

For someone who wants to test MuseTalk properly, with real settings and their own files, rather than through a public demo queue, Colab is the natural middle ground between the hosted Space and a full local install.

Option 1: The Community Notebook

The most referenced MuseTalk notebook comes from camenduru, a developer well known for packaging AI projects into ready to run notebooks.

You can find it at github.com/camenduru/MuseTalk-jupyter, with an “Open in Colab” badge that launches it directly.

How it works in practice:

  1. Open the notebook through the Colab badge
  2. Set your runtime to a T4 GPU through the runtime settings menu
  3. Run the setup cell and wait for the install to complete
  4. A Gradio interface link appears in the output
  5. Open that link, upload your face and audio, and generate

Before you rely on it, know this. The notebook dates from the MuseTalk 1.0 era, has only a handful of commits, and clones a fork rather than the official repository. It pins specific package versions, including a prebuilt mmcv wheel compiled for a particular Python version.

That last detail is the fragile part. Colab updates its default Python and CUDA versions periodically, and pinned wheels compiled against older versions stop installing when that happens.

A notebook that worked perfectly a year ago can fail on the first cell today, through no fault of its author.

Option 2: Build Your Own Notebook

More work upfront, but you control every version and you’re following the official installation steps rather than someone else’s snapshot of them.

Open a fresh Colab notebook, set the runtime to a T4 GPU, and work through these cells.

Cell 1: Confirm you actually have a GPU

!nvidia-smi

If this prints nothing useful, your runtime isn’t set to GPU. Fix that before continuing, since everything after this depends on it.

Cell 2: Clone the repository

!git clone https://github.com/TMElyralab/MuseTalk.git
%cd MuseTalk

Cell 3: Install the requirements

!pip install -r requirements.txt

Cell 4: Install the MMLab packages

!pip install --no-cache-dir -U openmim
!mim install mmengine
!mim install "mmcv>=2.0.1"
!mim install "mmdet>=3.1.0"
!mim install "mmpose>=1.1.0"

This is the cell most likely to give you trouble, and the next section explains why.

Cell 5: Confirm FFmpeg

!ffmpeg -version

Colab usually ships with FFmpeg already available. If it isn’t:

!apt-get install -y ffmpeg

Cell 6: Download the model weights

!sh ./download_weights.sh

This pulls several gigabytes, so give it time. Don’t interrupt it, since a partial download causes confusing errors later.

Cell 7: Upload your files

from google.colab import files
uploaded = files.upload()

Or mount your Google Drive instead, which is the better option for anything larger than a short test clip:

from google.colab import drive
drive.mount('/content/drive')

Cell 8: Point the config at your files

Edit configs/inference/test.yaml so video_path and audio_path reference wherever you put your uploads.

Cell 9: Run inference

!sh inference.sh v1.5 normal

Cell 10: Save your output somewhere permanent

!cp -r results/ /content/drive/MyDrive/musetalk_results/

Do this before your session ends. More on that shortly.

The Part That Usually Breaks: MMLab and Version Drift

If MuseTalk on Colab fails for you, there’s a good chance it happens at the mmcv step. Understanding why saves a lot of frustration.

The official MuseTalk setup recommends Python 3.10 and CUDA 11.7, with PyTorch 2.0.1 built against CUDA 11.8. Colab does not give you those versions. It ships whatever Python, CUDA, and PyTorch build Google currently defaults to, and that moves over time.

The mmcv package compiles against your specific combination of Python, PyTorch, and CUDA. When those don’t match what a prebuilt wheel expects, pip attempts to build from source, which on Colab either takes an extremely long time or fails outright.

Things that help:

  • Check what you’re actually running before installing anything, with !python --version and !nvcc --version
  • Try mim install rather than plain pip install for MMLab packages, since mim resolves compatible versions for you
  • If a specific mmcv version fails, loosening the version constraint sometimes finds a wheel that exists for your environment
  • Search the MMLab documentation for the wheel index matching your exact PyTorch and CUDA combination

There’s no permanent fix here, because the moving part is Colab’s environment rather than MuseTalk. This is the fundamental tradeoff of running a project with pinned dependency expectations on a platform that updates underneath you.

Colab Limits Worth Knowing About

Sessions end. Free tier runtimes disconnect after periods of inactivity and have a maximum lifetime regardless. When a session ends, everything on the runtime disappears, including your installed packages, downloaded weights, and generated videos.

That means reinstalling every time. A new session means running the whole setup again, including the multi gigabyte weight download. This is the single biggest practical annoyance of the Colab route, and it’s why saving finished output to Drive immediately matters so much.

GPU access isn’t guaranteed. Free tier GPU availability varies with demand. During busy periods you may get a slower GPU or none at all.

Usage limits apply. Heavy or sustained use of the free tier can result in temporary restrictions on GPU access.

Files need somewhere to go. Mount Google Drive early and write your results there. Discovering that a two hour session’s output vanished on disconnect is a lesson nobody enjoys learning firsthand.

Is Colab Actually the Right Choice?

Depends what you’re doing.

Colab suits you if you don’t own an NVIDIA GPU, you want to run occasional jobs rather than daily work, you want more control than a public demo allows, and you’re comfortable troubleshooting a Python environment when something breaks.

Colab works against you if you plan to use MuseTalk regularly, since reinstalling everything each session gets tiresome fast. It’s also awkward for long or batch jobs that might outlast a session, and for anything sensitive, since your files pass through a cloud environment.

Two alternatives worth weighing:

The hosted Hugging Face Space requires no setup at all and is the fastest way to see MuseTalk working. The tradeoff is reduced output quality, a shared queue, and no control over settings.

A local install is more work once and then simply works. The hardware bar is lower than people assume, since MuseTalk has been run successfully on a laptop GPU with 4GB of VRAM. If you own any reasonably recent NVIDIA card, installing locally is usually the better long term answer.

Common Colab Problems

No GPU detected

Set the runtime type to a GPU accelerator before running anything, then rerun the first cell to confirm.

mmcv installation fails or hangs

The version drift problem described above. Check your Python and CUDA versions, use mim rather than plain pip, and try loosening the version constraint.

Weight download stops partway

Rerun the download script. If a specific file keeps failing, download it manually. The face parsing weights are hosted on Google Drive rather than Hugging Face, which is the most common single point of failure.

Session disconnected and everything is gone

Expected behaviour rather than a fault. Mount Drive at the start of your session and copy results across as soon as they’re generated.

Out of memory during generation

Shorten your input clip and confirm you’re running in fp16 mode. A T4’s 16GB is generous for MuseTalk, so this usually points at an unusually long or high resolution input.

Everything installs but inference fails on paths

Confirm you’re running commands from inside the MuseTalk directory. The %cd MuseTalk in Cell 2 applies to that cell, and Colab sometimes resets your working directory in unexpected ways. Adding %cd /content/MuseTalk at the top of your inference cell removes the ambiguity.