twat-speech is a Python library, part of the twat ecosystem, designed to provide a solid foundation for speech processing tasks. It serves as both a functional module and an extensible template for developers looking to build, test, and maintain speech-related applications using modern Python tools and best practices.

The twat-speech library was created to offer a standardized, extensible foundation for speech data manipulation and analysis. Many projects require common preprocessing, feature extraction, or transformation steps for speech signals. This library aims to encapsulate such functionalities in a reusable manner, integrated with modern Python development tools like Hatch, Ruff, Mypy, and uv for a streamlined development experience. It also serves as an example of how to structure a Python library with comprehensive QA, CI/CD, and clear contribution guidelines.

• Modern Python Development: Built with PEP 621 compliance using Hatch.
• Accelerated Workflows: Supports uv for fast environment and dependency management (used by Hatch automatically if available).
• High Code Quality: Enforced through Ruff for linting and formatting, and Mypy for static type checking.
• Automated Versioning: Git tag-based versioning powered by hatch-vcs.
• Robust Testing: Comprehensive test suite using pytest.
• CI/CD: Automated testing, building, and releasing via GitHub Actions.
• Pre-commit Hooks: For maintaining code standards before commits.
• twat Ecosystem Plugin: Designed to integrate as a plugin.

This part of the documentation is for a wider audience, including those who may want to use twat-speech or understand its general purpose.

twat-speech aims to simplify the development of applications that handle speech data. While its core processing logic is designed to be extended, it provides a structured environment for common tasks such as:

• Preprocessing audio data (e.g., normalization, resampling – future capability)
• Extracting acoustic features (e.g., MFCCs, spectrograms – future capability)
• Interfacing with speech recognition models or services (future capability)
• Managing configurations for different speech processing pipelines.

The library is built with a focus on robustness, configurability, and a clean development experience.

twat-speech is for:

• Python Developers: Anyone building applications that involve audio or speech data.
• Researchers: Those who need a reliable framework for experimenting with speech processing algorithms.
• Hobbyists: Individuals exploring speech technology and looking for a well-structured starting point.
• Users of the twat ecosystem: If you're already using other twat tools, twat-speech integrates naturally as a plugin.

• Modern Tooling: Leverages Hatch, Ruff, Mypy, and pytest, ensuring code quality and maintainability.
• Extensible by Design: Its core functions are meant to be expanded or replaced by more specific speech processing logic.
• Best Practices Template: Serves as an excellent example of setting up a Python library with comprehensive QA, CI/CD, and clear contribution guidelines.
• Accelerated Development: Helps you get started quickly on speech-related projects without boilerplate setup.
• twat Ecosystem Integration: Designed to work as a plugin within the broader twat family of tools.

You can install twat-speech from PyPI using pip or uv:

# Using pip
pip install twat-speech

# Or using uv (recommended for faster performance)
uv pip install twat-speech

The primary way to use twat-speech is by importing it into your Python projects. The library centers around a process_data function and a Config class for managing settings.

import twat_speech
from twat_speech import Config, process_data

# Initialize configuration (optional)
# This allows you to define specific parameters for your speech processing tasks.
config = Config(
    name="my_custom_settings",
    value="alpha_params",
    options={"mode": "detailed_analysis", "threshold": 0.75}
)

# Sample data: This would typically be a list of audio file paths,
# raw audio data, or other speech-related inputs.
# The exact nature depends on the implemented processing logic.
data_to_process = ["path/to/audio1.wav", "another_audio_sample.mp3"]

# Process data
try:
    # The process_data function is currently a placeholder.
    # In a fully implemented version, it would perform speech-specific tasks
    # based on the input data and configuration.
    print(f"Attempting to process: {data_to_process} with config: {config.name}")
    result = process_data(data_to_process, config=config, debug=True)
    print(f"Processing successful. Result: {result}")

    # Example of processing without a specific configuration
    result_no_config = process_data(["simple_item.flac"])
    print(f"Processing with no config. Result: {result_no_config}")

except ValueError as e:
    # This error is raised if the input data list is empty.
    print(f"Error during processing: {e}")
except Exception as e:
    # Catch other potential errors
    print(f"An unexpected error occurred: {e}")

Note: The process_data function in the current version contains placeholder logic. As the library evolves, this function will be updated to perform actual speech processing tasks. The example above illustrates its intended interface.

twat-speech includes a demonstration script within the library that you can run to see its basic operation and logging output. This is not a full-fledged CLI application but serves to illustrate the library's current capabilities.

To run the demonstration, navigate to the root directory of the project (if you have cloned the repository) or ensure twat_speech is in your Python path, then execute:

# If you are in the root of the cloned repository:
python src/twat_speech/twat_speech.py

Or, if the package is installed in your environment:

python -m twat_speech.twat_speech

This will run the main() function in twat_speech.py, which executes a few examples of calling process_data, including one that intentionally triggers an error, showcasing the logging and error handling.

This section provides a deeper dive into the codebase, development practices, and contribution guidelines for twat-speech.

The core logic of twat-speech resides primarily in src/twat_speech/twat_speech.py.

• Config Dataclass:

  from dataclasses import dataclass
  from typing import Any
  
  @dataclass
  class Config:
      name: str
      value: str | int | float
      options: dict[str, Any] | None = None

  This simple dataclass is used to pass structured configuration settings to processing functions. It holds a name for the configuration set, a primary value, and an optional dictionary options for more granular settings.

