Usage

The “Usage” section of the werpy Python package encompasses a comprehensive guide on leveraging its diverse modules for various aspects of automatic speech recognition (ASR) evaluation.

Starting with the “Installation” page, users are provided with clear and concise instructions to seamlessly set up werpy in their Python environment.

The subsequent pages delve into the intricacies of text normalization modules, empowering users to preprocess and standardize reference and hypothesis texts effectively.

The “Word Error Rate Modules” and “Weighted Word Error Rate Modules” pages equip practitioners with powerful tools for evaluating ASR system outputs, accompanied by illustrative examples and Python code snippets for practical implementation.

The inclusion of the “Summarization Modules” page introduces users to a sophisticated feature allowing for a detailed breakdown of evaluation results, fostering a nuanced understanding of system performance.

Overall, Each page within the “Usage” section not only provides valuable examples and code snippets but also offers insights into why these modules are indispensable in the realm of ASR evaluations, enhancing the overall usability and effectiveness of the werpy package.

Please find the links to each section below:

• Normalization
  • Normalize a list of text
• Word Error Rate
  • Simple Word Error Rate Calculation
  • Cumulative Word Error Rate
  • Sequence-level Word Error Rates
  • Summary
• Weighted Word Error Rate
  • Cumulative Weighted Word Error Rate
  • Sequence-level Weighted Word Error Rate
  • Summary
• Summarization
  • Summary Analysis
  • Weighted Summary Analysis
  • Overall