LinguaLab 3.6.1

A multilingual text and voice processing toolkit

LinguaLab

LinguaLab is a comprehensive multilingual text and voice processing toolkit designed for language translation, speech recognition, and text processing tasks. The package provides robust tools for translating text between languages and transcribing audio/video files using advanced AI services.

Features

• Text Translation:

  • Multi-language text translation using Google Translate API
  • Automatic language detection
  • Fallback to alternative translation services
  • Support for bulk translations and nested text structures
  • Configurable translation providers and parameters

• Speech Recognition:

  • Audio/video file transcription using IBM Watson Speech-to-Text
  • Support for multiple audio formats (WAV, MP3, etc.)
  • High-accuracy transcription with confidence scoring
  • Batch processing capabilities
  • Configurable transcription parameters

• Language Processing:

  • Comprehensive language detection
  • Pronunciation assistance
  • Confidence scoring for translations
  • Error handling and fallback mechanisms

• Defensive Programming:

  • Automatic nested list flattening for text inputs
  • Comprehensive parameter validation
  • Enhanced error handling with detailed diagnostics
  • Type safety with modern Python annotations

Installation

Prerequisites

Before installing, please ensure the following dependencies are available on your system:

• External Tools (required for full functionality):

  • Microphone access (for speech recognition features)
  • Internet connection (for translation services)

• Required Third-Party Libraries:

  pip install numpy pandas SpeechRecognition googletrans gTTS
  

  Or via Anaconda (recommended channel: conda-forge):

  conda install -c conda-forge numpy pandas
  pip install SpeechRecognition googletrans gTTS
  

• Internal Package Dependencies:

  pip install filewise paramlib
  pip install pygenutils                    # Core functionality
  pip install pygenutils[arrow]             # With arrow support (optional)
  

For regular users (from PyPI)

pip install lingualab

For contributors/developers (with latest Git versions)

# Install with development dependencies (includes latest Git versions)
pip install -e .[dev]

# Alternative: Use requirements-dev.txt for explicit Git dependencies
pip install -r requirements-dev.txt
pip install -e .

Benefits of the new approach:

• Regular users: Simple pip install lingualab with all dependencies included
• Developers: Access to latest Git versions for development and testing
• PyPI compatibility: All packages can be published without Git dependency issues

If you encounter import errors:

1. For PyPI users: The package should install all dependencies automatically. If you get import errors, try:

   pip install --upgrade lingualab
   

2. For developers: Make sure you've installed the development dependencies:

   pip install -e .[dev]
   

3. Common issues:

   • Missing dependencies: For regular users, all dependencies are included. For developers, use pip install -e .[dev]
   • Python version: Ensure you're using Python 3.10 or higher
   • Speech recognition: Ensure microphone access is granted for speech features

Verify Installation

To verify that your installation is working correctly:

try:
    import LinguaLab
    from filewise.file_operations.path_utils import find_files
    from pygenutils.arrays_and_lists.data_manipulation import flatten_list
    from paramlib.global_parameters import COMMON_DELIMITER_LIST
    
    print("✅ All imports successful!")
    print(f"✅ LinguaLab version: {LinguaLab.__version__}")
    print("✅ Installation is working correctly.")
    
except ImportError as e:
    print(f"❌ Import error: {e}")
    print("💡 For regular users: pip install lingualab")
    print("💡 For developers: pip install -e .[dev]")

Usage

Text Translation Example

from LinguaLab.text_translations import translate_string

# Translate a single phrase
result = translate_string(
    phrase_or_words="Hello, how are you?",
    lang_origin="en",
    lang_translation="es"
)
print(result.text)  # "Hola, ¿cómo estás?"

# Translate multiple phrases
phrases = ["Good morning", "Good afternoon", "Good evening"]
results = translate_string(
    phrase_or_words=phrases,
    lang_origin="en",
    lang_translation="fr"
)
for result in results:
    print(result.text)

# Handle nested lists automatically
nested_phrases = [
    ["Hello", "Goodbye"],
    ["Thank you", "Please"],
    "Welcome"
]
results = translate_string(
    phrase_or_words=nested_phrases,
    lang_origin="en",
    lang_translation="de"
)

Language Detection Example

from LinguaLab.text_translations import translate_string

# Detect language of text
detection = translate_string(
    phrase_or_words="Bonjour, comment allez-vous?",
    lang_origin="auto",
    procedure="detect",
    text_which_language_to_detect="Bonjour, comment allez-vous?"
)
print(f"Detected language: {detection.lang}")
print(f"Confidence: {detection.confidence}")

