Step AudioEdit X: Edit Words, Emotions, Style & Pace with AI

Step AudioEdit X reshapes how I work with voice. It doesn’t stop at cloning a voice; it lets me edit spoken words inside an audio clip, rewrite lines without re-recording, infuse emotions, switch styles, control pacing, and sculpt the performance to match the intent.

If you have the hardware for it, this tool turns speech editing into a precise, controllable process. It demands a strong GPU plan for at least 30 GB of VRAM for smooth operation. With the right setup, it runs locally through a clean web interface and gives you full control over output.

In this guide, I walk through what it is, what it does well, and exactly how to install it and get it running on your machine. I also cover the quick-start workflow for editing and generating speech.

What Is Step AudioEdit X?

Step AudioEdit X is a local speech editing and voice cloning tool. It lets you:

• Change spoken words inside existing recordings without re-recording.
• Clone a voice from a short audio sample.
• Add emotion and style settings to a generated voice.
• Adjust pacing and delivery to fit a script.

It runs on your computer with GPU acceleration, and it loads models locally after a one-time setup. There’s also a hosted demo you can test, but usage limits apply there.

Overview of Step AudioEdit X

Key Features of Step AudioEdit X

• Edit words inside an audio clip without re-recording.
• Rewrite lines and regenerate speech that matches the target voice.
• Control emotion, style, and pacing for natural delivery.
• Clone from an uploaded source voice to maintain consistent tone across edits.
• Local processing with a web interface for repeatable, controllable results.

System Requirements

Hardware

• NVIDIA GPU with at least 30 GB VRAM for stable performance.

Software

• Python 3.10.11
• CUDA Toolkit (matching your GPU drivers)
• FFmpeg (added to PATH)
• Git for Windows
• Microsoft Visual Studio (Build Tools)
• PyTorch (matching your CUDA version; installed after environment setup)

Notes

• The tool pushes GPU memory hard. Lower VRAM may cause failures or force you to use smaller models and settings.
• You can test a limited demo on Hugging Face Space before installing locally.

Installation Options

Option 1: Try the Hosted Demo

• Use the Hugging Face Space for a quick test.
• Expect usage limits and lower performance than a tuned local install.

Option 2: Install Locally (Recommended for Full Control)

• Follow the step-by-step instructions below for a Windows setup with GPU acceleration.

Local Installation: Step-by-Step

1) Get the Code from GitHub

• Go to the project’s GitHub page.
• Click the Code button and copy the repository URL.

Open the folder or drive where you want the project to live. In the folder path bar, type “cmd” and press Enter to open a command window in that location.

Run:

git clone REPO_URL
cd CLONED_FOLDER

Replace REPO_URL with the repository link and CLONED_FOLDER with the folder name that git creates.

2) Create and Activate a Virtual Environment (Python 3.10.11)

Ensure Python 3.10.11 is installed and on PATH.

Create a virtual environment:

python -m venv venv

Activate it:

.\venv\Scripts\activate

Keep this environment active for all subsequent steps.

3) Install PyTorch for Your CUDA Version

Go to the official PyTorch “Get Started” page and copy the pip install command that matches your CUDA toolkit and Python version.

For example:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu<CUDA_VERSION>

If your exact CUDA build isn’t listed, use the closest official command and adjust the version string to match your installation.

4) Prepare Requirements and Install Dependencies

In the project’s main folder, open the requirements file. Make the following edits:

• Remove torch and torchvision entries from the list.
• Remove the version pin from torchaudio (or remove torchaudio entirely if it’s included with version constraints).
• Save the file.

Install the remaining requirements:

pip install -r requirements.txt

This ensures PyTorch packages you installed separately remain intact and compatible with your CUDA toolkit.

5) Download the Models

On the project’s main page, locate the commands provided to download the models (there are three separate commands).

Run each command one by one in the active virtual environment. For example:

python scripts/download_model_1.py
python scripts/download_model_2.py
python scripts/download_model_3.py

Use the exact commands from the repository documentation. These downloads can be large and may take time.

6) First Run and Resolve Missing Modules

Start the application using the main entry point referenced in the repository:

python MAIN_SCRIPT.py

If the app reports missing Python modules, copy the install commands shown in the error messages and run them. For example:

pip install MISSING_PACKAGE_NAME

Repeat until the import errors are resolved.

7) Install SoX and Update PATH (If Prompted)

If the app or processing steps require SoX:

• Download the SoX zip build for Windows.
• Extract it to a stable location (for example, C:\sox).
• Add the SoX “bin” folder to your system PATH and Environment Variables.

