├── .gitignore ├── LICENSE ├── README.md ├── audio_data ├── README.md ├── X2zqiX6yL3I.mp3 ├── donald_trump │ ├── fake │ │ ├── 0ghm5Cqpfwk.mp3 │ │ ├── 1bB5db0Srrw.mp3 │ │ ├── 6xxGIDfe5BU.mp3 │ │ ├── Ho9h0ouemWQ.mp3 │ │ ├── J-SwzTNeN4M.mp3 │ │ └── k1WRcEDW83U.mp3 │ └── real │ │ ├── 4glfwiMXgwQ.mp3 │ │ ├── 9BkOf5LQQBQ.mp3 │ │ ├── F2PiuixG0NY.mp3 │ │ ├── FrT_8lQTN6w.mp3 │ │ ├── dB0Za__55-Q.mp3 │ │ ├── dpJE5qd9CRM.mp3 │ │ ├── fDQuO1nm_NY.mp3 │ │ ├── mg8ST-ymYv0.mp3 │ │ ├── pMgjwBgCZIw.mp3 │ │ ├── qCCWXoCURKY.mp3 │ │ ├── w4FyVGtMa4s.mp3 │ │ └── y0xEoKRHRi4.mp3 ├── librispeech_test-other │ ├── 367 │ │ ├── 367-130732-0000.flac │ │ ├── 367-130732-0001.flac │ │ ├── 367-130732-0002.flac │ │ ├── 367-130732-0003.flac │ │ ├── 367-130732-0004.flac │ │ ├── 367-130732-0005.flac │ │ ├── 367-130732-0006.flac │ │ ├── 367-130732-0007.flac │ │ ├── 367-130732-0008.flac │ │ └── 367-130732-0009.flac │ ├── 533 │ │ ├── 533-1066-0000.flac │ │ ├── 533-1066-0001.flac │ │ ├── 533-1066-0002.flac │ │ ├── 533-1066-0003.flac │ │ ├── 533-1066-0004.flac │ │ ├── 533-1066-0005.flac │ │ ├── 533-1066-0006.flac │ │ ├── 533-1066-0007.flac │ │ ├── 533-1066-0008.flac │ │ └── 533-1066-0009.flac │ ├── 1688 │ │ ├── 1688-142285-0000.flac │ │ ├── 1688-142285-0001.flac │ │ ├── 1688-142285-0002.flac │ │ ├── 1688-142285-0003.flac │ │ ├── 1688-142285-0004.flac │ │ ├── 1688-142285-0005.flac │ │ ├── 1688-142285-0006.flac │ │ ├── 1688-142285-0007.flac │ │ ├── 1688-142285-0008.flac │ │ └── 1688-142285-0009.flac │ ├── 1998 │ │ ├── 1998-15444-0000.flac │ │ ├── 1998-15444-0001.flac │ │ ├── 1998-15444-0002.flac │ │ ├── 1998-15444-0003.flac │ │ ├── 1998-15444-0004.flac │ │ ├── 1998-15444-0005.flac │ │ ├── 1998-15444-0006.flac │ │ ├── 1998-15444-0007.flac │ │ ├── 1998-15444-0008.flac │ │ └── 1998-15444-0009.flac │ ├── 2033 │ │ ├── 2033-164914-0000.flac │ │ ├── 2033-164914-0001.flac │ │ ├── 2033-164914-0002.flac │ │ ├── 2033-164914-0003.flac │ │ ├── 2033-164914-0004.flac │ │ ├── 2033-164914-0005.flac │ │ ├── 2033-164914-0006.flac │ │ ├── 2033-164914-0007.flac │ │ ├── 2033-164914-0008.flac │ │ └── 2033-164914-0009.flac │ ├── 2414 │ │ ├── 2414-128291-0000.flac │ │ ├── 2414-128291-0001.flac │ │ ├── 2414-128291-0002.flac │ │ ├── 2414-128291-0003.flac │ │ ├── 2414-128291-0004.flac │ │ ├── 2414-128291-0005.flac │ │ ├── 2414-128291-0006.flac │ │ ├── 2414-128291-0007.flac │ │ ├── 2414-128291-0008.flac │ │ └── 2414-128291-0009.flac │ ├── 2609 │ │ ├── 2609-156975-0000.flac │ │ ├── 2609-156975-0001.flac │ │ ├── 2609-156975-0002.flac │ │ ├── 2609-156975-0003.flac │ │ ├── 2609-156975-0004.flac │ │ ├── 2609-156975-0005.flac │ │ ├── 2609-156975-0006.flac │ │ ├── 2609-156975-0007.flac │ │ ├── 2609-156975-0008.flac │ │ └── 2609-156975-0009.flac │ ├── 3005 │ │ ├── 3005-163389-0000.flac │ │ ├── 3005-163389-0001.flac │ │ ├── 3005-163389-0002.flac │ │ ├── 3005-163389-0003.flac │ │ ├── 3005-163389-0004.flac │ │ ├── 3005-163389-0005.flac │ │ ├── 3005-163389-0006.flac │ │ ├── 3005-163389-0007.flac │ │ ├── 3005-163389-0008.flac │ │ └── 3005-163389-0009.flac │ ├── 3080 │ │ ├── 3080-5032-0000.flac │ │ ├── 3080-5032-0001.flac │ │ ├── 3080-5032-0002.flac │ │ ├── 3080-5032-0003.flac │ │ ├── 3080-5032-0004.flac │ │ ├── 3080-5032-0005.flac │ │ ├── 3080-5032-0006.flac │ │ ├── 3080-5032-0007.flac │ │ ├── 3080-5032-0008.flac │ │ └── 3080-5032-0009.flac │ ├── 3331 │ │ ├── 3331-159605-0000.flac │ │ ├── 3331-159605-0001.flac │ │ ├── 3331-159605-0002.flac │ │ ├── 3331-159605-0003.flac │ │ ├── 3331-159605-0004.flac │ │ ├── 3331-159605-0005.flac │ │ ├── 3331-159605-0006.flac │ │ ├── 3331-159605-0007.flac │ │ ├── 3331-159605-0008.flac │ │ └── 3331-159605-0009.flac │ ├── LICENSE.TXT │ └── SPEAKERS.TXT └── librispeech_train-clean-100 │ ├── 103-1240-0000.flac │ ├── 1034-121119-0000.flac │ ├── 1040-133433-0000.flac │ ├── 1069-133699-0000.flac │ ├── 1081-125237-0000.flac │ ├── 1088-129236-0000.flac │ ├── 1098-133695-0000.flac │ ├── 1116-132847-0000.flac │ ├── 118-121721-0000.flac │ ├── 1183-124566-0000.flac │ ├── 1235-135883-0000.flac │ ├── 1246-124548-0000.flac │ ├── 125-121124-0000.flac │ ├── 1263-138246-0000.flac │ ├── 1334-135589-0000.flac │ ├── 1355-39947-0000.flac │ ├── 1363-135842-0000.flac │ ├── 1447-130550-0000.flac │ ├── 1455-134435-0000.flac │ ├── 150-126107-0000.flac │ ├── 1502-122615-0000.flac │ ├── 1553-140047-0000.flac │ ├── 1578-140045-0000.flac │ ├── 1594-135914-0000.flac │ ├── 1624-142933-0000.flac │ ├── 163-121908-0000.flac │ ├── 1723-141149-0000.flac │ ├── 1737-142396-0000.flac │ ├── 1743-142912-0000.flac │ ├── 1841-150351-0000.flac │ ├── 1867-148436-0000.flac │ ├── 1898-145702-0000.flac │ ├── 19-198-0000.flac │ ├── 1926-143879-0000.flac │ ├── 196-122150-0000.flac │ ├── 1963-142393-0000.flac │ ├── 1970-10594-0000.flac │ ├── 198-126831-0000.flac │ ├── 1992-141719-0000.flac │ ├── 200-124139-0000.flac │ ├── 2002-139469-0000.flac │ ├── 2007-132570-0000.flac │ ├── 201-122255-0000.flac │ ├── 2092-145706-0000.flac │ ├── 211-122425-0000.flac │ ├── 2136-5140-0000.flac │ ├── 2159-179154-0000.flac │ ├── 2182-150130-0000.flac │ ├── 2196-170151-0000.flac │ ├── 226-122538-0000.flac │ ├── 2289-152253-0000.flac │ ├── 229-130880-0000.flac │ ├── 233-134440-0000.flac │ ├── 2384-152900-0000.flac │ ├── 2391-145015-0000.flac │ ├── 2416-152137-0000.flac │ ├── 2436-2476-0000.flac │ ├── 248-130644-0000.flac │ ├── 250-140277-0000.flac │ ├── 2514-149482-0000.flac │ ├── 2518-154825-0000.flac │ ├── 254-12312-0000.flac │ ├── 26-495-0000.flac │ ├── 2691-156745-0000.flac │ ├── 27-123349-0000.flac │ ├── 2764-36616-0000.flac │ ├── 2817-142371-0000.flac │ ├── 2836-5354-0000.flac │ ├── 2843-152918-0000.flac │ ├── 289-121652-0000.flac │ ├── 2893-139310-0000.flac │ ├── 2910-131096-0000.flac │ ├── 2911-12359-0000.flac │ ├── 2952-407-0000.flac │ ├── 298-126790-0000.flac │ ├── 2989-138028-0000.flac │ ├── 302-123504-0000.flac │ ├── 307-127535-0000.flac │ ├── 311-124404-0000.flac │ ├── 3112-9554-0000.flac │ ├── 3168-173564-0000.flac │ ├── 32-21625-0000.flac │ ├── 3214-167602-0000.flac │ ├── 322-124146-0000.flac │ ├── 3235-11599-0000.flac │ ├── 3240-131231-0000.flac │ ├── 3242-67153-0000.flac │ ├── 3259-158083-0000.flac │ ├── 328-129766-0000.flac │ ├── 332-128985-0000.flac │ ├── 3374-298025-0000.flac │ ├── 3436-172162-0000.flac │ ├── 3440-171006-0000.flac │ ├── 3486-166424-0000.flac │ ├── 3526-175658-0000.flac │ ├── 3607-135982-0000.flac │ ├── 3664-11714-0000.flac │ ├── 3699-175950-0000.flac │ ├── 3723-171115-0000.flac │ ├── 374-180298-0000.flac │ ├── 3807-4923-0000.flac │ ├── 3830-12529-0000.flac │ ├── 3857-180923-0000.flac │ ├── 3879-173592-0000.flac │ ├── 39-121914-0000.flac │ ├── 3947-13260-0000.flac │ ├── 3982-178459-0000.flac │ ├── 3983-5331-0000.flac │ ├── 40-121026-0000.flac │ ├── 4014-186175-0000.flac │ ├── 4018-103416-0000.flac │ ├── 403-126855-0000.flac │ ├── 405-130894-0000.flac │ ├── 4051-10927-0000.flac │ ├── 4088-158077-0000.flac │ ├── 412-126975-0000.flac │ ├── 4137-11701-0000.flac │ ├── 4160-11549-0000.flac │ ├── 4195-17507-0000.flac │ ├── 4214-7146-0000.flac │ ├── 426-122819-0000.flac │ ├── 4267-287369-0000.flac │ ├── 4297-13006-0000.flac │ ├── 4340-15220-0000.flac │ ├── 4362-15663-0000.flac │ ├── 4397-15666-0000.flac │ ├── 4406-16882-0000.flac │ ├── 441-128982-0000.flac │ ├── 4441-76250-0000.flac │ ├── 445-123857-0000.flac │ ├── 446-123501-0000.flac │ ├── 4481-17498-0000.flac │ ├── 458-126290-0000.flac │ ├── 460-172357-0000.flac │ ├── 4640-19187-0000.flac │ ├── 4680-16026-0000.flac │ ├── 4788-294466-0000.flac │ ├── 481-123719-0000.flac │ ├── 4813-248638-0000.flac │ ├── 4830-25898-0000.flac │ ├── 4853-27670-0000.flac │ ├── 4859-22176-0000.flac │ ├── 4898-20016-0000.flac │ ├── 5022-29405-0000.flac │ ├── 5049-25947-0000.flac │ ├── 5104-33406-0000.flac │ ├── 5163-18515-0000.flac │ ├── 5192-19396-0000.flac │ ├── 5322-7678-0000.flac │ ├── 5339-14133-0000.flac │ ├── 5390-24512-0000.flac │ ├── 5393-19218-0000.flac │ ├── 5456-24741-0000.flac │ ├── 5463-39173-0000.flac │ ├── 5514-19192-0000.flac │ ├── 5561-39621-0000.flac │ ├── 5652-19215-0000.flac │ ├── 5678-43301-0000.flac │ ├── 5688-15787-0000.flac │ ├── 5703-47198-0000.flac │ ├── 5750-100289-0000.flac │ ├── 5778-12761-0000.flac │ ├── 5789-57158-0000.flac │ ├── 5808-48608-0000.flac │ ├── 5867-48852-0000.flac │ ├── 587-41611-0000.flac │ ├── 60-121082-0000.flac │ ├── 6000-55211-0000.flac │ ├── 6019-3185-0000.flac │ ├── 6064-300880-0000.flac │ ├── 6078-54007-0000.flac │ ├── 6081-41997-0000.flac │ ├── 6147-34605-0000.flac │ ├── 6181-216552-0000.flac │ ├── 6209-34599-0000.flac │ ├── 625-132112-0000.flac │ ├── 6272-70168-0000.flac │ ├── 6367-65536-0000.flac │ ├── 6385-220959-0000.flac │ ├── 6415-100596-0000.flac │ ├── 6437-66172-0000.flac │ ├── 6454-107462-0000.flac │ ├── 6476-57446-0000.flac │ ├── 6529-62554-0000.flac │ ├── 6531-61334-0000.flac │ ├── 6563-285357-0000.flac │ ├── 669-129061-0000.flac │ ├── 6818-68772-0000.flac │ ├── 6836-61803-0000.flac │ ├── 6848-252322-0000.flac │ ├── 6880-216547-0000.flac │ ├── 6925-80680-0000.flac │ ├── 696-92939-0000.flac │ ├── 7059-77897-0000.flac │ ├── 7067-76047-0000.flac │ ├── 7078-271888-0000.flac │ ├── 7113-86041-0000.flac │ ├── 7148-59157-0000.flac │ ├── 7178-34644-0000.flac │ ├── 7190-90542-0000.flac │ ├── 7226-86964-0000.flac │ ├── 7264-92310-0000.flac │ ├── 7278-104730-0000.flac │ ├── 730-358-0000.flac │ ├── 7302-86814-0000.flac │ ├── 7312-92432-0000.flac │ ├── 7367-86737-0000.flac │ ├── 7402-59171-0000.flac │ ├── 7447-91186-0000.flac │ ├── 7505-258958-0000.flac │ ├── 7511-102419-0000.flac │ ├── 7517-100429-0000.flac │ ├── 7635-105409-0000.flac │ ├── 7780-274562-0000.flac │ ├── 7794-295947-0000.flac │ ├── 78-368-0000.flac │ ├── 7800-283478-0000.flac │ ├── 7859-102518-0000.flac │ ├── 8014-112586-0000.flac │ ├── 8051-118101-0000.flac │ ├── 8063-274112-0000.flac │ ├── 8088-284756-0000.flac │ ├── 8095-274345-0000.flac │ ├── 8098-275181-0000.flac │ ├── 8108-274318-0000.flac │ ├── 8123-275193-0000.flac │ ├── 8226-274369-0000.flac │ ├── 8238-274553-0000.flac │ ├── 83-11691-0000.flac │ ├── 831-130739-0000.flac │ ├── 8312-279790-0000.flac │ ├── 8324-286681-0000.flac │ ├── 839-130898-0000.flac │ ├── 8419-286667-0000.flac │ ├── 8425-246962-0000.flac │ ├── 8465-246940-0000.flac │ ├── 8468-286673-0000.flac │ ├── 8580-287363-0000.flac │ ├── 8609-262281-0000.flac │ ├── 8629-261139-0000.flac │ ├── 8630-305212-0000.flac │ ├── 87-121553-0000.flac │ ├── 8747-293952-0000.flac │ ├── 8770-295462-0000.flac │ ├── 8797-294123-0000.flac │ ├── 8838-298545-0000.flac │ ├── 887-123289-0000.flac │ ├── 89-218-0000.flac │ ├── 8975-270782-0000.flac │ ├── 909-131041-0000.flac │ ├── 911-128684-0000.flac │ └── SPEAKERS.TXT ├── demo01_similarity.py ├── demo02_diarization.py ├── demo03_projection.py ├── demo04_clustering.py ├── demo05_fake_speech_detection.py ├── demo_utils.py ├── plots ├── all_clustering.png ├── clustering_sex.png ├── clustering_speaker.png ├── fake_speech_detection.png ├── sim_matrix_1.png ├── sim_matrix_2.png └── sim_matrix_3.png ├── requirements_demos.txt ├── requirements_package.txt ├── resemblyzer ├── __init__.py ├── audio.py ├── hparams.py ├── pretrained.pt └── voice_encoder.py └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | *.pyc 3 | .DS_Store 4 | ./__init__.py 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | 10 | # C extensions 11 | *.so 12 | 13 | # Distribution / packaging 14 | .Python 15 | build/ 16 | develop-eggs/ 17 | dist/ 18 | downloads/ 19 | eggs/ 20 | .eggs/ 21 | lib/ 22 | lib64/ 23 | parts/ 24 | sdist/ 25 | var/ 26 | wheels/ 27 | *.egg-info/ 28 | .installed.cfg 29 | *.egg 30 | MANIFEST 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | .hypothesis/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | .static_storage/ 60 | .media/ 61 | local_settings.py 62 | 63 | # Flask stuff: 64 | instance/ 65 | .webassets-cache 66 | 67 | # Scrapy stuff: 68 | .scrapy 69 | 70 | # Sphinx documentation 71 | docs/_build/ 72 | 73 | # PyBuilder 74 | target/ 75 | 76 | # Jupyter Notebook 77 | .ipynb_checkpoints 78 | 79 | # pyenv 80 | .python-version 81 | 82 | # celery beat schedule file 83 | celerybeat-schedule 84 | 85 | # SageMath parsed files 86 | *.sage.py 87 | 88 | # Environments 89 | .env 90 | .venv 91 | env/ 92 | venv/ 93 | ENV/ 94 | env.bak/ 95 | venv.bak/ 96 | 97 | # Spyder project settings 98 | .spyderproject 99 | .spyproject 100 | 101 | # Rope project settings 102 | .ropeproject 103 | 104 | # mkdocs documentation 105 | /site 106 | 107 | # mypy 108 | .mypy_cache/ 109 | 110 | 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However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | 179 | Copyright 2019 Resemble.AI 180 | 181 | Licensed under the Apache License, Version 2.0 (the "License"); 182 | you may not use this file except in compliance with the License. 183 | You may obtain a copy of the License at 184 | 185 | http://www.apache.org/licenses/LICENSE-2.0 186 | 187 | Unless required by applicable law or agreed to in writing, software 188 | distributed under the License is distributed on an "AS IS" BASIS, 189 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 190 | See the License for the specific language governing permissions and 191 | limitations under the License. 192 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Resemblyzer allows you to derive a **high-level representation of a voice** through a deep learning model (referred to as the voice encoder). Given an audio file of speech, it creates a summary vector of 256 values (an embedding, often shortened to "embed" in this repo) that summarizes the characteristics of the voice spoken. 2 | 3 | N.B.: this repo holds 100mb of audio data for demonstration purpose. To get [the package](https://pypi.org/project/Resemblyzer/) alone, run `pip install resemblyzer` (python 3.5+ is required). 4 | 5 | ## Demos 6 | **Speaker diarization**: [\[Demo 02\]](https://github.com/resemble-ai/Resemblyzer/blob/master/demo02_diarization.py) recognize who is talking when with only a few seconds of reference audio per speaker: 7 | *(click the image for a video)* 8 | 9 | [![demo_02](https://i.imgur.com/2MpNauG.png)](https://streamable.com/uef39) 10 | 11 | **Fake speech detection**: [\[Demo 05\]](https://github.com/resemble-ai/Resemblyzer/blob/master/demo05_fake_speech_detection.py) modest detection of fake speech by comparing the similarity of 12 unknown utterances (6 real ones, 6 fakes) against ground truth reference audio. Scores above the dashed line are predicted as real, so the model makes one error here. 12 | 13 | ![demo_05](plots/fake_speech_detection.png?raw=true) 14 | 15 | For reference, [this](https://www.youtube.com/watch?v=Ho9h0ouemWQ) is the fake video that achieved a high score. 16 | 17 | **Visualizing the manifold**: 18 | [\[Demo 03 - left\]](https://github.com/resemble-ai/Resemblyzer/blob/master/demo03_projection.py) projecting the embeddings of 100 utterances (10 each from 10 speakers) in 2D space. The utterances from the same speakers form a tight cluster. With a trivial clustering algorithm, the speaker verification error rate for this example (with data unseen in training) would be 0%. 