fairseq2.data

The data module provides flexible data processing pipelines and utilities for working with various data formats including text, audio, and structured data. It includes high-performance data loaders, text tokenization systems, and specialized processing utilities for machine learning workflows.

Key Features:

  • High-Performance Data Pipelines: Optimized C++-based data loading and processing

  • Text Processing: Comprehensive tokenization and text preprocessing utilities

  • Audio Processing: Tools for audio data loading and feature extraction

  • Structured Data: Support for Parquet, JSON, and other structured formats

  • Memory Efficient: Streaming and batched processing for large datasets

  • Integration: Seamless integration with fairseq2’s training and evaluation systems

Note

The fairseq2 data module is designed for high-throughput machine learning workloads and provides both Python and C++ implementations for performance-critical operations.

Text Processing

Data Pipeline Components

Coming Soon: Documentation for data pipeline components including:

  • DataPipeline: Core pipeline abstraction for chaining data transformations

  • DataLoader: High-performance data loading with batching and shuffling

  • Collators: Utilities for batching variable-length sequences

  • Samplers: Various sampling strategies for training and evaluation

Audio Processing

Coming Soon: Documentation for audio processing utilities including:

  • AudioDataset: Dataset classes for audio files and features

  • AudioCollator: Batching utilities for variable-length audio sequences

  • Feature Extraction: Mel-spectrogram, MFCC, and other audio features

Structured Data

Coming Soon: Documentation for structured data processing including:

  • ParquetDataset: Efficient loading of Parquet files

  • JsonDataset: JSON and JSONL file processing

  • CSVDataset: CSV file loading with type inference

See Also