OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks.
Features
- TensorFlow toolkit for sequence-to-sequence models across ASR, TTS, NMT, and NLP
- Built-in support for data-parallel multi-GPU and multi-node training with Horovod
- Mixed-precision training (float16) optimized for NVIDIA GPUs to boost speed and reduce memory usage
- Config-driven model definitions and training scripts for rapid experimentation
- Collection of ready-made model implementations, including wav2letter-style ASR and Transformer-based NMT
- Extensive documentation and examples for building custom encoder-decoder pipelines
Project Samples
