data2vec 2.0
data2vec 2.0 improves the training efficiency of the original data2vec algorithm. We make the following improvements for efficiency considerations - we forward only the unmasked timesteps through the encoder, we use convolutional decoder and we use multimasking to amortize the compute overhead of the teacher model. You can find details in the paper Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language and our blog post.
Pretrained and finetuned models
Vision
| Model | Finetuning split | Link |
|---|---|---|
| data2vec ViT-B | No fine-tuning | download |
| data2vec ViT-B | Imagenet-1K | download |
| data2vec ViT-L | No fine-tuning | download |
| data2vec ViT-L | Imagenet-1K | download |
| data2vec ViT-H | No fine-tuning | download |
| data2vec ViT-H | Imagenet-1K | download |
Vision models only are license under CC-BY-NC.
Speech
| Model | Finetuning split | Dataset | Link |
|---|---|---|---|
| data2vec Base | No fine-tuning | Librispeech | download |
| data2vec Base | 960 hours | Librispeech | download |
| data2vec Large | No fine-tuning | Libri-light | download |
| data2vec Large | 960 hours | Libri-light | download |
NLP
| Model | Fine-tuning data | Dataset | Link | Dict | BPE |
|---|---|---|---|---|---|
| data2vec Base | No fine-tuning | Books + Wiki | download | dict | encoder / vocab |
Commands to train different models using data2vec 2.0
Vision
Commands to pretrain different model configurations
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \
--config-name base_images_only_task task.data=/path/to/dir
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \
--config-name large_images_only_task task.data=/path/to/dir
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \
--config-name huge_images14_only_task task.data=/path/to/dir
Commands to finetune different model configurations
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/vision/finetuning \
--config-name mae_imagenet_clean task.data=/path/to/dir model.model_path=/path/to/pretrained/model
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/vision/finetuning \
--config-name mae_imagenet_large_clean task.data=/path/to/dir model.model_path=/path/to/pretrained/model
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/vision/finetuning \
--config-name mae_imagenet_huge_clean task.data=/path/to/dir model.model_path=/path/to/pretrained/model
Speech
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \
--config-name base_audio_only_task task.data=/path/to/manifests
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \
--config-name large_audio_only_task task.data=/path/to/manifests
Finetuning:
$ python fairseq_cli/hydra_train.py -m --config-dir examples/wav2vec/config/finetuning --config-name vox_10h \
task.data=/path/to/manifests model.w2v_path=/path/to/pretrained/model common.user_dir=examples/data2vec
Replace vox_10h with the right config depending on your model and fine-tuning split. See examples/wav2vec/config/finetuning for all available configs.
NLP
Commands to pretrain
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2 \
--config-name base_text_only_task task.data=/path/to/file
Commands to fine-tune all GLUE tasks
$ task=cola # choose from [cola|qnli|mrpc|rte|sst_2|mnli|qqp|sts_b]
$ lr=1e-5 # sweep [1e-5|2e-5|4e-5|6e-5] for each task
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/v2/text_finetuning \
--config-name $task task.data=/path/to/file model.model_path=/path/to/pretrained/model "optimization.lr=[${lr}]"
data2vec
data2vec is a framework for self-supervised representation learning for images, speech, and text as described in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language (Baevski et al., 2022). The algorithm uses the same learning mechanism for different modalities.
Pre-trained models
Vision
Code and pre-trained models for data2vec visions can be found here.
Speech
| Model | Finetuning split | Dataset | Link |
|---|---|---|---|
| data2vec Base | No fine-tuning | Librispeech | download |
| data2vec Base | 10 minutes | Librispeech | download |
| data2vec Base | 100 hours | Librispeech | download |
| data2vec Base | 960 hours | Librispeech | download |
| data2vec Large | No fine-tuning | Libri-light | download |
| data2vec Large | 10 minutes | Libri-light | download |
| data2vec Large | 100 hours | Libri-light | download |
| data2vec Large | 960 hours | Libri-light | download |
NLP
| Model | Fine-tuning data | Dataset | Link |
|---|---|---|---|
| data2vec Base | No fine-tuning | Books + Wiki | download |
Training a new speech model with the CLI tools
Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length)
Prepare training data manifest:
First, install the soundfile library:
pip install soundfile
Next, run:
$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext $ext --valid-percent $valid
$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read.
$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation. To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a separately pre-processed manifest file.
Train a data2vec Base model:
This configuration was used for the base model trained on the Librispeech dataset in the data2vec paper
Note that the input is expected to be single channel, sampled at 16 kHz
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/audio/pretraining \
--config-name base_librispeech task.data=/path/to/manifests common.user_dir=examples/data2vec
Note: you can simulate 16 GPUs by using k GPUs and adding command line parameters
distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 16/k
Fine-tune a pre-trained model with CTC:
Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format. A letter vocabulary can be downloaded here. An example script that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows:
split=train
$ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $split
Fine-tuning on 100h of Librispeech with letter targets:
$ fairseq-hydra-train \
distributed_training.distributed_port=$PORT \
task.data=/path/to/data \
model.w2v_path=/path/to/model.pt \
--config-dir /path/to/fairseq-py/examples/wav2vec/config/finetuning \
--config-name base_100h common.user_dir=examples/data2vec
There are other config files in the config/finetuning directory that can be used to fine-tune on other splits.
You can specify the right config via the --config-name parameter.
Decoding with a language model during training requires flashlight python bindings (previously called wav2letter.
If you want to use a language model, add +criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]' to the command line.
Evaluating a CTC model:
Evaluating a CTC model with a language model requires flashlight python bindings (previously called wav2letter to be installed.
Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the wav2letter model repository. Be sure to upper-case the language model vocab after downloading it.
Letter dictionary for pre-trained models can be found here.
Next, run the evaluation command:
python examples/speech_recognition/new/infer.py --config-dir examples/speech_recognition/new/conf \
--config-name infer task=audio_finetuning task.data=/path/to/manifests common.user_dir=examples/data2vec \
task.labels=ltr decoding.type=kenlm \
decoding.lmweight=${lmweight} decoding.wordscore=${wordscore} decoding.silweight=${silscore} \
decoding.lexicon=/path/to/lexicon \
decoding.lmpath=/path/to/lm decoding.unique_wer_file=True \
dataset.gen_subset=dev_clean,dev_other,test_clean,test_other \
common_eval.path=/path/to/checkpoint.pt decoding.beam=1500 distributed_training.distributed_world_size=${num_gpus}
To get raw numbers, use decoding.type=viterbi and omit the lexicon. To use the transformer language model, use decoding.type=fairseqlm.
Training a new NLP model with the CLI tools
Please follow the RoBERTa instructions to preprocess your data. To train a data2vec model on run:
$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/text/pretraining \
--config-name base task.data=/path/to/data common.user_dir=examples/data2vec
As for speech models, you can simulate 16 gpus by using the update_freq parameter.
Finetuning data2vec-text on GLUE
Please use a command similar to this:
$ python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \
--config-name $task task.data=$data_path checkpoint.restore_file="${/path/to/pretrained/model.pt}"