19 | [\[Demo 04 - right\]](https://github.com/resemble-ai/Resemblyzer/blob/master/demo04_clustering.py) same as demo 03 but with 251 embeddings all from distinct speakers, highlighting that the model has learned on its own to identify the sex of the speaker. 20 | 21 | ![demo_03_04](plots/all_clustering.png?raw=true) 22 | 23 | **Cross-similarity**: [\[Demo 01\]](https://github.com/resemble-ai/Resemblyzer/blob/master/demo01_similarity.py) comparing 10 utterances from 10 speakers against 10 other utterances from the same speakers. 24 | 25 | ![demo_01](plots/sim_matrix_1.png?raw=true) 26 | 27 | 28 | 29 | ## What can I do with this package? 30 | Resemblyzer has many uses: 31 | - **Voice similarity metric**: compare different voices and get a value on how similar they sound. This leads to other applications: 32 | - **Speaker verification**: create a voice profile for a person from a few seconds of speech (5s - 30s) and compare it to that of new audio. Reject similarity scores below a threshold. 33 | - **Speaker diarization**: figure out who is talking when by comparing voice profiles with the continuous embedding of a multispeaker speech segment. 34 | - **Fake speech detection**: verify if some speech is legitimate or fake by comparing the similarity of possible fake speech to real speech. 35 | - **High-level feature extraction**: you can use the embeddings generated as feature vectors for machine learning or data analysis. This also leads to other applications: 36 | - **Voice cloning**: see [this other project](https://github.com/CorentinJ/Real-Time-Voice-Cloning). 37 | - **Component analysis**: figure out accents, tones, prosody, gender, ... through a component analysis of the embeddings. 38 | - **Virtual voices**: create entirely new voice embeddings by sampling from a prior distribution. 39 | - **Loss function**: you can backpropagate through the voice encoder model and use it as a perceptual loss for your deep learning model! The voice encoder is written in PyTorch. 40 | 41 | Resemblyzer is fast to execute (around 1000x real-time on a GTX 1080, with a minimum of 10ms for I/O operations), and can run both on CPU or GPU. It is robust to noise. It currently works best on English language only, but should still be able to perform somewhat decently on other languages. 42 | 43 | 44 | ## Code example 45 | This is a short example showing how to use Resemblyzer: 46 | ```python 47 | from resemblyzer import VoiceEncoder, preprocess_wav 48 | from pathlib import Path 49 | import numpy as np 50 | 51 | fpath = Path("path_to_an_audio_file") 52 | wav = preprocess_wav(fpath) 53 | 54 | encoder = VoiceEncoder() 55 | embed = encoder.embed_utterance(wav) 56 | np.set_printoptions(precision=3, suppress=True) 57 | print(embed) 58 | ``` 59 | 60 | I highly suggest giving a peek to the demos to understand how similarity is computed and to see practical usages of the voice encoder. 61 | 62 | ## Additional info 63 | Resemblyzer emerged as a side project of the [Real-Time Voice Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) repository. The pretrained model that comes with Resemblyzer is interchangeable with models trained in that repository, so feel free to finetune a model on new data and possibly new languages! The paper from which the voice encoder was implemented is [Generalized End-To-End Loss for Speaker Verification](https://arxiv.org/pdf/1710.10467.pdf) (in which it is called the *speaker* encoder). 64 | -------------------------------------------------------------------------------- /audio_data/README.md: -------------------------------------------------------------------------------- 1 | In this directory: 2 | - 100 audio samples from [LibriSpeech](http://www.openslr.org/12) test-other (10 per speaker) 3 | - 251 audio samples from [LibriSpeech](http://www.openslr.org/12) train-clean (1 per speaker) 4 | - Interview segments from [this video](https://www.youtube.com/watch?v=X2zqiX6yL3I) 5 | 6 | -------------------------------------------------------------------------------- /audio_data/X2zqiX6yL3I.mp3: -------------------------------------------------------------------------------- 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/audio_data/librispeech_train-clean-100/909-131041-0000.flac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/audio_data/librispeech_train-clean-100/909-131041-0000.flac -------------------------------------------------------------------------------- /audio_data/librispeech_train-clean-100/911-128684-0000.flac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/audio_data/librispeech_train-clean-100/911-128684-0000.flac -------------------------------------------------------------------------------- /demo01_similarity.py: -------------------------------------------------------------------------------- 1 | from resemblyzer import preprocess_wav, VoiceEncoder 2 | from demo_utils import * 3 | from itertools import groupby 4 | from pathlib import Path 5 | from tqdm import tqdm 6 | import matplotlib.pyplot as plt 7 | import numpy as np 8 | 9 | 10 | # The demos are ordered so as to make the explanations in the comments consistent. If you only 11 | # care about running the code, then you don't have to follow that order. 12 | # DEMO 01: we'll show how to compare speech segments (=utterances) between them to get a metric 13 | # on how similar their voices sound. We expect utterances from the same speaker to have a high 14 | # similarity, and those from distinct speakers to have a lower one. 15 | 16 | 17 | # The neural network will automatically use CUDA if it'speaker available on your machine, otherwise it 18 | # will use the CPU. You can enforce a device of your choice by passing its name as argument to the 19 | # constructor. The model might take a few seconds to load with CUDA, but it then executes very 20 | # quickly. 21 | encoder = VoiceEncoder() 22 | 23 | # We'll use a smaller version of the dataset LibriSpeech test-other to run our examples. This 24 | # smaller dataset contains 10 speakers with 10 utterances each. N.B. "wav" in variable names stands 25 | # for "waveform" and not the wav file extension. 26 | wav_fpaths = list(Path("audio_data", "librispeech_test-other").glob("**/*.flac")) 27 | # Group the wavs per speaker and load them using the preprocessing function provided with 28 | # resemblyzer to load wavs in memory. It normalizes the volume, trims long silences and resamples 29 | # the wav to the correct sampling rate. 30 | speaker_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in 31 | groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"), 32 | lambda wav_fpath: wav_fpath.parent.stem)} 33 | 34 | 35 | ## Similarity between two utterances from each speaker 36 | # Embed two utterances A and B for each speaker 37 | embeds_a = np.array([encoder.embed_utterance(wavs[0]) for wavs in speaker_wavs.values()]) 38 | embeds_b = np.array([encoder.embed_utterance(wavs[1]) for wavs in speaker_wavs.values()]) 39 | # Each array is of shape (num_speakers, embed_size) which should be (10, 256) if you haven't 40 | # changed anything. 41 | print("Shape of embeddings: %s" % str(embeds_a.shape)) 42 | 43 | # Compute the similarity matrix. The similarity of two embeddings is simply their dot 44 | # product, because the similarity metric is the cosine similarity and the embeddings are 45 | # already L2-normed. 