{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/7"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"},{"label":"Speech","url":"https://skillfed.io/packages/category/multimedia-sound-audio-speech"}],"enrichment":{"capability":"Lhotse prepares multimodal (speech, audio, video, image, text) data for machine learning model training with flexible pipelines, on-the-fly augmentation, and efficient data loading.","skillfed_tags":["speech-processing","data-preparation","multimodal"],"use_cases":["Prepare speech recognition training datasets with on-the-fly mixing and augmentation to reduce storage overhead.","Build multimodal pipelines combining audio, text, image, and video for end-to-end model training.","Implement distributed multi-node training with data randomization and de-duplication across workers.","Create standard data recipes for common speech corpora to streamline reproducible research.","Combine multiple datasets with flexible blending and bucketing for efficient batching during training."],"what_it_does":"Lhotse is a Python library for preparing multimodal data (speech, audio, video, image, text) for training machine learning models. It sits alongside k2 as part of the next-generation Kaldi speech processing ecosystem. The library introduces the concept of audio cuts\u2014logical segments that can be mixed, truncated, and padded on-the-fly\u2014to minimize storage while supporting flexible training data construction. It handles data randomization and de-duplication for distributed training, provides standard recipes for common corpora, and integrates with PyTorch through task-specific Dataset classes.\n\nLhotse supports both pre-computed feature storage (with optional compression via lilcom) and on-the-fly feature extraction and augmentation. Data and metadata are stored in human-readable text manifests and exposed through convenient Python classes. The library offers efficient sequential I/O formats (Lhotse Shar, similar to webdataset) and state-of-the-art dataloading algorithms like dataset blending and bucketing. It runs on Python 3.8+ across Linux, macOS, and Windows, with dependencies on torch, numpy, audioread, SoundFile, and other standard tools.","worth_installing":"Yes. Lhotse is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and solves a real problem in multimodal data preparation for speech and audio ML. The low install friction and permissive license make it accessible. Install it if you are building speech, audio, or multimodal training pipelines; skip it if you only need simple audio I/O without data augmentation or complex dataset orchestration."},"id":"lhotse","links":{"html":"https://skillfed.io/packages/lhotse","md":"https://skillfed.io/packages/lhotse.md","pypi":"https://pypi.org/project/lhotse/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-20","license_spdx":null,"license_treatment":"permissive","name":"lhotse","python_support":"supports_current","summary":"Data preparation for speech processing models training."},"popularity":{"monthly_downloads":1304236,"position":4082,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.33.0"}