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facebookresearch/fairseq-py

0
View on GitHub↗
0 stars·0 forks·15 views

Fairseq Py

Features

  • Generative Models - Python implementation of sequence-to-sequence learning architectures.
  • Natural Language Processing - Toolkit for sequence-to-sequence modeling and translation.
  • Speech and Audio - Sequence-to-sequence toolkit for speech and text.

Star history

Star history chart for facebookresearch/fairseq-pyStar history chart for facebookresearch/fairseq-py

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Fairseq Py

These projects share indexed features with Fairseq Py. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • espnet/espnetespnet avatar

    espnet/espnet

    9,861View on GitHub↗

    ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It

    Python
    View on GitHub↗9,861
  • facebookresearch/fairseqfacebookresearch avatar

    facebookresearch/fairseq

    32,228View on GitHub↗

    Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

    Python
    View on GitHub↗32,228
  • awni/speechA

    awni/speech

    0View on GitHub↗
    View on GitHub↗0
  • facebookresearch/infersentfacebookresearch avatar

    facebookresearch/InferSent

    2,279View on GitHub↗

    InferSent sentence embeddings

    Jupyter Notebook
    View on GitHub↗2,279
Compare all 30 related projects→

Frequently asked questions

What are the main features of facebookresearch/fairseq-py?

The main features of facebookresearch/fairseq-py are: Generative Models, Natural Language Processing, Speech and Audio.

Which projects share features with facebookresearch/fairseq-py?

Projects with overlapping indexed features include: facebookresearch/fairseq — Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic… facebookresearch/loop. awni/speech. espnet/espnet — ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech… facebookresearch/infersent — InferSent sentence embeddings. freewym/espresso.