46 | # Short version: 47 | utt_sim_matrix = np.inner(embeds_a, embeds_b) 48 | # Long, detailed version: 49 | utt_sim_matrix2 = np.zeros((len(embeds_a), len(embeds_b))) 50 | for i in range(len(embeds_a)): 51 | for j in range(len(embeds_b)): 52 | # The @ notation is exactly equivalent to np.dot(embeds_a[i], embeds_b[i]) 53 | utt_sim_matrix2[i, j] = embeds_a[i] @ embeds_b[j] 54 | assert np.allclose(utt_sim_matrix, utt_sim_matrix2) 55 | 56 | 57 | ## Similarity between two speaker embeddings 58 | # Divide the utterances of each speaker in groups of identical size and embed each group as a 59 | # speaker embedding 60 | spk_embeds_a = np.array([encoder.embed_speaker(wavs[:len(wavs) // 2]) \ 61 | for wavs in speaker_wavs.values()]) 62 | spk_embeds_b = np.array([encoder.embed_speaker(wavs[len(wavs) // 2:]) \ 63 | for wavs in speaker_wavs.values()]) 64 | spk_sim_matrix = np.inner(spk_embeds_a, spk_embeds_b) 65 | 66 | 67 | ## Draw the plots 68 | fix, axs = plt.subplots(2, 2, figsize=(8, 10)) 69 | labels_a = ["%s-A" % i for i in speaker_wavs.keys()] 70 | labels_b = ["%s-B" % i for i in speaker_wavs.keys()] 71 | mask = np.eye(len(utt_sim_matrix), dtype=bool) 72 | plot_similarity_matrix(utt_sim_matrix, labels_a, labels_b, axs[0, 0], 73 | "Cross-similarity between utterances\n(speaker_id-utterance_group)") 74 | plot_histograms((utt_sim_matrix[mask], utt_sim_matrix[np.logical_not(mask)]), axs[0, 1], 75 | ["Same speaker", "Different speakers"], 76 | "Normalized histogram of similarity\nvalues between utterances") 77 | plot_similarity_matrix(spk_sim_matrix, labels_a, labels_b, axs[1, 0], 78 | "Cross-similarity between speakers\n(speaker_id-utterances_group)") 79 | plot_histograms((spk_sim_matrix[mask], spk_sim_matrix[np.logical_not(mask)]), axs[1, 1], 80 | ["Same speaker", "Different speakers"], 81 | "Normalized histogram of similarity\nvalues between speakers") 82 | plt.show() 83 | -------------------------------------------------------------------------------- /demo02_diarization.py: -------------------------------------------------------------------------------- 1 | from resemblyzer import preprocess_wav, VoiceEncoder 2 | from demo_utils import * 3 | from pathlib import Path 4 | 5 | 6 | # DEMO 02: we'll show how this similarity measure can be used to perform speaker diarization 7 | # (telling who is speaking when in a recording). 8 | 9 | 10 | ## Get reference audios 11 | # Load the interview audio from disk 12 | # Source for the interview: https://www.youtube.com/watch?v=X2zqiX6yL3I 13 | wav_fpath = Path("audio_data", "X2zqiX6yL3I.mp3") 14 | wav = preprocess_wav(wav_fpath) 15 | 16 | # Cut some segments from single speakers as reference audio 17 | segments = [[0, 5.5], [6.5, 12], [17, 25]] 18 | speaker_names = ["Kyle Gass", "Sean Evans", "Jack Black"] 19 | speaker_wavs = [wav[int(s[0] * sampling_rate):int(s[1] * sampling_rate)] for s in segments] 20 | 21 | 22 | ## Compare speaker embeds to the continuous embedding of the interview 23 | # Derive a continuous embedding of the interview. We put a rate of 16, meaning that an 24 | # embedding is generated every 0.0625 seconds. It is good to have a higher rate for speaker 25 | # diarization, but it is not so useful for when you only need a summary embedding of the 26 | # entire utterance. A rate of 2 would have been enough, but 16 is nice for the sake of the 27 | # demonstration. 28 | # We'll exceptionally force to run this on CPU, because it uses a lot of RAM and most GPUs 29 | # won't have enough. There's a speed drawback, but it remains reasonable. 30 | encoder = VoiceEncoder("cpu") 31 | print("Running the continuous embedding on cpu, this might take a while...") 32 | _, cont_embeds, wav_splits = encoder.embed_utterance(wav, return_partials=True, rate=16) 33 | 34 | 35 | # Get the continuous similarity for every speaker. It amounts to a dot product between the 36 | # embedding of the speaker and the continuous embedding of the interview 37 | speaker_embeds = [encoder.embed_utterance(speaker_wav) for speaker_wav in speaker_wavs] 38 | similarity_dict = {name: cont_embeds @ speaker_embed for name, speaker_embed in 39 | zip(speaker_names, speaker_embeds)} 40 | 41 | 42 | ## Run the interactive demo 43 | interactive_diarization(similarity_dict, wav, wav_splits) 44 | -------------------------------------------------------------------------------- /demo03_projection.py: -------------------------------------------------------------------------------- 1 | from resemblyzer import preprocess_wav, VoiceEncoder 2 | from demo_utils import * 3 | from itertools import groupby 4 | from pathlib import Path 5 | from tqdm import tqdm 6 | import numpy as np 7 | 8 | 9 | # DEMO 03: we'll show one way to visualize these utterance embeddings. Since they are 10 | # 256-dimensional, it is much simpler for us to get an overview of their manifold if we reduce 11 | # their dimensionality first. By doing so, we can observe clusters that form for utterances of 12 | # identical characteristics. What we'll see is that clusters form for distinct speakers, 13 | # and they are very tight and even linearly separable. 14 | 15 | 16 | ## Gather the wavs 17 | wav_fpaths = list(Path("audio_data", "librispeech_test-other").glob("**/*.flac")) 18 | speakers = list(map(lambda wav_fpath: wav_fpath.parent.stem, wav_fpaths)) 19 | wavs = np.array(list(map(preprocess_wav, tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths)))), dtype=object) 20 | speaker_wavs = {speaker: wavs[list(indices)] for speaker, indices in 21 | groupby(range(len(wavs)), lambda i: speakers[i])} 22 | 23 | 24 | ## Compute the embeddings 25 | encoder = VoiceEncoder() 26 | utterance_embeds = np.array(list(map(encoder.embed_utterance, wavs))) 27 | 28 | 29 | ## Project the embeddings in 2D space 30 | plot_projections(utterance_embeds, speakers, title="Embedding projections") 31 | plt.show() 32 | -------------------------------------------------------------------------------- /demo04_clustering.py: -------------------------------------------------------------------------------- 1 | from sklearn.linear_model import LogisticRegression 2 | from resemblyzer import preprocess_wav, VoiceEncoder 3 | from demo_utils import * 4 | from pathlib import Path 5 | from tqdm import tqdm 6 | import numpy as np 7 | 8 | 9 | # DEMO 04: building from the previous demonstration, we'll show how natural properties of the 10 | # voice can emerge through analysis of the embeddings. The dimensionality reduction algorithm 11 | # UMAP will create clusters from embeddings with similar features. When provided with samples 12 | # from many distinct speakers, it tends to create two clusters for each sex. This is what we'll 13 | # show here, by using the speaker metadata file provided in the LibriSpeech dataset to retrieve 14 | # the sex of each speaker. Note that this information was never used during training of the voice 15 | # encoder model, be it as input feature or target. This means that the distinction was learned 16 | # entirely in an unsupervised manner. 17 | # Note that if you try this code on different data (or on fewer speakers), you may observe an 18 | # entirely different clustering, e.g. based on the accent of the speakers. Changing the 19 | # parameters of UMAP or the dimensionality reduction altogether will also give you a different 20 | # view of the manifold. 