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.

• Added support for PyTorch 2.6+ with safe globals handling
• Updated dependency requirements for modern Python environments
• Improved Windows compatibility
• Added explicit fairscale dependency

• PyTorch version >= 2.6.0
• Python version >= 3.8
• For training new models, you'll also need an NVIDIA GPU and NCCL
• For faster training install NVIDIA's apex library with the --cuda_ext and --deprecated_fused_adam options

We provide reference implementations of various sequence modeling papers:

• May 2023 Released models for Scaling Speech Technology to 1,000+ Languages (Pratap, et al., 2023)
• June 2022 Released code for wav2vec-U 2.0 from Towards End-to-end Unsupervised Speech Recognition (Liu, et al., 2022)
• May 2022 Integration with xFormers
• December 2021 Released Direct speech-to-speech translation code
• October 2021 Released VideoCLIP and VLM models
• October 2021 Released multilingual finetuned XLSR-53 model
• September 2021 master branch renamed to main.
• July 2021 Released DrNMT code
• July 2021 Released Robust wav2vec 2.0 model
• June 2021 Released XLMR-XL and XLMR-XXL models
• May 2021 Released Unsupervised Speech Recognition code
• March 2021 Added full parameter and optimizer state sharding + CPU offloading
• February 2021 Added LASER training code
• December 2020: Added Adaptive Attention Span code
• December 2020: GottBERT model and code released
• November 2020: Adopted the Hydra configuration framework
  • see documentation explaining how to use it for new and existing projects
• November 2020: fairseq 0.10.0 released
• October 2020: Added R3F/R4F (Better Fine-Tuning) code
• October 2020: Deep Transformer with Latent Depth code released
• October 2020: Added CRISS models and code

• multi-GPU training on one machine or across multiple machines (data and model parallel)
• fast generation on both CPU and GPU with multiple search algorithms implemented:
  • beam search
  • Diverse Beam Search (Vijayakumar et al., 2016)
  • sampling (unconstrained, top-k and top-p/nucleus)
  • lexically constrained decoding (Post & Vilar, 2018)
• gradient accumulation enables training with large mini-batches even on a single GPU
• mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
• extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers
• flexible configuration based on Hydra allowing a combination of code, command-line and file based configuration
• full parameter and optimizer state sharding
• offloading parameters to CPU

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.

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

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

• Translation: convolutional and transformer models are available
• Language Modeling: convolutional and transformer models are available

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

• XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (Babu et al., 2021)
• Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)
• wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)
• Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)
• Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
• Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
• Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
• Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)
• Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
• Levenshtein Transformer (Gu et al., 2019)
• Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
• RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
• wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
• Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
• Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
• Understanding Back-Translation at Scale (Edunov et al., 2018)
• Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
• Hierarchical Neural Story Generation (Fan et al., 2018)
• Scaling Neural Machine Translation (Ott et al., 2018)
• Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
• Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)

• Twitter: https://twitter.com/fairseq
• Facebook page: https://www.facebook.com/groups/fairseq.users
• Google group: https://groups.google.com/forum/#!forum/fairseq-users

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

Please cite as:

@inproceedings{ott2019fairseq,
  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},
}