Speech Recognition Example

from LinguaLab.transcribe_video_files import save_transcription_in_file

# Note: Requires IBM Watson API credentials
# Set up your API_KEY and SERVICE_ID in the module

# The module automatically processes WAV files in the specified directory
# and can save transcriptions to text files

Project Structure

The package is organised as a focused language processing toolkit:

LinguaLab/
├── text_translations.py      # Text translation and language detection
├── transcribe_video_files.py # Speech recognition and transcription
├── __init__.py              # Package initialisation
└── README.md                # Package documentation

Key Functions

translate_string()

Purpose: Translate text between languages using multiple translation services

Key Features:

• Supports single strings, lists, and nested lists of text
• Automatic fallback between translation services
• Language detection capabilities
• Configurable translation parameters
• Comprehensive error handling

Parameters:

• phrase_or_words: Text to translate (supports nested lists)
• lang_origin: Source language code
• lang_translation: Target language code (default: "en")
• procedure: "translate" or "detect"
• provider: Translation service provider
• print_attributes: Whether to print detailed results

save_transcription_in_file()

Purpose: Save speech transcription results to text files

Key Features:

• Automatic file extension handling
• Progress reporting
• Error handling and validation
• Flexible output formatting

Advanced Features

Defensive Programming

• Nested List Support: Automatically flattens complex nested text structures
• Parameter Validation: Comprehensive input validation with detailed error messages
• Type Safety: Modern Python type annotations (PEP-604) for better IDE support
• Error Handling: Detailed error reporting for debugging

Service Integration

• Google Translate: Primary translation service with automatic fallback
• IBM Watson: Speech-to-text transcription service
• Alternative Services: Support for multiple translation providers
• Connection Management: Robust handling of service availability

Performance Optimisation

• Batch Processing: Efficient handling of multiple texts
• Service Fallback: Automatic switching between translation services
• Resource Management: Proper cleanup and memory management

Supported Languages

Translation Services

• Google Translate: 100+ languages supported
• Microsoft Translator: Enterprise-grade translation
• MyMemory: Free translation service
• LibreTranslate: Open-source translation

Speech Recognition

• IBM Watson: 20+ languages supported
• Multiple Audio Formats: WAV, MP3, FLAC, etc.
• Real-time Processing: Stream-based transcription

Version Information

Current version: 3.6.1

Recent updates

• 3.6.x: NumPy / Pandas ≥2.2.3 packaging, aligned filewise / pygenutils / paramlib floors, requests / beautifulsoup4 / lxml added to pyproject.toml, and conda post-link for PyPI-only speech/translation deps (see changelog).
• 3.5.x: Type-hint and docstring standardisation, defensive list handling, and broader NLP workflow polish (see changelog for detail).

Error Handling

The package provides comprehensive error handling:

• ValueError: For invalid language codes or parameters
• RuntimeError: For service connection issues
• AttributeError: For service availability problems
• SyntaxError: For malformed input parameters

System Requirements

• Python: 3.10 or higher
• Internet Connection: Required for translation and speech services
• Memory: Sufficient RAM for processing large text batches
• Storage: Space for transcription output files

Dependencies

Core Dependencies

• SpeechRecognition: Speech recognition capabilities
• googletrans: Google Translate integration
• gTTS: Google Text-to-Speech (if needed)

Internal Dependencies

• filewise: File operations and path utilities
• pygenutils: Utility functions and data manipulation
• paramlib: Parameter and configuration management

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Guidelines

• Follow existing code structure and language processing best practices
• Add comprehensive docstrings with parameter descriptions
• Include error handling for all service operations
• Test with various languages and text formats
• Update changelog for significant changes

License

This project is licensed under the MIT License — see the LICENSE file in the repository for details.

Acknowledgments

• Google Translate Team for the translation API
• IBM Watson Team for speech recognition services
• Python NLP Community for ecosystem development
• Open Source Translation Providers for free services

Contact

For any questions or suggestions, please open an issue on GitHub or contact the maintainers.

Troubleshooting

Common Issues

1. Translation Service Errors:

   • Check internet connection
   • Verify language codes are valid
   • Try alternative translation providers

2. Speech Recognition Issues:

   • Ensure IBM Watson credentials are set
   • Check audio file format compatibility
   • Verify API service availability

3. Import Errors:

   • Run pip install -e . for development setup
   • Check Python version compatibility
   • Verify all dependencies are installed

Getting Help

• Check function docstrings for parameter details
• Review service provider documentation
• Open an issue on GitHub for bugs or feature requests