21 | 22 | 23 | ## Gather a single utterance per speaker 24 | data_dir = Path("audio_data", "librispeech_train-clean-100") 25 | wav_fpaths = list(data_dir.glob("*.flac")) 26 | speakers = [fpath.stem.split("-")[0] for fpath in wav_fpaths] 27 | wavs = [preprocess_wav(wav_fpath) for wav_fpath in \ 28 | tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit=" utterances")] 29 | 30 | # Get the sex of each speaker from the metadata file 31 | with data_dir.joinpath("SPEAKERS.TXT").open("r") as f: 32 | sexes = dict(l.replace(" ", "").split("|")[:2] for l in f if not l.startswith(";")) 33 | markers = ["x" if sexes[speaker] == "M" else "o" for speaker in speakers] 34 | colors = ["black"] * len(speakers) 35 | 36 | 37 | ## Compute the embeddings 38 | encoder = VoiceEncoder() 39 | utterance_embeds = np.array(list(map(encoder.embed_utterance, wavs))) 40 | 41 | 42 | ## Project the embeddings in 2D space. 43 | _, ax = plt.subplots(figsize=(6, 6)) 44 | # Passing min_dist=1 to UMAP will make it so the projections don't necessarily need to fit in 45 | # clusters, so that you can have a better idea of what the manifold really looks like. 46 | projs = plot_projections(utterance_embeds, speakers, ax, colors, markers, False, 47 | min_dist=1) 48 | ax.set_title("Embeddings for %d speakers" % (len(speakers))) 49 | ax.scatter([], [], marker="x", c="black", label="Male speaker") 50 | ax.scatter([], [], marker="o", c="black", label="Female speaker") 51 | 52 | # Separate the data by the sex 53 | classifier = LogisticRegression(solver="lbfgs") 54 | classifier.fit(projs, markers) 55 | x = np.linspace(*ax.get_xlim(), num=200) 56 | y = -(classifier.coef_[0, 0] * x + classifier.intercept_) / classifier.coef_[0, 1] 57 | mask = (y > ax.get_ylim()[0]) & (y < ax.get_ylim()[1]) 58 | ax.plot(x[mask], y[mask], label="Decision boundary") 59 | 60 | ax.legend() 61 | plt.show() 62 | -------------------------------------------------------------------------------- /demo05_fake_speech_detection.py: -------------------------------------------------------------------------------- 1 | from resemblyzer import preprocess_wav, VoiceEncoder 2 | from demo_utils import * 3 | from pathlib import Path 4 | from tqdm import tqdm 5 | import numpy as np 6 | 7 | 8 | # DEMO 05: In this demo we'll show how we can achieve a modest form of fake speech detection with 9 | # Resemblyzer. This method assumes you have some reference audio for the target speaker that you 10 | # know is real, so it is not a universal fake speech detector on its own. 11 | # In the audio data directory we have 18 segments of Donald Trump. 12 are real and extracted from 12 | # actual speeches, while the remaining 6 others are fake and generated by various users on 13 | # youtube, with a high discrepancy of voice cloning quality and naturalness achieved. We will 14 | # take 6 segments of real speech as ground truth reference and compare those against the 12 15 | # remaining. Those segments are selected at random, so will run into different results every time 16 | # you run the script, but they should be more or less consistent. 17 | # Using the voice of Donald Trump is merely a matter of convenience, as several fake speeches 18 | # with his voice were already put up on youtube. This choice was not politically motivated. 19 | 20 | 21 | ## Load and preprocess the audio 22 | data_dir = Path("audio_data", "donald_trump") 23 | wav_fpaths = list(data_dir.glob("**/*.mp3")) 24 | wavs = [preprocess_wav(wav_fpath) for wav_fpath in \ 25 | tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit=" utterances")] 26 | 27 | 28 | ## Compute the embeddings 29 | encoder = VoiceEncoder() 30 | embeds = np.array([encoder.embed_utterance(wav) for wav in wavs]) 31 | speakers = np.array([fpath.parent.name for fpath in wav_fpaths]) 32 | names = np.array([fpath.stem for fpath in wav_fpaths]) 33 | 34 | 35 | # Take 6 real embeddings at random, and leave the 6 others for testing 36 | gt_indices = np.random.choice(*np.where(speakers == "real"), 6, replace=False) 37 | mask = np.zeros(len(embeds), dtype=bool) 38 | mask[gt_indices] = True 39 | gt_embeds = embeds[mask] 40 | gt_names = names[mask] 41 | gt_speakers = speakers[mask] 42 | embeds, speakers, names = embeds[~mask], speakers[~mask], names[~mask] 43 | 44 | 45 | ## Compare all embeddings against the ground truth embeddings, and compute the average similarities. 46 | scores = (gt_embeds @ embeds.T).mean(axis=0) 47 | 48 | # Order the scores by decreasing order 49 | sort = np.argsort(scores)[::-1] 50 | scores, names, speakers = scores[sort], names[sort], speakers[sort] 51 | 52 | 53 | ## Plot the scores 54 | fig, _ = plt.subplots(figsize=(6, 6)) 55 | indices = np.arange(len(scores)) 56 | plt.axhline(0.84, ls="dashed", label="Prediction threshold", c="black") 57 | plt.bar(indices[speakers == "real"], scores[speakers == "real"], color="green", label="Real") 58 | plt.bar(indices[speakers == "fake"], scores[speakers == "fake"], color="red", label="Fake") 59 | plt.legend() 60 | plt.xticks(indices, names, rotation="vertical", fontsize=8) 61 | plt.xlabel("Youtube video IDs") 62 | plt.ylim(0.7, 1) 63 | plt.ylabel("Similarity to ground truth") 64 | fig.subplots_adjust(bottom=0.25) 65 | plt.show() 66 | -------------------------------------------------------------------------------- /demo_utils.py: -------------------------------------------------------------------------------- 1 | from mpl_toolkits.axes_grid1 import make_axes_locatable 2 | from matplotlib.animation import FuncAnimation 3 | from resemblyzer import sampling_rate 4 | from matplotlib import cm 5 | from time import sleep, perf_counter as timer 6 | from umap import UMAP 7 | from sys import stderr 8 | import matplotlib.pyplot as plt 9 | import numpy as np 10 | 11 | _default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] 12 | _my_colors = np.array([ 13 | [0, 127, 70], 14 | [255, 0, 0], 15 | [255, 217, 38], 16 | [0, 135, 255], 17 | [165, 0, 165], 18 | [255, 167, 255], 19 | [97, 142, 151], 20 | [0, 255, 255], 21 | [255, 96, 38], 22 | [142, 76, 0], 23 | [33, 0, 127], 24 | [0, 0, 0], 25 | [183, 183, 183], 26 | [76, 255, 0], 27 | ], dtype=float) / 255 28 | 29 | 30 | def play_wav(wav, blocking=True): 31 | try: 32 | import sounddevice as sd 33 | # Small bug with sounddevice.play: the audio is cut 0.5 second too early. We pad it to 34 | # make up for that 35 | wav = np.concatenate((wav, np.zeros(sampling_rate // 2))) 36 | sd.play(wav, sampling_rate, blocking=blocking) 37 | except Exception as e: 38 | print("Failed to play audio: %s" % repr(e)) 39 | 40 | 41 | def plot_similarity_matrix(matrix, labels_a=None, labels_b=None, ax: plt.Axes=None, title=""): 42 | if ax is None: 43 | _, ax = plt.subplots() 44 | fig = plt.gcf() 45 | 46 | img = ax.matshow(matrix, extent=(-0.5, matrix.shape[0] - 0.5, 47 | -0.5, matrix.shape[1] - 0.5)) 48 | 49 | ax.xaxis.set_ticks_position("bottom") 50 | if labels_a is not None: 51 | ax.set_xticks(range(len(labels_a))) 52 | ax.set_xticklabels(labels_a, rotation=90) 53 | if labels_b is not None: 54 | ax.set_yticks(range(len(labels_b))) 55 | ax.set_yticklabels(labels_b[::-1]) # Upper origin -> reverse y axis 56 | ax.set_title(title) 57 | 58 | cax = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.15) 59 | fig.colorbar(img, cax=cax, ticks=np.linspace(0.4, 1, 7)) 60 | img.set_clim(0.4, 1) 61 | img.set_cmap("inferno") 62 | 63 | return ax 64 | 65 | 66 | def plot_histograms(all_samples, ax=None, names=None, title=""): 67 | """ 68 | Plots (possibly) overlapping histograms and their median 69 | """ 70 | if ax is None: 71 | _, ax = plt.subplots() 72 | 73 | for samples, color, name in