{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/18"},{"label":"Speech","url":"https://skillfed.io/packages/category/multimedia-sound-audio-speech"}],"enrichment":{"capability":"Finds the most probable alignment between a text sequence and a speech sequence using monotonic alignment search, with Cython-optimized and NumPy implementations.","skillfed_tags":["speech-synthesis","audio-alignment","sequence-matching"],"use_cases":["Align text phonemes to speech frames in text-to-speech synthesis pipelines.","Find frame-level correspondences between text and audio in voice conversion systems.","Compute monotonic alignments for duration prediction models in speech generation.","Prototype or extend speech synthesis models that require text-speech synchronization.","Integrate alignment search into audio-text multimodal learning tasks."],"what_it_does":"Monotonic Alignment Search (MAS) is a specialized algorithm extracted from the Glow-TTS text-to-speech project that solves the problem of finding the optimal alignment between a text sequence and a speech sequence. It takes two tensors\u2014a value matrix and a mask\u2014and returns a path indicating which text tokens correspond to which speech frames, respecting the constraint that alignments must be monotonic (never backtrack in time). The package provides two implementations: a Cython-optimized version for performance and a pure NumPy fallback for compatibility.\n\nThe package is designed for researchers and developers working on speech synthesis, voice conversion, or other audio-text alignment tasks. It depends only on NumPy at runtime, though PyTorch is required to prepare the input tensors. The implementation is extracted directly from the original Glow-TTS repository and is intended for reuse in other projects that need this specific alignment algorithm.","worth_installing":"Yes, if you are building or extending a text-to-speech or speech alignment system and need a standalone, well-tested implementation of monotonic alignment search. The MIT license is permissive, install friction is moderate (compiled wheels available, PyTorch must be managed separately), and there are no known security vulnerabilities. However, maintenance is minimal (aging status, 303 days since last release); use it as a stable library component rather than expecting active development or rapid bug fixes."},"id":"monotonic-alignment-search","links":{"html":"https://skillfed.io/packages/monotonic-alignment-search","md":"https://skillfed.io/packages/monotonic-alignment-search.md","pypi":"https://pypi.org/project/monotonic-alignment-search/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-15","license_spdx":null,"license_treatment":"permissive","name":"monotonic-alignment-search","python_support":"supports_current","summary":"Monotonically align text and speech"},"popularity":{"monthly_downloads":131042,"position":11612,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.1"}