NVIDIA/NeMo-text-processing

NeMo text processing for ASR and TTS

What it solves

It provides a way to handle text normalization and inverse text normalization, which is essential for converting text into a format suitable for speech synthesis or processing.

How it works

The package uses a combination of techniques, including Weighted Finite-State Transducers (WFST) for grammar customization and a hybrid approach that can integrate with PyTorch for advanced normalization.

Who it’s for

Developers and researchers working on NLP and speech-related AI, specifically those needing to prepare text for downstream tasks like text-to-speech (TTS) systems.

Highlights

  • Text normalization: Converting written text (e.g., "$10") into a spoken form (e.g., "ten dollars").
  • Inverse text normalization: Converting spoken forms back into written forms.
  • Support for grammar customization via WFST.
  • Hybrid normalization options using PyTorch.

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