zip(all_samples, _default_colors, names): 74 | ax.hist(samples, density=True, color=color + "80", label=name) 75 | ax.legend() 76 | ax.set_xlim(0.35, 1) 77 | ax.set_yticks([]) 78 | ax.set_title(title) 79 | 80 | ylim = ax.get_ylim() 81 | ax.set_ylim(*ylim) # Yeah, I know 82 | for samples, color in zip(all_samples, _default_colors): 83 | median = np.median(samples) 84 | ax.vlines(median, *ylim, color, "dashed") 85 | ax.text(median, ylim[1] * 0.15, "median", rotation=270, color=color) 86 | 87 | return ax 88 | 89 | 90 | def plot_projections(embeds, speakers, ax=None, colors=None, markers=None, legend=True, 91 | title="", **kwargs): 92 | if ax is None: 93 | _, ax = plt.subplots(figsize=(6, 6)) 94 | 95 | # Compute the 2D projections. You could also project to another number of dimensions (e.g. 96 | # for a 3D plot) or use a different different dimensionality reduction like PCA or TSNE. 97 | reducer = UMAP(**kwargs) 98 | projs = reducer.fit_transform(embeds) 99 | 100 | # Draw the projections 101 | speakers = np.array(speakers) 102 | colors = colors or _my_colors 103 | for i, speaker in enumerate(np.unique(speakers)): 104 | speaker_projs = projs[speakers == speaker] 105 | marker = "o" if markers is None else markers[i] 106 | label = speaker if legend else None 107 | ax.scatter(*speaker_projs.T, c=[colors[i]], marker=marker, label=label) 108 | 109 | if legend: 110 | ax.legend(title="Speakers", ncol=2) 111 | ax.set_title(title) 112 | ax.set_xticks([]) 113 | ax.set_yticks([]) 114 | ax.set_aspect("equal") 115 | 116 | return projs 117 | 118 | 119 | def interactive_diarization(similarity_dict, wav, wav_splits, x_crop=5, show_time=False): 120 | fig, ax = plt.subplots() 121 | lines = [ax.plot([], [], label=name)[0] for name in similarity_dict.keys()] 122 | text = ax.text(0, 0, "", fontsize=10) 123 | 124 | def init(): 125 | ax.set_ylim(0.4, 1) 126 | ax.set_ylabel("Similarity") 127 | if show_time: 128 | ax.set_xlabel("Time (seconds)") 129 | else: 130 | ax.set_xticks([]) 131 | ax.set_title("Diarization") 132 | ax.legend(loc="lower right") 133 | return lines + [text] 134 | 135 | times = [((s.start + s.stop) / 2) / sampling_rate for s in wav_splits] 136 | rate = 1 / (times[1] - times[0]) 137 | crop_range = int(np.round(x_crop * rate)) 138 | ticks = np.arange(0, len(wav_splits), rate) 139 | ref_time = timer() 140 | 141 | def update(i): 142 | # Crop plot 143 | crop = (max(i - crop_range // 2, 0), i + crop_range // 2) 144 | ax.set_xlim(i - crop_range // 2, crop[1]) 145 | if show_time: 146 | crop_ticks = ticks[(crop[0] <= ticks) * (ticks <= crop[1])] 147 | ax.set_xticks(crop_ticks) 148 | ax.set_xticklabels(np.round(crop_ticks / rate).astype(np.int)) 149 | 150 | # Plot the prediction 151 | similarities = [s[i] for s in similarity_dict.values()] 152 | best = np.argmax(similarities) 153 | name, similarity = list(similarity_dict.keys())[best], similarities[best] 154 | if similarity > 0.75: 155 | message = "Speaker: %s (confident)" % name 156 | color = _default_colors[best] 157 | elif similarity > 0.65: 158 | message = "Speaker: %s (uncertain)" % name 159 | color = _default_colors[best] 160 | else: 161 | message = "Unknown/No speaker" 162 | color = "black" 163 | text.set_text(message) 164 | text.set_c(color) 165 | text.set_position((i, 0.96)) 166 | 167 | # Plot data 168 | for line, (name, similarities) in zip(lines, similarity_dict.items()): 169 | line.set_data(range(crop[0], i + 1), similarities[crop[0]:i + 1]) 170 | 171 | # Block to synchronize with the audio (interval is not reliable) 172 | current_time = timer() - ref_time 173 | if current_time < times[i]: 174 | sleep(times[i] - current_time) 175 | elif current_time - 0.2 > times[i]: 176 | print("Animation is delayed further than 200ms!", file=stderr) 177 | return lines + [text] 178 | 179 | ani = FuncAnimation(fig, update, frames=len(wav_splits), init_func=init, blit=not show_time, 180 | repeat=False, interval=1) 181 | play_wav(wav, blocking=False) 182 | plt.show() 183 | 184 | 185 | def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)): 186 | if ax is None: 187 | _, ax = plt.subplots() 188 | 189 | if shape is None: 190 | height = int(np.sqrt(len(embed))) 191 | shape = (height, -1) 192 | embed = embed.reshape(shape) 193 | 194 | cmap = cm.get_cmap() 195 | mappable = ax.imshow(embed, cmap=cmap) 196 | cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04) 197 | cbar.set_clim(*color_range) 198 | 199 | ax.set_xticks([]), ax.set_yticks([]) 200 | ax.set_title(title) 201 | 202 | -------------------------------------------------------------------------------- /plots/all_clustering.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/all_clustering.png -------------------------------------------------------------------------------- /plots/clustering_sex.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/clustering_sex.png -------------------------------------------------------------------------------- /plots/clustering_speaker.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/clustering_speaker.png -------------------------------------------------------------------------------- /plots/fake_speech_detection.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/fake_speech_detection.png -------------------------------------------------------------------------------- /plots/sim_matrix_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/sim_matrix_1.png -------------------------------------------------------------------------------- /plots/sim_matrix_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/sim_matrix_2.png -------------------------------------------------------------------------------- /plots/sim_matrix_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/plots/sim_matrix_3.png -------------------------------------------------------------------------------- /requirements_demos.txt: -------------------------------------------------------------------------------- 1 | librosa>=0.9.1 2 | numpy>=1.20.0 3 | webrtcvad>=2.0.10 4 | torch>=1.0.1 5 | scipy>=1.2.1 6 | typing 7 | matplotlib>=3.0.0 8 | sounddevice 9 | tqdm 10 | umap-learn 11 | -------------------------------------------------------------------------------- /requirements_package.txt: -------------------------------------------------------------------------------- 1 | librosa>=0.9.1 2 | numpy>=1.20.0 3 | webrtcvad>=2.0.10 4 | torch>=1.0.1 5 | scipy>=1.2.1 6 | typing 7 | -------------------------------------------------------------------------------- /resemblyzer/__init__.py: -------------------------------------------------------------------------------- 1 | name = "resemblyzer" 2 | 3 | from resemblyzer.audio import preprocess_wav, wav_to_mel_spectrogram, trim_long_silences, \ 4 | normalize_volume 5 | from resemblyzer.hparams import sampling_rate 6 | from resemblyzer.voice_encoder import VoiceEncoder 7 | -------------------------------------------------------------------------------- /resemblyzer/audio.py: -------------------------------------------------------------------------------- 1 | from scipy.ndimage.morphology import binary_dilation 2 | from resemblyzer.hparams import * 3 | from pathlib import Path 4 | from typing import Optional, Union 5 | import numpy as np 6 | import webrtcvad 7 | import librosa 8 | import struct 9 | 10 | int16_max = (2 ** 15) - 1 11 | 12 | 13 | def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], source_sr: Optional[int]=None): 14 | """ 15 | Applies preprocessing operations to a waveform either on disk or in memory such that 16 | The waveform will be resampled to match the data hyperparameters. 