UniSpeech - Large Scale Self-Supervised Learning for Speech

Overview

UniSpeech

The family of UniSpeech:

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

UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

UniSpeech-SAT (ICASSP 2022 Submission): Universal Speech Representation Learning with Speaker Aware Pre-Training

Update

Pre-trained models

We strongly suggest using our UniSpeech-SAT model for speaker related tasks, since it shows very powerful performance on various speaker related benchmarks.

Model Pretraining Dataset Finetuning Dataset Model
UniSpeech Large EN Labeled: 1350 hrs en - download
UniSpeech Large Multilingual Labeled: 1350 hrs en + 353 hrs fr + 168 hrs es + 90 hrs it - download
Unispeech Large+ Labeled: 1350 hrs en, Unlabeled: 353 hrs fr - download
UniSpeech Large+ Labeld: 1350 hrs en, Unlabeled: 168 hrs es - download
UniSpeech Large+ Labeled: 1350 hrs en, Unlabeld: 90 hrs it - download
UniSpeech Large Multilingual Labeled: 1350 hrs en + 353 hrs fr + 168 hrs es + 90 hrs it, Unlabeled: 17 hrs ky - download
UniSpeech Large+ Labeled: 1350 hrs en, Unlabeled: 353 hrs fr 1 hr fr download
UniSpeech Large+ Labeld: 1350 hrs en, Unlabeled: 168 hrs es 1 hr es download
UniSpeech Large+ Labeled: 1350 hrs en, Unlabeld: 90 hrs it 1 hr it download
UniSpeech Large Multilingual Labeled: 1350 hrs en + 353 hrs fr + 168 hrs es + 90 hrs it, Unlabeled: 17 hrs ky 1 hr ky download
UniSpeech-SAT Base 960 hrs LibriSpeech - download
UniSpeech-SAT Base+ 60k hrs Libri-Light + 10k hrs GigaSpeech + 24k hrs VoxPopuli - download
UniSpeech-SAT Large 60k hrs Libri-Light + 10k hrs GigaSpeech + 24k hrs VoxPopuli - download
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

Universal Representation Evaluation on SUPERB

alt text

Downstream Task Performance

We also evaluate our models on typical speaker related benchmarks.

Speaker Verification

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

Our paper for verification

Speech Separation

Evaluation on LibriCSS

Model 0S 0L OV10 OV20 OV30 OV40
Conformer (SOTA) 4.5 4.4 6.2 8.5 11 12.6
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 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
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

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:

@inproceedings{Wang2021UniSpeech,
  author    = {Chengyi Wang and Yu Wu and Yao Qian and Kenichi Kumatani and Shujie Liu and Furu Wei and Michael Zeng and Xuedong Huang},
  editor    = {Marina Meila and Tong Zhang},
  title     = {UniSpeech: Unified Speech Representation Learning with Labeled and
               Unlabeled Data},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning,
               {ICML} 2021, 18-24 July 2021, Virtual Event},
  series    = {Proceedings of Machine Learning Research},
  volume    = {139},
  pages     = {10937--10947},
  publisher = {{PMLR}},
  year      = {2021},
  url       = {http://proceedings.mlr.press/v139/wang21y.html},
  timestamp = {Thu, 21 Oct 2021 16:06:12 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/0002WQK0WZ021.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@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 Michael Zeng and Furu Wei},
  eprint={2110.13900},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  year={2021}
}
@article{Chen2021UniSpeechSAT,
  title   = {UniSpeech-SAT: Universal Speech Representation Learning with  Speaker Aware Pre-Training},
  author  = {Sanyuan Chen and Yu Wu and Chengyi Wang and Zhengyang Chen and Zhuo Chen and Shujie Liu and   Jian Wu and Yao Qian and Furu Wei and Jinyu Li and  Xiangzhan Yu},
  eprint={2110.05752},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  year={2021}
}

Contact Information

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

For other communications related to UniSpeech, please contact Yu Wu ([email protected]).

