🎤 TTS Tool - Text-to-Speech Converter

A comprehensive, production-ready Text-to-Speech (TTS) converter powered by state-of-the-art AI models. Features multiple TTS engines, web interface, batch processing, dataset integration, and seamless deployment options.

✨ Key Features

🤖 Multiple TTS Models

• Microsoft SpeechT5: High-quality English TTS with speaker control
• Facebook MMS-TTS: Multilingual support (20+ languages), optimized for speed
• Suno Bark: Creative multilingual TTS with emotions and sound effects

🎵 Audio Processing

• Multi-format Support: WAV, MP3, FLAC, OGG, and more
• Advanced Processing: Audio normalization, effects, and enhancement
• Batch Processing: Convert multiple texts efficiently with parallel workers
• Format Conversion: Seamless conversion between audio formats

🎭 Advanced Features

• Emotion Control: Add emotions (happy, sad, excited, whisper) to speech
• Speaker Voice Control: Different speaker embeddings with SpeechT5
• Language Support: 20+ languages with automatic detection
• Creative Audio: Sound effects and expressive audio generation

📊 Dataset Integration

• Hugging Face Datasets: LJ Speech, Common Voice, VCTK support
• Dataset Analysis: Statistical analysis and visualization
• Training Preparation: Format data for TTS model training

🌐 Web Interface

• Beautiful Gradio Interface: Intuitive, real-time web interface
• Real-time Generation: Instant audio playback and download
• Multiple Tabs: Single conversion, batch processing, settings, and more
• Mobile Responsive: Works on desktop and mobile devices

🚀 Quick Start

Option 1: Docker (Recommended)

# Clone and run with Docker
git clone https://github.com/yourusername/tts-tool.git
cd tts-tool

# Build and run
docker build -t tts-tool .
docker run -p 7860:7860 tts-tool

# Access web interface
open http://localhost:7860

Option 2: Python Installation

# Clone repository
git clone https://github.com/yourusername/tts-tool.git
cd tts-tool

# Install dependencies
pip install -r requirements.txt

# Launch web interface
python main.py --web

# Access web interface
open http://localhost:7860

Option 3: Pip Package

# Install from PyPI
pip install tts-tool

# Run directly
tts-tool --web

📱 Screenshots

Web Interface

Batch Processing

Command Line Interface

💻 Usage Examples

Python API

Basic TTS

from tts_tool import TTSProcessor

# Initialize processor
processor = TTSProcessor("speecht5")

# Generate speech
text = "Hello, this is a test of the text-to-speech system."
audio_file = processor.generate_speech(text, "output.wav")

print(f"Audio generated: {audio_file}")

Advanced TTS with Emotions

from tts_tool import AdvancedTTSProcessor

# Initialize advanced processor
processor = AdvancedTTSProcessor("bark")

# Generate with emotions
text = "This is an exciting announcement!"

emotions = ["happy", "excited", "neutral", "whisper"]
for emotion in emotions:
    output_file = processor.generate_with_emotion(text, emotion)
    print(f"Generated {emotion} version: {output_file}")

Batch Processing

from tts_tool import BatchTTSProcessor

# Initialize batch processor
batch_processor = BatchTTSProcessor("speecht5", max_workers=4)

# Process multiple texts
texts = [
    "First sentence to convert.",
    "Second sentence to convert.",
    "Third sentence to convert."
]

results = batch_processor.process_batch(
    texts, 
    output_dir="batch_output",
    progress_callback=lambda completed, total: print(f"Progress: {completed}/{total}")
)

print(f"✅ Processed {len(results)} texts")

Command Line Interface

Single Text Conversion

# Convert text to speech
python main.py --text "Hello world" --output hello.wav

# With emotion
python main.py --text "This is exciting!" --emotion happy --output excited.wav

# With specific model
python main.py --text "Bonjour le monde" --model mms_tts --language fr --output french.mp3

Batch Conversion

# Batch processing from file
python main.py --batch texts.txt --output-dir batch_output

# With custom settings
python main.py --batch texts.txt --output-dir batch_output --workers 8 --emotion neutral

Audio Format Conversion

# Convert audio format
python main.py --convert-audio input.wav --format mp3 --bitrate 192k

# Batch conversion
python main.py --convert-audio *.wav --format flac

Dataset Analysis

# Analyze LJ Speech dataset
python main.py --analyze-dataset lj_speech --analysis-output lj_analysis.json

# Analyze Common Voice
python main.py --analyze-dataset common_voice --language en --analysis-output cv_analysis.json

Web Interface Usage

1. 🎵 Single Conversion Tab

   • Enter text in the text area
   • Select TTS model and emotion
   • Click "Generate Speech"
   • Play and download audio file

2. 📦 Batch Processing Tab

   • Upload text file or paste multiple texts
   • Configure batch settings
   • Start processing
   • Download results as ZIP

3. 🔄 Audio Converter Tab

   • Upload audio file
   • Select target format and settings
   • Convert and download

4. 📊 Dataset Analysis Tab

   • Select dataset to analyze
   • View statistics and visualizations
   • Download analysis reports

5. ⚙️ Settings Tab

   • Configure default models
   • View system information
   • Test audio output

📊 Model Comparison

Feature	SpeechT5	MMS-TTS	Bark
Quality	High	Medium	Very High
Speed	Medium	Fast	Slow
Languages	English	20+	13
Memory Usage	Medium	Low	High
Special Features	Speaker Control	Multilingual	Creative Audio
Best For	Professional TTS	Edge Deployment	Creative Content
GPU Required	Optional	No	Recommended
Batch Processing	✅	✅	✅

🛠️ Installation Options

Development Installation

# Clone repository
git clone https://github.com/yourusername/tts-tool.git
cd tts-tool

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Install in development mode
pip install -e .