17 | 18 | :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 19 | just .wav), either the waveform as a numpy array of floats. 20 | :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 21 | preprocessing. After preprocessing, the waveform'speaker sampling rate will match the data 22 | hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 23 | this argument will be ignored. 24 | """ 25 | # Load the wav from disk if needed 26 | if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path): 27 | wav, source_sr = librosa.load(str(fpath_or_wav), sr=None) 28 | else: 29 | wav = fpath_or_wav 30 | 31 | # Resample the wav 32 | if source_sr is not None: 33 | wav = librosa.resample(wav, orig_sr=source_sr, target_sr=sampling_rate) 34 | 35 | # Apply the preprocessing: normalize volume and shorten long silences 36 | wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True) 37 | wav = trim_long_silences(wav) 38 | 39 | return wav 40 | 41 | 42 | def wav_to_mel_spectrogram(wav): 43 | """ 44 | Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform. 45 | Note: this not a log-mel spectrogram. 46 | """ 47 | frames = librosa.feature.melspectrogram( 48 | y=wav, 49 | sr=sampling_rate, 50 | n_fft=int(sampling_rate * mel_window_length / 1000), 51 | hop_length=int(sampling_rate * mel_window_step / 1000), 52 | n_mels=mel_n_channels 53 | ) 54 | return frames.astype(np.float32).T 55 | 56 | 57 | def trim_long_silences(wav): 58 | """ 59 | Ensures that segments without voice in the waveform remain no longer than a 60 | threshold determined by the VAD parameters in params.py. 61 | 62 | :param wav: the raw waveform as a numpy array of floats 63 | :return: the same waveform with silences trimmed away (length <= original wav length) 64 | """ 65 | # Compute the voice detection window size 66 | samples_per_window = (vad_window_length * sampling_rate) // 1000 67 | 68 | # Trim the end of the audio to have a multiple of the window size 69 | wav = wav[:len(wav) - (len(wav) % samples_per_window)] 70 | 71 | # Convert the float waveform to 16-bit mono PCM 72 | pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) 73 | 74 | # Perform voice activation detection 75 | voice_flags = [] 76 | vad = webrtcvad.Vad(mode=3) 77 | for window_start in range(0, len(wav), samples_per_window): 78 | window_end = window_start + samples_per_window 79 | voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], 80 | sample_rate=sampling_rate)) 81 | voice_flags = np.array(voice_flags) 82 | 83 | # Smooth the voice detection with a moving average 84 | def moving_average(array, width): 85 | array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) 86 | ret = np.cumsum(array_padded, dtype=float) 87 | ret[width:] = ret[width:] - ret[:-width] 88 | return ret[width - 1:] / width 89 | 90 | audio_mask = moving_average(voice_flags, vad_moving_average_width) 91 | audio_mask = np.round(audio_mask).astype(bool) 92 | 93 | # Dilate the voiced regions 94 | audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1)) 95 | audio_mask = np.repeat(audio_mask, samples_per_window) 96 | 97 | return wav[audio_mask == True] 98 | 99 | 100 | def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False): 101 | if increase_only and decrease_only: 102 | raise ValueError("Both increase only and decrease only are set") 103 | rms = np.sqrt(np.mean((wav * int16_max) ** 2)) 104 | wave_dBFS = 20 * np.log10(rms / int16_max) 105 | dBFS_change = target_dBFS - wave_dBFS 106 | if dBFS_change < 0 and increase_only or dBFS_change > 0 and decrease_only: 107 | return wav 108 | return wav * (10 ** (dBFS_change / 20)) 109 | -------------------------------------------------------------------------------- /resemblyzer/hparams.py: -------------------------------------------------------------------------------- 1 | 2 | ## Mel-filterbank 3 | mel_window_length = 25 # In milliseconds 4 | mel_window_step = 10 # In milliseconds 5 | mel_n_channels = 40 6 | 7 | 8 | ## Audio 9 | sampling_rate = 16000 10 | # Number of spectrogram frames in a partial utterance 11 | partials_n_frames = 160 # 1600 ms 12 | 13 | 14 | ## Voice Activation Detection 15 | # Window size of the VAD. Must be either 10, 20 or 30 milliseconds. 16 | # This sets the granularity of the VAD. Should not need to be changed. 17 | vad_window_length = 30 # In milliseconds 18 | # Number of frames to average together when performing the moving average smoothing. 19 | # The larger this value, the larger the VAD variations must be to not get smoothed out. 20 | vad_moving_average_width = 8 21 | # Maximum number of consecutive silent frames a segment can have. 22 | vad_max_silence_length = 6 23 | 24 | 25 | ## Audio volume normalization 26 | audio_norm_target_dBFS = -30 27 | 28 | 29 | ## Model parameters 30 | model_hidden_size = 256 31 | model_embedding_size = 256 32 | model_num_layers = 3 33 | 34 | -------------------------------------------------------------------------------- /resemblyzer/pretrained.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/resemble-ai/Resemblyzer/15d828edebe06bc72b9cabc8ef8ca5ab2cb457ce/resemblyzer/pretrained.pt -------------------------------------------------------------------------------- /resemblyzer/voice_encoder.py: -------------------------------------------------------------------------------- 1 | from resemblyzer.hparams import * 2 | from resemblyzer import audio 3 | from pathlib import Path 4 | from typing import Union, List 5 | from torch import nn 6 | from time import perf_counter as timer 7 | import numpy as np 8 | import torch 9 | 10 | 11 | class VoiceEncoder(nn.Module): 12 | def __init__(self, device: Union[str, torch.device]=None, verbose=True, weights_fpath: Union[Path, str]=None): 13 | """ 14 | If None, defaults to cuda if it is available on your machine, otherwise the model will 15 | run on cpu. Outputs are always returned on the cpu, as numpy arrays. 16 | :param weights_fpath: path to ".pt" file path. 17 | If None, defaults to built-in "pretrained.pt" model 18 | """ 19 | super().__init__() 20 | 21 | # Define the network 22 | self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) 23 | self.linear = nn.Linear(model_hidden_size, model_embedding_size) 24 | self.relu = nn.ReLU() 25 | 26 | # Get the target device 27 | if device is None: 28 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 29 | elif isinstance(device, str): 30 | device = torch.device(device) 31 | self.device = device 32 | 33 | # Load the pretrained model'speaker weights 34 | if weights_fpath is None: 35 | weights_fpath = Path(__file__).resolve().parent.joinpath("pretrained.pt") 36 | else: 37 | weights_fpath = Path(weights_fpath) 38 | 39 | if not weights_fpath.exists(): 40 | raise Exception("Couldn't find the voice encoder pretrained model at %s." % 41 | weights_fpath) 42 | start = timer() 43 | checkpoint = torch.load(weights_fpath, map_location="cpu") 44 | self.load_state_dict(checkpoint["model_state"], strict=False) 45 | self.to(device) 46 | 47 | if verbose: 48 | print("Loaded the voice encoder model on %s in %.2f seconds." % 49 | (device.type, timer() - start)) 50 | 51 | def forward(self, mels: torch.FloatTensor): 52 | """ 53 | Computes the embeddings of a batch of utterance spectrograms. 54 | 55 | :param mels: a batch of mel spectrograms of same duration as a float32 tensor of shape 56 | (batch_size, n_frames, n_channels) 57 | :return: the embeddings as a float 32 tensor of shape (batch_size, embedding_size). 