fairseq/examples/wav2vec/README.md at main · facebookresearch/fairseq
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Facebook AI Research Sequence-to-Sequence Toolkit written in Python. - facebookresearch/fairseq
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).
We combined speech data from multiple domains in Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021).
We finetuned XLSR-53 on multiple languages to transcribe unseen languages in Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021).
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 conformer - rel_pos (LV-60)* | No finetuning | Libri-Light | download |
| Wav2Vec 2.0 Large conformer - rope (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 conformer - rel_pos (LV-60)* | 100 hours | Libri-Light | download |
| Wav2Vec 2.0 Large conformer - rope (LV-60)* | 100 hours | Libri-Light | download |
| Wav2Vec 2.0 Large (LV-60)* | 960 hours | Libri-Light + Librispeech | download |
| Wav2Vec 2.0 Large conformer - rel_pos (LV-60)* | 960 hours | Libri-Light | download |
| Wav2Vec 2.0 Large conformer - rope (LV-60)* | 960 hours | Libri-Light | 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 |
| Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | No finetuning | Libri-Light + CommonVoice + Switchboard + Fisher | download |
| Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | 960 hours Librispeech | Libri-Light + CommonVoice + Switchboard + Fisher | download |
| Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | 300 hours Switchboard | Libri-Light + CommonVoice + Switchboard + Fisher | download |
* updated (Oct. 24, 2020)
** updated (Nov. 13, 2021)
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
We also finetuned several models on languages from CommonVoice (version 6.1) and Babel. Please refer to our paper for details about which languages are used.
| Pretrained Model | Fintune Dataset | # Languages | Phonemizer | Model | Dictionary |
|---|---|---|---|---|---|
| LV-60 | CommonVoice | 26 | Espeak | download | download |
| XLSR-53 | CommonVoice | 26 | Espeak | download | download |
| XLSR-53 | CommonVoice | 21 | Phonetisaurus | download | download |
| XLSR-53 | CommonVoice, BABEL | 21, 19 | Phonetisaurus | download | download |
We release 2 models that are finetuned on data from 2 different phonemizers. Although the phonemes are all IPA symbols, there are still subtle differences between the phonemized transcriptions from the 2 phonemizers. Thus, it’s better to use the corresponding model, if your data is phonemized by either phonemizer above.
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:
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_librispeechNote: 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_librivoxNote: 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
Train a wav2vec 2.0 model with conformer backbone
To replace the transformer layers in the encoder with the conformer layers, set --layer-type conformer --attn-type espnet --pos-enc-type ${POS_ENC_TYPE}. POS_ENC_TYPE refers to positional encoding to be used in the conformer encoder. Set it to abs, rope or rel_pos to use the absolute positional encoding, rotary positional encoding or relative positional encoding in the conformer layer respectively.
To train a base model with conformer:
$ fairseq-hydra-train \
task.data=/path/to/data \
--config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \
--config-name wav2vec2_conformer_base_librispeech \
--attn-type espnet --pos-enc-type ${POS_ENC_TYPE}To train a large model with conformer:
$ fairseq-hydra-train \
task.data=/path/to/data \
--config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \
--config-name wav2vec2_conformer_large_librivox
--attn-type espnet --pos-enc-type ${POS_ENC_TYPE}
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 $splitFine-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_100hThere 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_finetuning \
--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 letterTo 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 4.4.
Pretrained Models can be found on the hub and documentation can be found here.
Usage example:
# !pip install transformers
# !pip install datasets
import soundfile as sf
import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
librispeech_samples_ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# load audio
audio_input, sample_rate = sf.read(librispeech_samples_ds[0]["file"])
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0])
# FINE-TUNE
target_transcription = "A MAN SAID TO THE UNIVERSE I EXIST"
# encode labels
with processor.as_target_processor():
labels = processor(target_transcription, return_tensors="pt").input_ids
# compute loss by passing labels
loss = model(input_values, labels=labels).loss
loss.backward()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
Note: Commandline arguments way of execution has a known-problem currently.
$ 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)
Note: Commandline arguments way of execution has a known-problem currently.
$ 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 modelTraining 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