# Run tests
pytest tests/

GPU Support

# Install PyTorch with CUDA support
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118

# Run with GPU acceleration
python main.py --web --device cuda

Docker with GPU Support

# Build GPU-enabled Docker image
docker build -f Dockerfile.gpu -t tts-tool:gpu .

# Run with GPU support
docker run --gpus all -p 7860:7860 tts-tool:gpu

🏗️ Architecture

tts-tool/
├── src/tts_tool/              # Core Python package
│   ├── __init__.py            # Package initialization
│   ├── tts_processor.py       # Core TTS processing
│   ├── advanced_tts.py        # Advanced features
│   ├── batch_processor.py     # Batch processing
│   ├── dataset_integration.py # Dataset handling
│   ├── audio_processing.py    # Audio format conversion
│   └── gradio_interface.py    # Web interface
├── examples/                   # Usage examples
├── tests/                      # Test suite
├── docs/                       # Documentation
├── main.py                     # CLI entry point
├── requirements.txt            # Core dependencies
├── requirements-dev.txt        # Development dependencies
├── Dockerfile                  # Docker configuration
├── docker-compose.yml          # Docker Compose setup
├── DEPLOYMENT.md              # Deployment guide
└── DEPLOY_GUIDE.md            # Detailed deployment instructions

🔧 Configuration

Environment Variables

# Core settings
export TTS_DEVICE="cpu"                    # cpu, cuda, mps
export TTS_CACHE_DIR="./models_cache"      # Model cache directory
export TTS_MAX_TEXT_LENGTH="5000"         # Maximum text length

# Web interface
export TTS_WEB_PORT="7860"                # Web interface port
export TTS_WEB_HOST="0.0.0.0"             # Web interface host
export TTS_WEB_SHARE="false"              # Create shareable link

# Performance
export TTS_WORKERS="4"                    # Parallel workers
export TTS_BATCH_SIZE="8"                 # Batch size
export TTS_MEMORY_LIMIT="8GB"             # Memory limit

# Models
export TTS_DEFAULT_MODEL="speecht5"       # Default TTS model
export TTS_MODEL_CACHE_SIZE="3"           # Models to cache

# Logging
export TTS_LOG_LEVEL="INFO"               # Logging level
export TTS_LOG_FILE="./logs/tts.log"      # Log file path

Configuration File

Create config/settings.yaml:

models:
  default: "speecht5"
  cache_dir: "./models_cache"
  device: "cpu"

web:
  host: "0.0.0.0"
  port: 7860
  share: false

performance:
  workers: 4
  batch_size: 8
  memory_limit: "8GB"

logging:
  level: "INFO"
  file: "./logs/tts.log"

🧪 Testing

Run Test Suite

# Run all tests
pytest

# Run with coverage
pytest --cov=src/tts_tool

# Run specific test
pytest tests/test_tts_processor.py

# Run integration tests
pytest tests/integration/

Test Examples

# Test single conversion
python -c "
from src.tts_tool import TTSProcessor
processor = TTSProcessor('speecht5')
audio = processor.generate_speech('Test', 'test.wav')
print('✅ Single conversion works!')
"

# Test batch processing
python -c "
from src.tts_tool import BatchTTSProcessor
processor = BatchTTSProcessor('speecht5')
results = processor.process_batch(['Hello', 'World'], 'test_output')
print(f'✅ Batch processing works! Processed {len(results)} items')
"

🚀 Deployment

Quick Start Options

Option 1: Verify Setup (Recommended first step)

# Run setup verification
bash setup_verification.sh

# Or use Python validator
python deployment_validator.py

Option 2: Fast Deploy (5 minutes)

# Hugging Face Spaces (recommended for sharing)
cp -r deployment_configs/huggingface/* ./
git add . && git commit -m "Deploy TTS Tool" && git push origin main

Option 3: Local Development

# Clone and run
git clone https://github.com/yourusername/tts-tool.git
cd tts-tool
pip install -r requirements.txt
python main.py --web

Comprehensive Deployment Guides

• 📋 Final Package Overview: FINAL_DEPLOYMENT_PACKAGE.md
• ⚡ Quick Start Guide: QUICK_START_GUIDE.md - Deploy in 5-15 minutes
• ✅ Deployment Checklist: DEPLOYMENT_CHECKLIST.md - Complete validation checklist
• 📊 Deployment Status: DEPLOYMENT_STATUS.md - Track deployment progress
• 🚀 Full Deployment Guide: DEPLOYMENT.md - Comprehensive deployment instructions
• 📖 Step-by-Step Guide: DEPLOY_GUIDE.md - Detailed deployment procedures
• 🏭 Production Operations: production/README.md - Production deployment and monitoring