58 | Embeddings are positive and L2-normed, thus they lay in the range [0, 1]. 59 | """ 60 | # Pass the input through the LSTM layers and retrieve the final hidden state of the last 61 | # layer. Apply a cutoff to 0 for negative values and L2 normalize the embeddings. 62 | _, (hidden, _) = self.lstm(mels) 63 | embeds_raw = self.relu(self.linear(hidden[-1])) 64 | return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) 65 | 66 | @staticmethod 67 | def compute_partial_slices(n_samples: int, rate, min_coverage): 68 | """ 69 | Computes where to split an utterance waveform and its corresponding mel spectrogram to 70 | obtain partial utterances of each. Both the waveform and the 71 | mel spectrogram slices are returned, so as to make each partial utterance waveform 72 | correspond to its spectrogram. 73 | 74 | The returned ranges may be indexing further than the length of the waveform. It is 75 | recommended that you pad the waveform with zeros up to wav_slices[-1].stop. 76 | 77 | :param n_samples: the number of samples in the waveform 78 | :param rate: how many partial utterances should occur per second. Partial utterances must 79 | cover the span of the entire utterance, thus the rate should not be lower than the inverse 80 | of the duration of a partial utterance. By default, partial utterances are 1.6s long and 81 | the minimum rate is thus 0.625. 82 | :param min_coverage: when reaching the last partial utterance, it may or may not have 83 | enough frames. If at least of are present, 84 | then the last partial utterance will be considered by zero-padding the audio. Otherwise, 85 | it will be discarded. If there aren't enough frames for one partial utterance, 86 | this parameter is ignored so that the function always returns at least one slice. 87 | :return: the waveform slices and mel spectrogram slices as lists of array slices. Index 88 | respectively the waveform and the mel spectrogram with these slices to obtain the partial 89 | utterances. 90 | """ 91 | assert 0 < min_coverage <= 1 92 | 93 | # Compute how many frames separate two partial utterances 94 | samples_per_frame = int((sampling_rate * mel_window_step / 1000)) 95 | n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) 96 | frame_step = int(np.round((sampling_rate / rate) / samples_per_frame)) 97 | assert 0 < frame_step, "The rate is too high" 98 | assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % \ 99 | (sampling_rate / (samples_per_frame * partials_n_frames)) 100 | 101 | # Compute the slices 102 | wav_slices, mel_slices = [], [] 103 | steps = max(1, n_frames - partials_n_frames + frame_step + 1) 104 | for i in range(0, steps, frame_step): 105 | mel_range = np.array([i, i + partials_n_frames]) 106 | wav_range = mel_range * samples_per_frame 107 | mel_slices.append(slice(*mel_range)) 108 | wav_slices.append(slice(*wav_range)) 109 | 110 | # Evaluate whether extra padding is warranted or not 111 | last_wav_range = wav_slices[-1] 112 | coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start) 113 | if coverage < min_coverage and len(mel_slices) > 1: 114 | mel_slices = mel_slices[:-1] 115 | wav_slices = wav_slices[:-1] 116 | 117 | return wav_slices, mel_slices 118 | 119 | def embed_utterance(self, wav: np.ndarray, return_partials=False, rate=1.3, min_coverage=0.75): 120 | """ 121 | Computes an embedding for a single utterance. The utterance is divided in partial 122 | utterances and an embedding is computed for each. The complete utterance embedding is the 123 | L2-normed average embedding of the partial utterances. 124 | 125 | TODO: independent batched version of this function 126 | 127 | :param wav: a preprocessed utterance waveform as a numpy array of float32 128 | :param return_partials: if True, the partial embeddings will also be returned along with 129 | the wav slices corresponding to each partial utterance. 130 | :param rate: how many partial utterances should occur per second. Partial utterances must 131 | cover the span of the entire utterance, thus the rate should not be lower than the inverse 132 | of the duration of a partial utterance. By default, partial utterances are 1.6s long and 133 | the minimum rate is thus 0.625. 134 | :param min_coverage: when reaching the last partial utterance, it may or may not have 135 | enough frames. If at least of are present, 136 | then the last partial utterance will be considered by zero-padding the audio. Otherwise, 137 | it will be discarded. If there aren't enough frames for one partial utterance, 138 | this parameter is ignored so that the function always returns at least one slice. 139 | :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If 140 | is True, the partial utterances as a numpy array of float32 of shape 141 | (n_partials, model_embedding_size) and the wav partials as a list of slices will also be 142 | returned. 143 | """ 144 | # Compute where to split the utterance into partials and pad the waveform with zeros if 145 | # the partial utterances cover a larger range. 146 | wav_slices, mel_slices = self.compute_partial_slices(len(wav), rate, min_coverage) 147 | max_wave_length = wav_slices[-1].stop 148 | if max_wave_length >= len(wav): 149 | wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant") 150 | 151 | # Split the utterance into partials and forward them through the model 152 | mel = audio.wav_to_mel_spectrogram(wav) 153 | mels = np.array([mel[s] for s in mel_slices]) 154 | with torch.no_grad(): 155 | mels = torch.from_numpy(mels).to(self.device) 156 | partial_embeds = self(mels).cpu().numpy() 157 | 158 | # Compute the utterance embedding from the partial embeddings 159 | raw_embed = np.mean(partial_embeds, axis=0) 160 | embed = raw_embed / np.linalg.norm(raw_embed, 2) 161 | 162 | if return_partials: 163 | return embed, partial_embeds, wav_slices 164 | return embed 165 | 166 | def embed_speaker(self, wavs: List[np.ndarray], **kwargs): 167 | """ 168 | Compute the embedding of a collection of wavs (presumably from the same speaker) by 169 | averaging their embedding and L2-normalizing it. 170 | 171 | :param wavs: list of wavs a numpy arrays of float32. 172 | :param kwargs: extra arguments to embed_utterance() 173 | :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). 174 | """ 175 | raw_embed = np.mean([self.embed_utterance(wav, return_partials=False, **kwargs) \ 176 | for wav in wavs], axis=0) 177 | return raw_embed / np.linalg.norm(raw_embed, 2) 178 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | with open("README.md", "r") as f: 4 | long_description = f.read() 5 | 6 | with open("requirements_package.txt", "r") as f: 7 | requirements = f.read().splitlines() 8 | 9 | setup( 10 | name="Resemblyzer", 11 | version="0.1.4", 12 | packages=find_packages(), 13 | package_data={ 14 | "resemblyzer": ["pretrained.pt"] 15 | }, 16 | python_requires=">=3.5", 17 | install_requires=requirements, 18 | author="Corentin Jemine", 19 | author_email="corentin@resemble.ai", 20 | description="Analyze and compare voices with deep learning", 21 | long_description=long_description, 22 | long_description_content_type="text/markdown", 23 | url="https://github.com/resemble-ai/Resemblyzer", 24 | classifiers=[ 25 | "Programming Language :: Python :: 3", 26 | "License :: OSI Approved :: Apache Software License", 27 | "Operating System :: OS Independent", 28 | ], 29 | ) 30 | --------------------------------------------------------------------------------