2 Repos
Serving ASR models in both real-time streaming and batch processing modes.
Distinct from Model Serving: Distinct from general Model Serving: focuses on both real-time streaming and batch processing modes for ASR.
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WeNet is an end-to-end automatic speech recognition (ASR) toolkit designed for both Chinese and English, built around transformer-based models. It supports streaming and non-streaming inference out of the box, and is structured to be production-ready, with model export and deployment paths for servers and mobile devices. The toolkit distinguishes itself through a chunk-based streaming transformer architecture that processes audio in fixed-size segments for low latency while preserving context across chunks. It jointly trains models with both CTC and attention loss to combine alignment accurac
Serves trained ASR models in both real-time streaming and batch processing modes for production use.
Pandera is a data pipeline validation framework and statistical type validation tool. It functions as a library for defining and enforcing schemas on datasets to ensure data quality and consistency, specifically providing validation capabilities for Pandas dataframes. The project includes a schema inference tool that automates setup by analyzing existing dataset samples to generate validation schemas. It also serves as a synthetic data generator, creating artificial datasets based on predefined schemas to verify data-producing functions. The framework covers data engineering quality assuranc
Supports the delivery of real-time and batch predictions within data workflows.