Cloud Platform Deployments

Platform	Time	Difficulty	Best For	Guide
Hugging Face Spaces	5 min	Easy	Quick demos & sharing	HF Guide
Streamlit Cloud	10 min	Easy	Python web apps	Streamlit Guide
Render.com	15 min	Easy	Modern cloud deployment	Render Guide
Docker Production	30 min	Medium	Full production stack	Docker Guide
AWS/GCP/Azure	60 min	Advanced	Enterprise deployment	Cloud Guide

Deployment Verification

# 1. Verify system setup
bash setup_verification.sh

# 2. Validate configuration
python deployment_validator.py --verbose

# 3. Test basic functionality
python main.py --text "Test" --output test.wav

# 4. Test web interface
python main.py --web

# 5. Run comprehensive deployment checklist
# See DEPLOYMENT_CHECKLIST.md for complete list

Production Deployment

For production environments with monitoring, scaling, and backup:

# Setup production environment
./production/scripts/setup-env.sh

# Deploy with full monitoring
./production/scripts/deploy.sh production latest

# Monitor with Grafana/Prometheus
# Access: http://localhost:3000 (Grafana)
# Access: http://localhost:9090 (Prometheus)

See production/README.md for complete production deployment guide.

📚 API Reference

Core Classes

TTSProcessor

class TTSProcessor:
    def __init__(self, model_name: str, device: str = "cpu")
    def generate_speech(self, text: str, output_path: str, **kwargs) -> str
    def get_model_info(self) -> dict

AdvancedTTSProcessor

class AdvancedTTSProcessor:
    def __init__(self, model_name: str)
    def generate_with_emotion(self, text: str, emotion: str, output_path: str) -> str
    def set_speaker(self, speaker_id: str)
    def adjust_pace(self, text: str, speed_factor: float, output_path: str) -> str

BatchTTSProcessor

class BatchTTSProcessor:
    def __init__(self, model_name: str, max_workers: int = 4)
    def process_batch(self, texts: List[str], output_dir: str, **kwargs) -> List[dict]
    def export_batch_summary(self, results: List[dict], output_path: str) -> str

AudioFormatConverter

class AudioFormatConverter:
    def convert_format(self, input_path: str, output_path: str, target_format: str, **kwargs) -> bool
    def apply_audio_effects(self, input_path: str, output_path: str, **effects) -> bool
    def get_audio_info(self, audio_path: str) -> dict

Command Line Interface

Global Options

python main.py [OPTIONS]

Web Interface

python main.py --web --port 7860 --share

Text Conversion

python main.py --text "Hello" --output hello.wav --model speecht5

Batch Processing

python main.py --batch texts.txt --output-dir output/ --workers 4

Audio Conversion

python main.py --convert-audio input.wav --format mp3 --bitrate 192k

🎯 Use Cases

Content Creation

• Audiobook Production: Convert written content to audio
• Video Voiceovers: Generate professional voiceovers for videos
• Podcast Generation: Create podcast episodes from scripts
• Presentation Audio: Add audio to slide presentations

Accessibility

• Text Reading: Help visually impaired users read text
• Language Learning: Pronunciation examples for language students
• Educational Content: Audio versions of learning materials

Business Applications

• Customer Service: Automated voice responses
• Voice Mail: Generate professional voicemail greetings
• Training Materials: Convert training documents to audio
• Marketing Content: Create engaging audio advertisements

Development

• API Integration: Integrate TTS into applications
• Voice Assistants: Build conversational interfaces
• Chatbots: Add speech capabilities to chatbots
• Mobile Apps: Speech functionality for mobile applications

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Fork and clone repository
git clone https://github.com/yourusername/tts-tool.git
cd tts-tool

# Setup development environment
python -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt

# Install in development mode
pip install -e .

# Create feature branch
git checkout -b feature/amazing-feature

# Make changes and test
pytest
black src/
flake8 src/

# Commit and push
git commit -m "Add amazing feature"
git push origin feature/amazing-feature

# Create pull request

Contribution Guidelines

• Follow PEP 8 style guidelines
• Write comprehensive tests
• Update documentation
• Add examples for new features
• Ensure backward compatibility

Reporting Issues

• Use GitHub Issues for bug reports
• Include environment details
• Provide minimal reproduction steps
• Attach relevant logs and screenshots

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 TTS Tool Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

🙏 Acknowledgments

• Microsoft: SpeechT5 model and research
• Meta AI: MMS-TTS multilingual model
• Suno AI: Bark creative TTS model
• Hugging Face: Model hosting and transformers library
• Gradio: Web interface framework
• PyTorch: Deep learning framework
• Community: All contributors and users

📊 Project Statistics

📞 Support & Community

• 📧 Email: support@tts-tool.com
• 💬 Discord: Join our community
• 🐛 Bug Reports: GitHub Issues
• 💡 Feature Requests: GitHub Discussions
• 📖 Documentation: Full Documentation
• 📚 Examples: Example Gallery

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