UniSpeech/WavLM

WavLM

WavLM : WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing

Official PyTorch implementation and pretrained models of WavLM

• Oct 2021: release preprint in arXiv

Pre-Trained Models

Model	Pre-training Dataset	Fine-tuning Dataset	Model
WavLM Base	960 hrs LibriSpeech	-	Azure Storage Google Drive
WavLM Base+	60k hrs Libri-Light + 10k hrs GigaSpeech + 24k hrs VoxPopuli	-	Azure Storage Google Drive
WavLM Large	60k hrs Libri-Light + 10k hrs GigaSpeech + 24k hrs VoxPopuli	-	Azure Storage Google Drive

Load Pre-Trained Models for Inference

import torch
from WavLM import WavLM, WavLMConfig

# load the pre-trained checkpoints
checkpoint = torch.load('/path/to/wavlm.pt')
cfg = WavLMConfig(checkpoint['cfg'])
model = WavLM(cfg)
model.load_state_dict(checkpoint['model'])
model.eval()

# extract the the representation of last layer
wav_input_16khz = torch.randn(1,10000)
rep = model.extract_features(wav_input_16khz)[0]

# extract the the representation of each layer
wav_input_16khz = torch.randn(1,10000)
rep, layer_results = model.extract_features(wav_input_16khz, output_layer=model.cfg.encoder_layers, ret_layer_results=True)[0]
layer_reps = [x.transpose(0, 1) for x, _ in layer_results]

Universal Representation Evaluation on SUPERB

Downstream Task Performance

We also evaluate our models on typical speech processing benchmarks.

Speaker Verification

Evaluate on the VoxCeleb

Model	Fix pre-train	Vox1-O	Vox1-E	Vox1-H
ECAPA-TDNN	-	0.87	1.12	2.12
HuBERT large	Yes	0.888	0.912	1.853
Wav2Vec2.0 (XLSR)	Yes	0.915	0.945	1.895
UniSpeech-SAT large	Yes	0.771	0.781	1.669
WavLM large	Yes	0.638	0.687	1.457
HuBERT large	No	0.585	0.654	1.342
Wav2Vec2.0 (XLSR)	No	0.564	0.605	1.23
UniSpeech-SAT large	No	0.564	0.561	1.23
WavLM large	No	0.431	0.538	1.154

Speech Separation

Evaluation on the LibriCSS

Model	0S	0L	OV10	OV20	OV30	OV40
Conformer (SOTA)	4.5	4.4	6.2	8.5	11	12.6
HuBERT base	4.7	4.6	6.1	7.9	10.6	12.3
UniSpeech-SAT base	4.4	4.4	5.4	7.2	9.2	10.5
UniSpeech-SAT large	4.3	4.2	5.0	6.3	8.2	8.8
WavLM base+	4.5	4.4	5.6	7.5	9.4	10.9
WavLM large	4.2	4.1	4.8	5.8	7.4	8.5

Speaker Diarization

Evaluation on the CALLHOME

Model	spk_2	spk_3	spk_4	spk_5	spk_6	spk_all
EEND-vector clustering	7.96	11.93	16.38	21.21	23.1	12.49
EEND-EDA clustering (SOTA)	7.11	11.88	14.37	25.95	21.95	11.84
HuBERT base	7.93	12.07	15.21	19.59	23.32	12.63
HuBERT large	7.39	11.97	15.76	19.82	22.10	12.40
UniSpeech-SAT large	5.93	10.66	12.9	16.48	23.25	10.92
WavLM Base	6.99	11.12	15.20	16.48	21.61	11.75
WavLm large	6.46	10.69	11.84	12.89	20.70	10.35

Speech Recogntion

Evaluate on the LibriSpeech

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the FAIRSEQ project.

Microsoft Open Source Code of Conduct

Reference

If you find our work is useful in your research, please cite the following paper:

@article{Chen2021WavLM,
  title   = {WavLM: Large-Scale Self-Supervised  Pre-training   for Full Stack Speech Processing},
  author  = {Sanyuan Chen and Chengyi Wang and Zhengyang Chen and Yu Wu and Shujie Liu and Zhuo Chen and Jinyu Li and Naoyuki Kanda and Takuya Yoshioka and Xiong Xiao and Jian Wu and Long Zhou and Shuo Ren and Yanmin Qian and Yao Qian and Jian Wu and Micheal Zeng and Furu Wei},
  eprint={2110.13900},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  year={2021}
}

Contact Information

For help or issues using WavLM models, please submit a GitHub issue.

For other communications related to WavLM, please contact Yu Wu (yuwu1@microsoft.com).