Welcome to lhotse’s documentation!

Contents:

• Getting started
  • About
  • Installation
  • Examples
• Representing a corpus
  • Recording manifest
  • Supervision manifest
  • Standard data preparation recipes
  • Adding new corpora
• Cuts
  • Overview
  • Types of cuts
  • CLI
• Feature extraction
  • Storing features
  • Feature normalization
  • Python usage
  • CLI usage
  • Kaldi compatibility caveats
• Executing tasks in parallel
• PyTorch Datasets
  • A quick re-cap of PyTorch’s data API
  • About Lhotse’s Datasets and Samplers
  • Restoring sampler’s state: continuing the training
  • Resumable Stateful Dataloading (Indexed)
  • Batch I/O: pre-computed vs. on-the-fly features
  • Handling random seeds
  • Customizing sampling constraints
  • Dataset’s list
  • Sampler’s list
  • Input strategies’ list
  • Augmentation - transforms on cuts
  • Augmentation - transforms on signals
  • Collation utilities for building custom Datasets
  • Dataloading seeding utilities
• Indexed Manifests and IteratorNodes
  • What an indexed manifest is
  • Creating indexes
  • Reading indexed data
  • How iterator composition works
  • Three important capabilities
  • Property summary for built-in iterators
  • Checkpointing: graph tokens
  • Why this enables O(1) restore
  • Worker-process restore
  • Implementing a new IteratorNode
  • Checkpointable does not imply O(1)
  • Minimal stateless transform node
  • Stateful node with RNG or cursor state
  • When a node should not support exact restore
  • Runtime metadata rules
  • Testing new IteratorNodes
• Kaldi Interoperability
  • Data import/export
  • Kaldi feature extractors
  • Python
  • CLI
  • Example
• Command-line interface
  • lhotse
• API Reference
  • Audio loading, saving, and manifests
  • Supervision manifests
  • Lhotse Shar – sequential storage
  • Feature extraction and manifests
  • Augmentation
  • Cuts
  • Recipes
  • Kaldi conversion
  • Others

Indices and tables

• Index

• Module Index

• Search Page