wespeaker | Research and Production Oriented Speaker Recognition Toolkit

by wenet-e2e Python Version: v1.1.0 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | wespeaker Summary

wespeaker is a Python library. wespeaker has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install wespeaker' or download it from GitHub, PyPI.

Roadmap | Paper | Runtime (x86_gpu) | Python binding | Pretrained Models | Huggingface Demo. WeSpeaker mainly focuses on speaker embedding learning, with application to the speaker verification task. We support online feature extraction or loading pre-extracted features in kaldi-format.

Support

Quality

Security

License

Reuse

Support

wespeaker has a low active ecosystem.

It has 287 star(s) with 58 fork(s). There are 17 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 29 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of wespeaker is v1.1.0

Quality

wespeaker has no bugs reported.

Security

wespeaker has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

wespeaker is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

wespeaker releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

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wespeaker Key Features

No Key Features are available at this moment for wespeaker.

wespeaker Examples and Code Snippets

No Code Snippets are available at this moment for wespeaker.

Community Discussions

No Community Discussions are available at this moment for wespeaker.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install wespeaker

Clone this repo
Create conda env: pytorch version >= 1.10.0 is required !!!
If you just want to use the pretrained model, try the python binding!

Support

Model (SOTA Models) [x] Standard X-vector [x] ResNet [x] ECAPA_TDNN [x] RepVGGPooling Functions [x] TAP(mean) / TSDP(std) / TSTP(mean+std) Comparison of mean/std pooling can be found in shuai_iscslp, anna_arxiv [x] Attentive Statistics Pooling (ASTP) Mainly for ECAPA_TDNN [x] Multi-Query and Multi-Head Attentive Statistics Pooling (MQMHASTP) Details can be found in MQMHASTPCriteria [x] Softmax [x] Sphere (A-Softmax) [x] Add_Margin (AM-Softmax) [x] Arc_Margin (AAM-Softmax) [x] Arc_Margin+Inter-topk+Sub-centerScoring [x] Cosine [x] PLDA [x] Score Normalization (AS-Norm)Metric [x] EER [x] minDCFOnline Augmentation [x] Noise && RIR [x] Speed Perturb [x] SpecAugTraining Strategy [x] Well-designed Learning Rate and Margin Schedulers [x] Large Margin Fine-tuning [x] Automatic Mixed Precision (AMP) TrainingLiterature [x] Awesome Speaker Papers

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