• process_data(data: list[Any], config: Config | None = None, *, debug: bool = False) -> dict[str, Any]: This is the main function intended for data processing.

  • Parameters:
    • data: list[Any]: A list of items to be processed. The exact type of items (e.g., file paths, raw data) will depend on the specific implementation of the processing logic.
    • config: Config | None = None: An optional Config object to guide the processing.
    • debug: bool = False: A keyword-only argument. If True, it enables detailed debug logging for the function call.
  • Current Logic: As of the current version, this function contains placeholder logic. It:
    1. Adjusts the global logger level to DEBUG if debug=True, and restores it afterward.
    2. Raises a ValueError if the input data list is empty.
    3. Logs information about the data being processed and the configuration being used (if any).
    4. Simulates item processing by iterating through the input data.
    5. Returns a dictionary containing a status message, counts of items received and processed, and the name of the configuration used.
  • Extensibility: This function is designed to be the primary point for implementing actual speech processing algorithms. Future development will replace the placeholder logic with concrete operations.

• main() -> None: This function serves as a runnable demonstration of the library's capabilities. When src/twat_speech/twat_speech.py is executed as a script, main() is called. It showcases:

  1. How to initialize Config objects.
  2. How to call process_data with and without a Config, and with debug mode enabled/disabled.
  3. An example of process_data handling an expected ValueError (when called with empty data). It utilizes the logging module to provide informative output about its operations.

• Logging: The module configures basic logging using logging.basicConfig at the module level. The process_data function demonstrates dynamic adjustment of logging levels based on its debug parameter.

The pyproject.toml file is central to the project's structure, dependency management, and tooling.

• Project Management with Hatch:
  • Hatch is used for build processes, environment management, and running scripts.
  • It uses hatchling as the build backend and hatch-vcs to derive the project version from Git tags (e.g., v0.1.0). The version is written to src/twat_speech/__version__.py.
• Code Quality Tools:
  • Ruff: Used for extremely fast Python linting and code formatting. Configuration in pyproject.toml.
  • Mypy: Used for static type checking. Configuration in pyproject.toml.
• Testing with Pytest:
  • pytest is the testing framework. Tests are in tests/. Configuration in pyproject.toml.
• Codebase Structure Overview:

  .
  ├── .github/                # GitHub Actions workflows (CI/CD)
  ├── src/
  │   └── twat_speech/        # Main source code for the library
  │       ├── __init__.py
  │       ├── __version__.py  # Version managed by hatch-vcs
  │       └── twat_speech.py  # Core logic
  ├── tests/                  # Test suite
  │   └── test_twat_speech.py
  ├── .gitignore
  ├── .pre-commit-config.yaml # Pre-commit hook configurations
  ├── LICENSE
  ├── README.md               # This file
  └── pyproject.toml          # Project metadata and build configuration (PEP 621)
  

We welcome contributions! Please adhere to the following guidelines.

1. Prerequisites:

   • Python 3.10+
   • Git
   • Hatch
   • (Highly Recommended) uv for significantly faster dependency management. Hatch will use uv automatically if available.

2. Clone the Repository:

   git clone https://github.com/twardoch/twat-speech.git
   cd twat-speech

3. Install Hatch (if not already installed):

   pip install hatch  # Or: uv pip install hatch

4. Install uv (Recommended): Follow instructions at astral.sh/uv#installation. E.g., for macOS/Linux:

   curl -LsSf https://astral.sh/uv/install.sh | sh

5. Activate Hatch Environment: This creates a virtual environment and installs project dependencies.

   hatch shell

6. Install Pre-commit Hooks: This helps maintain code quality by running checks before each commit.

   pre-commit install

All commands should be run from within the activated Hatch environment (hatch shell).

• Run Tests:

  hatch run test  # or simply: pytest

• Run Tests with Coverage:

  hatch run test-cov

• Linting and Formatting (Ruff):
  • Check for issues (no changes made): hatch run lint:style
  • Format code and fix issues: hatch run lint:fmt
• Static Type Checking (Mypy):

  hatch run lint:typing

• Build the Package: Creates wheel and sdist packages in dist/.

  hatch build

• PEP 8: Follow PEP 8 style guidelines.
• Formatting & Linting: Code is automatically formatted and linted by Ruff. Ensure hatch run lint:fmt passes.
• Type Hinting: Use type hints. Ensure hatch run lint:typing (Mypy) passes.
• Comments: Write clear comments for complex logic.

• Write tests for new features/fixes in tests/.
• Ensure all tests pass (hatch run test) and aim for high coverage.

• Follow Conventional Commits.
  • Examples: feat: add whisper ASR integration, fix: correct RMS energy calculation.

• Create branches from main: feature/your-feature-name or bugfix/issue-id.

• Submit PRs to the main branch.
• Provide clear descriptions and ensure CI checks pass.
• Link to relevant issues.

• Semantic Versioning (SemVer: MAJOR.MINOR.PATCH) via hatch-vcs from Git tags.
• Maintainers handle tagging and releases (automated via GitHub Actions).

• Documentation: https://github.com/twardoch/twat-speech#readme
• Issue Tracker: https://github.com/twardoch/twat-speech/issues
• Source Code: https://github.com/twardoch/twat-speech

twat-speech is distributed under the terms of the MIT license. See the LICENSE file for details.