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time

SentimentArcs - Emotion in Text An end-to-end pipeline based on Jupyter notebooks to detect, extract, process and anlayze emotion over time in text. E

jon_chun 14 Dec 19, 2022
2021海华AI挑战赛·中文阅读理解·技术组·第三名

文字是人类用以记录和表达的最基本工具,也是信息传播的重要媒介。透过文字与符号,我们可以追寻人类文明的起源,可以传播知识与经验,读懂文字是认识与了解的第一步。对于人工智能而言,它的核心问题之一就是认知,而认知的核心则是语义理解。

21 Dec 26, 2022
CCKS-Title-based-large-scale-commodity-entity-retrieval-top1

- 基于标题的大规模商品实体检索top1 一、任务介绍 CCKS 2020:基于标题的大规模商品实体检索,任务为对于给定的一个商品标题,参赛系统需要匹配到该标题在给定商品库中的对应商品实体。 输入:输入文件包括若干行商品标题。 输出:输出文本每一行包括此标题对应的商品实体,即给定知识库中商品 ID,

43 Nov 11, 2022
Club chatbot

Chatbot Club chatbot Instructions to get the Chatterbot working Step 1. First make sure you are using a version of Python 3 or newer. To check your ve

5 Mar 07, 2022
Task-based datasets, preprocessing, and evaluation for sequence models.

SeqIO: Task-based datasets, preprocessing, and evaluation for sequence models. SeqIO is a library for processing sequential data to be fed into downst

Google 290 Dec 26, 2022
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation

Diaformer Diaformer: Automatic Diagnosis via Symptoms Sequence Generation (AAAI 2022) Diaformer is an efficient model for automatic diagnosis via symp

Junying Chen 20 Dec 13, 2022
Code for PED: DETR For (Crowd) Pedestrian Detection

Code for PED: DETR For (Crowd) Pedestrian Detection

36 Sep 13, 2022
HF's ML for Audio study group

Hugging Face Machine Learning for Audio Study Group Welcome to the ML for Audio Study Group. Through a series of presentations, paper reading and disc

Vaibhav Srivastav 110 Jan 01, 2023
This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Technique for Text Classification

The baseline code is for EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks

Akbar Karimi 81 Dec 09, 2022
CCQA A New Web-Scale Question Answering Dataset for Model Pre-Training

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training This is the official repository for the code and models of the paper CCQA: A N

Meta Research 29 Nov 30, 2022
An open collection of annotated voices in Japanese language

声庭 (Koniwa): オープンな日本語音声とアノテーションのコレクション Koniwa (声庭): An open collection of annotated voices in Japanese language 概要 Koniwa(声庭)は利用・修正・再配布が自由でオープンな音声とアノテ

Koniwa project 32 Dec 14, 2022
Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

smaller-LaBSE LaBSE(Language-agnostic BERT Sentence Embedding) is a very good method to get sentence embeddings across languages. But it is hard to fi

Jeong Ukjae 13 Sep 02, 2022
Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch

N-Grammer - Pytorch Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch Install $ pip install n-grammer-pytorch Usage

Phil Wang 66 Dec 29, 2022
NLP project that works with news (NER, context generation, news trend analytics)

СоАвтор СоАвтор – платформа и открытый набор инструментов для редакций и журналистов-фрилансеров, который призван сделать процесс создания контента ма

38 Jan 04, 2023
TweebankNLP - Pre-trained Tweet NLP Pipeline (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Models + Tweebank-NER

TweebankNLP This repo contains the new Tweebank-NER dataset and off-the-shelf Twitter-Stanza pipeline for state-of-the-art Tweet NLP, as described in

Laboratory for Social Machines 84 Dec 20, 2022
glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end.

Glow-Speak glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end. Installation git clone https://g

Rhasspy 8 Dec 25, 2022
Galois is an auto code completer for code editors (or any text editor) based on OpenAI GPT-2.

Galois is an auto code completer for code editors (or any text editor) based on OpenAI GPT-2. It is trained (finetuned) on a curated list of approximately 45K Python (~470MB) files gathered from the

Galois Autocompleter 91 Sep 23, 2022
Simple python code to fix your combo list by removing any text after a separator or removing duplicate combos

Combo List Fixer A simple python code to fix your combo list by removing any text after a separator or removing duplicate combos Removing any text aft

Hamidreza Dehghan 3 Dec 05, 2022
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)

DataCLUE 以数据为中心的AI测评(DataCLUE) DataCLUE: A Chinese Data-centric Language Evaluation Benchmark 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE)的背景 任务描述 任务描述 实验结果

CLUE benchmark 135 Dec 22, 2022
A PyTorch implementation of the Transformer model in "Attention is All You Need".

Attention is all you need: A Pytorch Implementation This is a PyTorch implementation of the Transformer model in "Attention is All You Need" (Ashish V

Yu-Hsiang Huang 7.1k Jan 05, 2023