wav2vec 2.0

wav2vec 2.0 learns speech representations on unlabeled data as described in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020).

We learned speech representations in multiple languages as well in Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020).

We also combined wav2vec 2.0 with self-training in Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020).

Pre-trained models

Model	Finetuning split	Dataset	Model
Wav2Vec 2.0 Base	No finetuning	Librispeech	download
Wav2Vec 2.0 Base	10 minutes	Librispeech	download
Wav2Vec 2.0 Base	100 hours	Librispeech	download
Wav2Vec 2.0 Base	960 hours	Librispeech	download
Wav2Vec 2.0 Large	No finetuning	Librispeech	download
Wav2Vec 2.0 Large	10 minutes	Librispeech	download
Wav2Vec 2.0 Large	100 hours	Librispeech	download
Wav2Vec 2.0 Large	960 hours	Librispeech	download
Wav2Vec 2.0 Large (LV-60)*	No finetuning	Libri-Light	download
Wav2Vec 2.0 Large (LV-60)*	10 minutes	Libri-Light + Librispeech	download
Wav2Vec 2.0 Large (LV-60)*	100 hours	Libri-Light + Librispeech	download
Wav2Vec 2.0 Large (LV-60)*	960 hours	Libri-Light + Librispeech	download
Wav2Vec 2.0 Large (LV-60) + Self Training *	10 minutes	Libri-Light + Librispeech	download
Wav2Vec 2.0 Large (LV-60) + Self Training *	100 hours	Libri-Light + Librispeech	download
Wav2Vec 2.0 Large (LV-60) + Self Training *	960 hours	Libri-Light + Librispeech	download

* updated (Oct. 24, 2020)

We also release multilingual pre-trained wav2vec 2.0 (XLSR) models:

Model	Architecture	Hours	Languages	Datasets	Model
XLSR-53	Large	56k	53	MLS, CommonVoice, BABEL	download

The XLSR model uses the following datasets for multilingual pretraining:

MLS: Multilingual LibriSpeech (8 languages, 50.7k hours): Dutch, English, French, German, Italian, Polish, Portuguese, Spanish
CommonVoice (36 languages, 3.6k hours): Arabic, Basque, Breton, Chinese (CN), Chinese (HK), Chinese (TW), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakh-Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Mongolian, Persian, Portuguese, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Welsh (see also finetuning splits from this paper).
Babel (17 languages, 1.7k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu

Training a new 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 wav2vec 2.0 base model:

This configuration was used for the base model trained on the Librispeech dataset in the wav2vec 2.0 paper

Note that the input is expected to be single channel, sampled at 16 kHz

$ fairseq-hydra-train \
    task.data=/path/to/data \
    --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \
    --config-name wav2vec2_base_librispeech

Note: you can simulate 64 GPUs by using k GPUs and adding command line parameters (before --config-dir) distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 64/k

Train a wav2vec 2.0 large model:

This configuration was used for the large model trained on the Libri-light dataset in the wav2vec 2.0 paper

$ fairseq-hydra-train \
    task.data=/path/to/data \
    --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \
    --config-name wav2vec2_large_librivox

Note: you can simulate 128 GPUs by using k GPUs and adding command line parameters (before --config-dir) distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 128/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

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.

Note: you can simulate 24 GPUs by using k GPUs and adding command line parameters (before --config-dir) distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 24/k

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:

$subset=dev_other
python examples/speech_recognition/infer.py /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw --task audio_pretraining \
--nbest 1 --path /path/to/model --gen-subset $subset --results-path /path/to/save/results/for/sclite --w2l-decoder kenlm \
--lm-model /path/to/kenlm.bin --lm-weight 2 --word-score -1 --sil-weight 0 --criterion ctc --labels ltr --max-tokens 4000000 \
--post-process letter

To get raw numbers, use --w2l-decoder viterbi and omit the lexicon. To use the transformer language model, use --w2l-decoder fairseqlm.

Use wav2vec 2.0 with 🤗Transformers:

Wav2Vec2 is also available in the 🤗Transformers library since version 4.3.

Pretrained Models can be found on the hub and documentation can be found here.

Usage example:

# !pip install transformers
import soundfile as sf
import torch
from transformers import Wav2Vec2ForMaskedLM, Wav2Vec2Tokenizer

# load pretrained model
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-base-960h")

# load audio
audio_input, _ = sf.read("path/to/audio/file")

# transcribe
input_values = tokenizer(audio_input, return_tensors="pt").input_values
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = tokenizer.batch_decode(predicted_ids)[0]

wav2vec

Example to train a wav2vec model as described in wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019).

Pre-trained models

Description	Dataset	Model
Wav2Vec large	Librispeech	download

Example usage:

import torch
import fairseq

cp_path = '/path/to/wav2vec.pt'
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp_path])
model = model[0]
model.eval()

wav_input_16khz = torch.randn(1,10000)
z = model.feature_extractor(wav_input_16khz)
c = model.feature_aggregator(z)

Training a new model with the CLI tools

Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate files 10 to 30 seconds in length)

Prepare training data manifest:

$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav

Train a wav2vec model:

$ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \
--arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \
--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \
--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \
--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \
--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test

Run wav2vec2 pre-training on Google Cloud TPUs:

Wav2Vec2 is now supported on TPUs! It's currently pre-training only.

