How to Clone Your Own Voice Using Open Source AI

Higgs Audio is an open source text-to-audio foundation model developed by Boson AI. Trained on over 10 million hours of audio data, it excels in expressive, natural-sounding speech generation. Unlike many closed-source TTS systems, Higgs Audio is available on GitHub under the Apache 2.0 license, meaning you can run it locally, modify it, and integrate it into your own projects free of charge.

The model supports zero-shot voice cloning, meaning it can replicate a voice it has never been trained on, simply by being given a short reference audio clip. It outperforms commercial alternatives like ElevenLabs and GPT-4o-mini-TTS on key benchmarks, particularly in emotional expressiveness. The latest version, Higgs Audio V2.5, condenses the model to 1 billion parameters while actually improving speed and accuracy over its 3B predecessor.

Before getting started, make sure you have the following in place:

**Hardware:** A GPU with at least 24GB of VRAM is strongly recommended for optimal performance. NVIDIA GPUs work best, as the model leverages CUDA. While CPU-only inference is technically possible, it will be significantly slower.

**Software:** - Python 3.10 - Git - pip or an alternative package manager (conda, uv, or venv) - A CUDA-compatible environment (NVIDIA Deep Learning Container is recommended)

**Audio Recording Setup:** - A decent quality microphone (a USB condenser mic works well, but even a modern smartphone mic is sufficient) - A quiet room with minimal background noise and echo - Audio recording software (Audacity is free and excellent)

Start by cloning the Higgs Audio repository from GitHub and installing its dependencies. Open your terminal and run the following commands:

```bash git clone https://github.com/boson-ai/higgs-audio.git cd higgs-audio ```

Next, install the required Python packages. You can choose from several environment management options. Using a virtual environment is recommended to avoid dependency conflicts:

**Option A — venv (recommended for most users):** ```bash python3 -m venv higgs_audio_env source higgs_audio_env/bin/activate pip install -r requirements.txt pip install -e . ```

**Option B — conda:** ```bash conda create -y --prefix ./conda_env --override-channels --strict-channel-priority --channel "conda-forge" "python==3.10.*" conda activate ./conda_env pip install -r requirements.txt pip install -e . ```

**Option C — uv (fastest install times):** ```bash uv venv --python 3.10 source .venv/bin/activate uv pip install -r requirements.txt uv pip install -e . ```

Once installation completes, your environment is ready. The model weights (approximately 3.6GB total for the LLM and audio adapter) will be downloaded automatically from HuggingFace on first use.

This is arguably the most important step in the entire process. The quality of your voice clone depends almost entirely on the quality of your reference audio. Higgs Audio uses zero-shot voice cloning, which means it extracts the acoustic fingerprint of your voice directly from the clip you provide.

**Record at least 60 seconds of natural speech.** A full minute gives the model enough acoustic information to capture the nuances that make your voice distinctly yours. Shorter clips can work, but you may notice less consistency in the output.

**Export your file as a WAV (recommended) at 44.1kHz or 24kHz, mono or stereo.** Save it as `my_voice.wav` and place it in the `examples/voice_prompts/` directory of the higgs-audio repository:

```bash cp /path/to/my_voice.wav examples/voice_prompts/my_voice.wav ```

You're now ready to use this file as a reference for voice cloning.

With your reference audio in place, you can now generate cloned speech. The `examples/generation.py` script is the primary interface for this. Here's the command to clone your voice and have it speak a sentence of your choice:

```bash python3 examples/generation.py \ --transcript "Welcome to my channel. Today I'm going to walk you through something truly exciting." \ --ref_audio my_voice \ --temperature 0.3 \ --out_path cloned_output.wav ```

Let's break down each argument: - `--transcript` — The text you want your cloned voice to speak. - `--ref_audio` — The name of your reference audio file (without the `.wav` extension), located in `examples/voice_prompts/`. - `--temperature` — Controls the randomness of the audio generation (more on this below). - `--out_path` — Where to save the output audio file.

If you have multiple GPUs and want to specify a particular one, add `--device_id 0` (or whichever GPU index you prefer).

The first run will download model weights from HuggingFace. Subsequent runs will use the cached weights and generate audio much faster.

Temperature is the single most impactful parameter for voice quality, and getting it right is what separates a robotic, unnatural output from a convincingly human one.

In audio generation models, temperature controls how much randomness is introduced during generation. A higher temperature produces more varied, creative outputs, but too high, and the voice becomes unstable, inconsistent, or garbled. A lower temperature produces more deterministic, tight outputs, but too low, and the result can sound flat or overly measured.

For voice cloning, use a temperature between 0.2 and 0.4.