After updating PATH, close and reopen your command window (or re-activate the virtual environment) so the changes take effect:

.\venv\Scripts\activate

8) Launch the Web UI

Run the main command again:

python MAIN_SCRIPT.py

When the terminal shows a Local URL (for example, http://127.0.0.1:7860), copy it into a web browser. The Step AudioEdit X interface will load.

9) Create a One-Click Launcher (Optional)

To make startup easier:

• In the project’s main folder, create a new text file.
• Add commands to activate the virtual environment and start the app. For example:

@echo off
cd /d "FULL_PATH_TO_PROJECT"
call .\venv\Scripts\activate
python MAIN_SCRIPT.py
pause

• Save the file as run.bat.

Next time, double-click run.bat to launch the app without retyping commands.

Using Step AudioEdit X

Start with a Source Voice

• Upload an audio file with the voice you want to match.
• This sets the reference tone for cloning and editing.

Enter Your Text

• Type the line you want spoken.
• For editing an existing clip, type the corrected text corresponding to the section you need changed.

Choose Emotion, Style, and Pacing

• Select emotion and style settings that fit the content.
• Adjust pacing for timing and emphasis.

Generate the Audio

• Start the cloning or editing process from the interface.
• Review the result and iterate if needed.

Detailed Setup Checklist

Prerequisites Installed

• Python 3.10.11 installed and on PATH.
• CUDA Toolkit installed and matching your GPU drivers.
• FFmpeg installed and on PATH.
• Git for Windows installed.
• Microsoft Visual Studio Build Tools installed.

Environment Ready

• Repository cloned.
• Virtual environment created and activated.
• PyTorch installed for your CUDA version.

Dependencies Complete

• requirements.txt edited to remove torch and torchvision, and to remove the torchaudio version pin if present.
• pip install -r requirements.txt executed without errors.
• Model download commands completed.

First Run Successful

• Missing modules installed as prompted.
• SoX installed and PATH updated if required.
• Local URL appears on launch and loads in a browser.

Step-by-Step Guide: From Zero to First Output

1. Clone the repository:

• Use git clone in your target folder.
• cd into the project directory.

2. Set up Python:

• Create and activate a venv with Python 3.10.11.

3. Install PyTorch:

• Use the official pip command for your CUDA version.

4. Clean requirements:

• Remove torch and torchvision entries and torchaudio version pin.
• Install remaining packages with pip install -r requirements.txt.

5. Get the models:

• Run the three provided model download commands.

6. Run the app once:

• Start the main script.
• Install any missing modules that appear in error messages.

7. Install SoX if prompted:

• Download, extract to C:, and add to PATH.
• Re-activate venv.

8. Launch the UI:

• Run the main script again.
• Open the Local URL in your browser.

9. Create a run.bat:

• Add commands to activate venv and start the app.
• Save and use for quick launching.

10. Produce audio:

• Upload a source voice.
• Enter text.
• Set emotion, style, and pacing.
• Generate and review.

Notes on Performance and Stability

VRAM Headroom

• Aim for ~30 GB VRAM for steady runs, larger models, and fewer out-of-memory errors.

Dependency Versions

• Keep PyTorch aligned with your CUDA toolkit.
• Avoid conflicting torch packages by removing them from requirements.txt before bulk installs.

Environment Variables

• FFmpeg and SoX must be on PATH if the app calls them from the system.

Troubleshooting: Common Pitfalls

• ImportError on first run:
  • Install the missing packages using the pip commands suggested in the traceback.
• CUDA mismatch:
  • Reinstall PyTorch with the correct CUDA build and confirm driver compatibility.
• Command not found for FFmpeg or SoX:
  • Add the binary folder to PATH and restart the terminal.
• Out-of-memory GPU errors:
  • Close other GPU-heavy apps, reduce model size or settings, or use a GPU with more VRAM.

Conclusion

Step AudioEdit X gives me precise control over voice editing. I can rewrite words inside a recording, generate new lines in the same voice, adjust emotion and style, and fine-tune pacing so the read matches the intent. With a strong GPU, proper CUDA/PyTorch pairing, FFmpeg, and a few model downloads, it runs locally through a browser-based interface.

Follow the sequence above clone, set up Python and PyTorch, clean requirements, download models, resolve missing modules, add SoX if needed, and launch. Once you see the local URL, the rest is straightforward: upload a source voice, enter your text, set emotion and pacing, and render the result.