Using hydra on a v3-8:

$ OMP_NUM_THREADS=1 fairseq-hydra-train \
  task.data=/manifest/path \
  --config-dir /PATH/TO/FAIRSEQ/examples/wav2vec/config/pretraining \
  --config-name wav2vec2_large_librivox_tpu.yaml

Using command line arguments on a v3-8:

$ OMP_NUM_THREADS=1 python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \
--arch wav2vec2 --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \
--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \
--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \
--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \
--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test \
--tpu --distributed-world-size 8 --num-batch-buckets 3 --enable-padding \
--encoder-layerdrop 0 --mask-channel-prob 0.1

Using hydra on a pod slice (v3-N with N > 8):

$ OMP_NUM_THREADS=1 fairseq-hydra-train \
  task.data=/manifest/path \
  --config-dir /PATH/TO/FAIRSEQ/examples/wav2vec/config/pretraining \
  --config-name wav2vec2_large_librivox_tpu-pod.yaml  # edit distributed-world-size accordingly

Using command line arguments on a pod slice (v3-N with N > 8):

$ python -m torch_xla.distributed.xla_dist \
  --tpu ${TPUNAME} --conda-env=torch-xla-${TORCH_XLA_VERSION} --env OMP_NUM_THREADS=1 \
  -- \
python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \
--arch wav2vec2 --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \
--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \
--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \
--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \
--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test \
--tpu --distributed-world-size ${WORLD_SIZE} --num-batch-buckets 3 --enable-padding \
--encoder-layerdrop 0 --mask-channel-prob 0.1

Extract embeddings from the downstream task data:

$ PYTHONPATH=/path/to/fairseq python examples/wav2vec/wav2vec_featurize.py --input /path/to/task/waves --output /path/to/output \
--model /model/path/checkpoint_best.pt --split train valid test

vq-wav2vec

Example to train a vq-wav2vec model as described in vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations (Baevski et al., 2019).

These models are also used in Effectiveness of self-supervised pre-training for speech recognition (Baevski et al., 2019).

Pre-trained models

Description	Dataset	Model
vq-wav2vec Gumbel	Librispeech	download
vq-wav2vec K-means	Librispeech	download
Roberta on K-means codes	Librispeech	download

Example usage:

import torch
import fairseq

cp = torch.load('/path/to/vq-wav2vec.pt')
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp])
model = model[0]
model.eval()

wav_input_16khz = torch.randn(1,10000)
z = model.feature_extractor(wav_input_16khz)
_, idxs = model.vector_quantizer.forward_idx(z)
print(idxs.shape) # output: torch.Size([1, 60, 2]), 60 timesteps with 2 indexes corresponding to 2 groups in the model

Training a new 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:

$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav

Train a gumbel vq-wav2vec model:

$ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 \
--save-interval 1 --no-epoch-checkpoints --arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 \
--optimizer adam --lr 1e-05 --lr-scheduler cosine \
--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)] \
--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \
--activation gelu --offset auto --skip-connections-agg --residual-scale 0.5 \
--log-keys ["prob_perplexity","code_perplexity","temp"] --vq-type gumbel --vq-groups 2 --vq-depth 2 \
--combine-groups --vq-vars 320 --vq-temp (2,0.5,0.999995) --prediction-steps 12 --warmup-updates 1000 \
--warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 --max-sample-size 150000 \
--max-tokens 300000 --cross-sample-negatives 0 --update-freq 1 --seed 2 --skip-invalid-size-inputs-valid-test

for k-means training, set vq-type with "kmeans" and add --loss-weights [1] argument. Pre-trained models were trained on 16 GPUs.

Tokenize audio data (e.g. for BERT training):

$ PYTHONPATH=/path/to/fairseq python examples/wav2vec/vq-wav2vec_featurize.py --data-dir /manifest/path --output-dir /path/to/output \
--checkpoint /model/path/checkpoint_best.pt --split train valid test --extension tsv

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README.md

README.md

wav2vec 2.0

Pre-trained models

Training a new model with the CLI tools

Prepare training data manifest:

Train a wav2vec 2.0 base model:

Train a wav2vec 2.0 large model:

Fine-tune a pre-trained model with CTC:

Evaluating a CTC model:

Use wav2vec 2.0 with 🤗Transformers:

wav2vec

Pre-trained models

Example usage:

Training a new model with the CLI tools

Prepare training data manifest:

Train a wav2vec model:

Run wav2vec2 pre-training on Google Cloud TPUs:

Using hydra on a v3-8:

Using command line arguments on a v3-8:

Using hydra on a pod slice (v3-N with N > 8):

Using command line arguments on a pod slice (v3-N with N > 8):

Extract embeddings from the downstream task data:

vq-wav2vec

Pre-trained models

Example usage:

Training a new model with the CLI tools

Prepare training data manifest:

Train a gumbel vq-wav2vec model:

Tokenize audio data (e.g. for BERT training):

Files

README.md

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wav2vec 2.0

Pre-trained models

Training a new model with the CLI tools

Prepare training data manifest:

Train a wav2vec 2.0 base model:

Train a wav2vec 2.0 large model:

Fine-tune a pre-trained model with CTC:

Evaluating a CTC model:

Use wav2vec 2.0 with 🤗Transformers:

wav2vec

Pre-trained models

Example usage:

Training a new model with the CLI tools

Prepare training data manifest:

Train a wav2vec model:

Run wav2vec2 pre-training on Google Cloud TPUs:

Using hydra on a v3-8:

Using command line arguments on a v3-8:

Using hydra on a pod slice (v3-N with N > 8):

Using command line arguments on a pod slice (v3-N with N > 8):

Extract embeddings from the downstream task data:

vq-wav2vec

Pre-trained models

Example usage:

Training a new model with the CLI tools

Prepare training data manifest:

Train a gumbel vq-wav2vec model:

Tokenize audio data (e.g. for BERT training):