What software should I use for high-quality text-to-speech in videos?
I’ve noticed that some videos are fully artificially narrated, and the sound quality is quite high. Are there simple tools for creating such narration that work for free and offline?
Most available options either require internet connection, are paid, or offer mediocre sound quality. Some offline tools require deep learning and possibly training the model yourself.
I’m willing to train the model myself as I have a GPU for this purpose.
The best free offline programs for high-quality text-to-speech in videos include Balabolka, Panopreter Basic, and eSpeak NG, which offer good synthesis quality without an internet connection. For more advanced users with a graphics card, you can use open-source neural network-based TTS models such as Coqui TTS or Mozilla TTS, which allow you to train your own voice models.
Table of Contents
- Free Offline TTS Tools
- Advanced Solutions with Model Training
- Comparison of Best Programs
- Practical Selection Recommendations
- Setup and Usage Guide
- Tips for Improving Voice Quality
Free Offline TTS Tools
Free offline programs for high-quality text-to-speech are divided into several categories depending on ease of use and capabilities.
Balabolka
Balabolka is one of the most popular free offline TTS tools with Russian language support. The program uses system-installed SAPI4, SAPI5, and Microsoft Speech Platform voices, allowing for decent voice quality.
- Advantages:
- Completely free and doesn’t require internet connection
- Support for multiple text file formats
- Ability to save in WAV, MP3, OGG and other audio formats
- Basic voice speed, pitch, and tone adjustment
- Batch file processing
Balabolka is ideal for quick voiceovers of small texts and presentations, but the voice quality may be inferior to neural network solutions.
Panopreter Basic
Panopreter Basic offers a simple interface for text-to-speech using Windows system voices. The program includes a text editor and features for reading aloud and saving audio files.
- Main features:
- Reading texts from various sources
- Voice settings (speed, tone, volume)
- Hotkey support
- Ability to create audiobooks
- Compatible with most Windows versions
eSpeak NG
eSpeak NG is an open-source speech synthesizer supporting many languages, including Russian. Although the voice quality may seem less natural compared to commercial solutions, it compensates with its lightweight design and offline capabilities.
- Features:
- Very small program size
- Support for more than 100 languages
- Voice parameter customization options
- Cross-platform (Windows, Linux, macOS)
- GPL license (completely free to use)
Advanced Solutions with Model Training
For users with a graphics card who are willing to train their own models, there are powerful open-source solutions based on neural networks.
Coqui TTS
Coqui TTS is a modern open-source text-to-speech platform based on PyTorch. It allows training high-quality voice models using GPU.
- Technical specifications:
- Support for Tacotron2, FastSpeech2, VITS architectures
- Fine-tuning capability for specific voices
- Integration with Hugging Face ecosystem
- Model export in various formats
- Active developer community
To get started with Coqui TTS, you’ll need to install Python and necessary dependencies, as well as have access to GPU for accelerated training.
Mozilla TTS (TTS by Mozilla)
Mozilla TTS is another powerful open-source solution from Mozilla Foundation. The project offers flexible capabilities for training and using neural network TTS models.
- Key features:
- Support for various neural network architectures
- Tools for data and audio processing
- Web interface for model testing
- Access to pre-trained models
- Good documentation and examples
OpenVoice
OpenVoice is an innovative voice cloning platform that allows quick model training on short audio recordings. Although the main focus is voice cloning, it can be effectively used for speech synthesis.
- Advantages:
- Training with just 1 minute of audio
- Multilingual support
- Preservation of voice emotional coloring
- Ability to adapt to different accents
- Open source code
Comparison of Best Programs
| Program | Voice Quality | Ease of Use | System Requirements | Russian Language Support |
|---|---|---|---|---|
| Balabolka | Medium | Low | Minimal | Yes |
| Panopreter Basic | Medium | Low | Minimal | Yes |
| eSpeak NG | Low | Low | Minimal | Yes |
| Coqui TTS | High | High | GPU recommended | Yes |
| Mozilla TTS | High | Medium/High | GPU recommended | Yes |
| OpenVoice | Very High | High | GPU required | Yes |
Practical Selection Recommendations
For Beginner Users
If you’re just starting with text-to-speech, it’s recommended to begin with simple tools:
- Balabolka - ideal option for quick start
- Panopreter Basic - good alternative with additional features
- eSpeak NG - if you need support for multiple languages
For Experienced Users with GPU
If you have a graphics card and are willing to spend time on training:
- Coqui TTS - best choice for high-quality synthesis
- Mozilla TTS - flexible platform with good documentation
- OpenVoice - for voice cloning work
When choosing a program, consider not only the voice quality but also the time you’re willing to spend on setup and model training.
Setup and Usage Guide
Installing Balabolka
- Download the installation file from the official website
- Run the installation (process takes a few minutes)
- Install additional Microsoft Speech Platform voices (if needed)
- Launch the program and configure voice parameters
Getting Started with Coqui TTS
- Install Python 3.8 or higher
- Create a virtual environment:
python -m venv tts_env - Activate the environment:
source tts_env/bin/activate(Linux/macOS) ortts_env\Scripts\activate(Windows) - Install Coqui TTS:
pip install TTS - Download a pre-trained model:
tts --list_modelsandtts --model_name "tts_models/ru/tacotron2-DDC" - Start synthesis:
tts --text "Your text" --out_path output.wav
Training Your Own Model with OpenVoice
- Clone the repository:
git clone https://github.com/myshell-ai/OpenVoice.git - Install dependencies:
pip install -e . - Prepare audio data (1-2 minutes of clean voice recording)
- Start training:
python inference_main.py --voice your_voice.wav - Test the model on various texts
Tips for Improving Voice Quality
Text Preprocessing
- Use punctuation for proper pause placement
- Break long texts into paragraphs
- Remove unnecessary characters and formatting
- Check spelling and grammar
Voice Parameter Settings
- Adjust reading speed according to content type
- Regulate pitch for naturalness
- Use pauses for better perception
- Experiment with different voices
Audio Post-processing
- Apply noise reduction if necessary
- Normalize volume
- Add light reverb for depth
- Use equalizer to improve quality
Conclusion
High-quality text-to-speech in videos is possible with free offline tools, the choice of which depends on your requirements and technical capabilities. For a quick start, programs like Balabolka are ideal, while for professional results with a graphics card, modern neural network solutions like Coqui TTS or OpenVoice are recommended.
Main Recommendations:
- Start with simple tools to assess your needs
- Invest time in learning advanced platforms if quality is critically important
- Experiment with different voices and settings to achieve optimal results
- Don’t neglect audio post-processing for better perception
- Keep an eye on updates to open-source projects - they constantly improve
With the right approach and persistence, you can achieve professional voice quality without an internet connection and without significant financial investment.