Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Latest commit

* fix imports referencing moved file

* Make representation computation branchless in TransformerEncoderBase (#4818)

We want to make the computation branchless here because fairseq code may be
exported and traced for deployment purposes, and tracing mechanisms can
break the correctness for a captured program if it's dependent on input data.
In this diff we try to rewrite the code to remove one branch so that tracer
can proceed here and preserve the correct semantics of the model.

Test Plan:





* Fix Torchscript typing in (#4847)

* Add Generative Spoken Dialogue Language Modeling (#4879)

* Update deprecated torch.qr in example (#4685)

torch.qr is deprecated for a long time and is being removed by pytorch/pytorch#70989.

This PR makes the example compatible with new and old PyTorch versions.

* Emotion Conversion Paper Open Source (#4895)

* data2vec v2.0 (#4903)

data2v2c 2.0
Co-authored-by: Arun Babu <>
Co-authored-by: Wei-Ning Hsu <>

* remove missing config entries when loading task from checkpoint (#4905)

* make apex optional (#4906)

* Add file to generate manifests for stop dataset. (#4891)

* Update STOP dataset README to include proper link. (#4892)

* Update (#4893)

* using foreach to reduce kernel (#4904)

* using foreach to reduce kernel

* set reproducibility to looser threshold

* revert optimzer

* update

* update

* update

* update

* update

* update

* update

Co-authored-by: juntengjia <>

* Update to add data2vec blog post (#4913)

* Update

* Update config to fix circleci failure (#4949)

* Generative Spoken Dialogue Language Modeling Paper Open Source (#4957)

* wav2vec2_laser (#4968)

* ASR BLEU tool copied from ust branch into main (#4914)

* Add transcript option for asr-bleu (#4981)


Co-authored-by: zhxchen17 <>
Co-authored-by: zhxchen17 <>
Co-authored-by: Nguyen Tu Anh <>
Co-authored-by: Sergii Dymchenko <>
Co-authored-by: Felix Kreuk <>
Co-authored-by: Alexei Baevski <>
Co-authored-by: padentomasello <>
Co-authored-by: Junteng Jia <>
Co-authored-by: juntengjia <>
Co-authored-by: arbabu123 <>
Co-authored-by: dianaml0 <>
Co-authored-by: Pierre Andrews <>
Co-authored-by: Ilia Kulikov <>
Co-authored-by: Xutai Ma <>

Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Support Ukraine MIT License Latest Release Build Status Documentation Status CicleCI Status

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

We provide reference implementations of various sequence modeling papers:

List of implemented papers

What's New:

Previous updates


We also provide pre-trained models for translation and language modeling with a convenient torch.hub interface:

en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
# 'Hallo Welt'

See the PyTorch Hub tutorials for translation and RoBERTa for more examples.

Requirements and Installation

  • PyTorch version >= 1.10.0
  • Python version >= 3.8
  • For training new models, you'll also need an NVIDIA GPU and NCCL
  • To install fairseq and develop locally:
git clone
cd fairseq
pip install --editable ./

# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./

# to install the latest stable release (0.10.x)
# pip install fairseq
  • For faster training install NVIDIA's apex library:
git clone
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
  --global-option="--fast_multihead_attn" ./
  • For large datasets install PyArrow: pip install pyarrow
  • If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run .

Getting Started

The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.

Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands.

We also have more detailed READMEs to reproduce results from specific papers:

Join the fairseq community


fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.


Please cite as:

  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},