https://github.com/fgnt/tssep_data
Science Score: 44.0%
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○CITATION.cff file
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✓codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 8 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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Organization fgnt has institutional domain (nt.uni-paderborn.de) -
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○Scientific vocabulary similarity
Low similarity (10.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: fgnt
- License: mit
- Language: Python
- Default Branch: master
- Size: 340 KB
Statistics
- Stars: 2
- Watchers: 6
- Forks: 2
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings
This repository contains the data preparation and evaluation code for the TS-VAD and TS-SEP experiments in our 2024 IEEE/ACM TASLP article, TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings by Christoph Boeddeker, Aswin Shanmugam Subramanian, Gordon Wichern, Reinhold Haeb-Umbach, Jonathan Le Roux (IEEE Xplore, arXiv).
The core and training code is available at https://github.com/merlresearch/tssep .
Installation
Using an existing environment, you can install the data preparation code with:
git clone https://github.com/merlresearch/tssep.git
cd tssep
pip install -e .
cd ..
git clone https://github.com/fgnt/tssep_data.git
cd tssep_data
pip install -e .
If you want so setup a fresh environment, see tools/README.md.
Once you have installed a fresh environment, you can activate it with . tools/path.sh (It will also setup some environment variables).
Note: Kaldi and MPI are required for the recipes.
For ASR, you can use
openai-whisper, espnet or nemo_toolkit as alternatives.
ToDo: Limit this to whisper, it has less dependencies.
LibriCSS data preparation, training and evaluation
egs/libri_css/README.md#steps-to-run-the-recipe contains the instructions for the LibriCSS data preparation, training and evaluation.
LibriCSS evaluation with pretrained model
egs/libri_css/README.md#steps-to-evaluate-a-pretrained-model contains the instructions for the LibriCSS evaluation with a pretrained model.
Cite
If you are using this code please cite our paper (
):
@article{Boeddeker2024feb,
author = {Boeddeker, Christoph and Subramanian, Aswin Shanmugam and Wichern, Gordon and Haeb-Umbach, Reinhold and Le Roux, Jonathan},
title = {{TS-SEP}: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year = 2024,
volume = 32,
pages = {1185--1197},
month = feb,
}
Owner
- Name: Department of Communications Engineering University of Paderborn
- Login: fgnt
- Kind: organization
- Location: Paderborn, Germany
- Website: http://nt.uni-paderborn.de
- Repositories: 37
- Profile: https://github.com/fgnt
GitHub Events
Total
- Watch event: 2
- Push event: 2
- Pull request event: 1
- Fork event: 1
Last Year
- Watch event: 2
- Push event: 2
- Pull request event: 1
- Fork event: 1
Dependencies
- Cython *
- IPython *
- cached_property *
- dataclasses *
- diskcache *
- dlp_mpi *
- editdistance *
- einops *
- espnet *
- espnet_model_zoo *
- fire *
- humanfriendly *
- kaldi_io *
- lazy_dataset *
- meeteval *
- mms_msg *
- nara_wpe *
- natsort *
- nemo_toolkit *
- numpy *
- openai-whisper *
- paderbox *
- padertorch *
- pb_bss *
- psutil *
- pyroomacoustics *
- pytimeparse *
- pyyaml >=5.1
- questionary *
- resampy *
- rirgen *
- sacred *
- scipy *
- simplejson *
- sms_wsj *
- soundfile *
- tensorboardX *
- torch *
- torchaudio *
- torchvision *
- tqdm *
- webrtcvad *
- openai-whisper *