AI Voice Clone with Coqui XTTS-v2

Free voice cloning for creators using Coqui XTTS-v2 on Google Colab. Clone your voice with just 2-5 minutes of audio for consistent narration. Complete guide to build your own notebook. Non-commercial use only.

Overview

Coqui XTTS-v2 is a multilingual text-to-speech model with zero-shot voice cloning capabilities. It uses a Transformer architecture similar to GPT-style autoregressive models combined with a VQ-VAE (Vector Quantized Variational AutoEncoder) to generate realistic speech in 16+ languages from just a few seconds of reference audio.

How It Works

Voice Cloning Process:

• Audio Analysis: The model extracts acoustic features from your reference audio (pitch, tone, speaking style, cadence)
• Voice Encoding: These features are encoded into a speaker embedding vector
• Text-to-Speech Generation: Given new text, the model generates speech that matches your voice characteristics
• Waveform Synthesis: The output is synthesized into a high-quality audio file

Technical Stack:

• Model: XTTS-v2 (1.8GB pretrained model from Coqui AI)
• Framework: PyTorch 2.1.0 with CUDA support
• Inference: Runs on Google Colab’s free T4 GPU (16GB VRAM)
• Sample Rate: 24kHz output
• Languages: Supports 16 languages including English, Spanish, French, German, Japanese, and more

Why Google Colab?

Google Colab provides free access to GPU-accelerated computing, which is essential for running large neural network models like XTTS-v2. Voice synthesis on CPU would take significantly longer (10-20x slower). The free T4 GPU tier is sufficient for generating voice clones without requiring local hardware or paid cloud services.

Intended Use Cases

• Consistent narration for storytelling, tutorials, and educational content
• Editing specific audio sections without full re-recording
• Creating voiceovers when recording conditions aren’t ideal
• Maintaining voice consistency across multiple recording sessions
• Generating placeholder audio for video editing workflows

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Requirements

• Google account (for Google Colab and Google Drive)

• 2-5 minutes of clean audio in WAV format

• Best results: clear speech, minimal background noise

• Mix of scripted and natural speaking recommended

• Google Colab with T4 GPU runtime (available with free plan but subject to usage limits)

• No Python installation needed locally (runs in Colab)

Prerequisites

🎤Audio File

• .wav or .mp3 sample audio file uploaded to your Google Drive
• 2-5 minutes in length
• 16-bit or 24-bit, 44.1kHz or 48kHz sample rate recommended

Converting Audio to WAV

macOS (built-in tool):

# afconvert comes pre-installed on macOS
afconvert -f WAVE -d LEI16 input.m4a output.wav

Mac/Linux/Windows (use ffmpeg):

Install ffmpeg first:

# macOS with MacPorts
sudo port install ffmpeg

# Ubuntu/Debian
sudo apt-get install ffmpeg

# Windows (with Chocolatey)
choco install ffmpeg

Convert audio:

ffmpeg -i input.m4a -ar 24000 output.wav

Supported input formats: .m4a, .mp3, .mp4, .mov, and most audio/video formats.

See Notes: section below for Hardware Recommendations

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🎬 Video Guide

This repository was created as a companion to the YouTube video covering:

• Coqui XTTS-v2 setup with Google Colab

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🚀 Quick Start

1. Open the Colab notebook:
2. Enable GPU: Runtime → Change runtime type → T4 GPU
3. Run cells 1-4 in order (takes ~5 minutes first time)
4. Upload your audio file when prompted
5. Edit the text you want generated in Cell 6
6. Download your cloned voice!

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Cell 1 - Install Python 3.11:

!apt-get update -qq
!apt-get install -y python3.11 python3.11-venv python3.11-dev

Note: Python 3.11 is the only recommended version tested for compatibility with this notebook. Other versions may trigger runtime errors.

Cell 2 - Create virtual environment and install TTS:

!python3.11 -m venv /content/py311env
!/content/py311env/bin/pip install --upgrade pip
!/content/py311env/bin/pip install TTS

Install additional requirements:

Transformers with BeamSearchScorer

!/content/py311env/bin/pip install "transformers<4.50.0"

PyTorch 2.1.x

!/content/py311env/bin/pip install torch==2.1.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118

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Cell 3 - Create a Python Script to Load the Model:

%%writefile /content/load_model.py
import os
os.environ['MPLBACKEND'] = 'Agg'
from TTS.api import TTS
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2').to(device)
print(f'Model loaded on {device}!')

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Cell 4 - Run the Model with Python 3.11:

!/content/py311env/bin/python /content/load_model.py

When prompted: Type y and press Enter to agree to the non-commercial license (CPML).

Cell 5 - Upload your audio file:

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

Cell 6 - Generate cloned voice:

%%writefile /content/generate_voice.py
import os
os.environ['MPLBACKEND'] = 'Agg'
from TTS.api import TTS
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2').to(device)

# Insert your text here to replace the example "..."
text = "He became as good a friend, as good a master, and as good a man as the good old city knew."

# Generate speech
tts.tts_to_file(
text=text,
speaker_wav="/content/drive/MyDrive/Your_Audio_File.wav",  # <-- change this to match your audio file
language="en",
file_path="/content/cloned_voice.wav"
)

print("Voice generated: /content/cloned_voice.wav")

Note: Replace Your_Audio_File.wav with your own recorded audio sample filename

Cell 6b - Run the script:

!/content/py311env/bin/python /content/generate_voice.py

Cell 7 - Download your cloned voice:

from google.colab import files
files.download("/content/cloned_voice.wav")

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Notes:

Recording Equipment (Minimum Recommended)

Recommended for best results while recording audio samples:

• USB audio interface (we used an Arturia MiniFuse 2)
• Condenser or shotgun microphone (we used an Audio-Technica AT875R)
• Quiet recording environment

Acceptable minimum:

• Smartphone (eg. iPhone 8+) in a quiet room
• USB microphone with cardioid pattern
• Desktop/Laptop built-in mic in very quiet environment (quality will be lower)

Background noise:

• More important than mic quality. Record in a quiet space.

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PyTorch & CUDA Compatibility

This notebook uses:

torch==2.1.0
torchaudio==2.1.0

installed from the CUDA 11.8 wheel index: https://download.pytorch.org/whl/cu118

CUDA 11.8 is compatible with Colab’s common T4 GPU hardware. If a different GPU is assigned, PyTorch may fallback to CPU

Transformers Version Requirement

This notebook also pins:

transformers < 4.50.0

to ensure BeamSearchScorer remains available and XTTS-v2 loads correctly

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License

This repository’s code and documentation: MIT License

However: The Coqui XTTS-v2 model used in this tutorial is licensed under the Coqui Public Model License (CPML), which restricts usage to non-commercial purposes only. See https://coqui.ai/cpml for details.

Acknowledgements

This project builds upon:

• Coqui TTS - The XTTS-v2 model and framework
• Google Colab - Free GPU infrastructure
• PyTorch - Deep learning framework

We’re grateful to the open-source community for making voice cloning accessible to all creators.

⚠️ GPU Usage Limits

Colab Free Plan Limitations:

• In the free version of Colab notebooks can run for at most 12 hours, depending on availability and usage patterns.
• Colab Pro and Pay As You Go offer increased compute availability based on your compute unit balance.
• If unavailable, wait 12+ hours or consider Colab Pro ($9.99/month) for increased access.

Support

Questions? Check the video tutorial or open an issue!