OpenSeq2Seq – TensorFlow-based toolkit for sequence-to-sequence models

OpenSeq2Seq is a toolkit for distributed and mixed precision training of sequence-to-sequence models:

  • Machine translation (GNMT, Transformer, ConvS2S, …). These models have been trained with BPE vocabulary used for text tokenization.
  • Speech recognition (DeepSpeech2, Wave2Letter, Jasper, …).
  • Speech commands (RN-50, Jasper). Automatic speech recognition (ASR) systems can be built using a number of approaches depending on input data type, intermediate representation, model’s type and output post-processing. OpenSeq2Seq is currently focused on end-to-end CTC-based models (like original DeepSpeech model).
  • Speech synthesis (Tacotron2, Tacotron2 GST, WaveNet, Centaur, …).
  • Language model (LSTM with WikiText-2, LSTM with WikiText-103).
  • Sentiment analysis (SST, IMDB, …).
  • Image classification, a mixed precison replica of TensorFlow ResNet-50.

OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training.

OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation, automatic speech recognition, speech synthesis, and language modeling.

Speech-to-text workflow uses some parts of Mozilla DeepSpeech project.

This is a research project, not an official NVIDIA product.

Features include:

  • Models for:
    1. Neural Machine Translation.
    2. Automatic Speech Recognition.
    3. Speech Synthesis.
    4. Language Modeling.
    5. NLP tasks (sentiment analysis).
  • Data-parallel distributed training:
    1. Multi-GPU.
    2. Multi-node.
  • Mixed precision training for NVIDIA Volta/Turing GPUs.
  • Supports two modes for parallel training: simple multi-tower approach and Horovod-based approach.
  • Supports two new optimizers: Layer-wise Adaptive Rate Control (LARC) and NovoGrad. NovoGrad is a first-order SGD-based algorithm, which computes second moments per layer instead of per weight as in Adam.
  • Mixed precision with existing models.
  • Interactive infer – a mode that makes it easy to demo trained models.

Support: GitHub Code Repository
Developer: NVIDIA
License: Apache License 2.0

OpenSeq2Seq is written in Python. Learn Python with our recommended free books and free tutorials.

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