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so-vits-svc-paul-v2

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Paul Sajna
2023-05-02 02:29:10 -07:00
parent 39292534fb
commit a468975c29
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voice_conversion/so-vits-svc-paul-v2/.all-contributorsrc
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voice_conversion/so-vits-svc-paul-v2/.editorconfig
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voice_conversion/so-vits-svc-paul-v2/.flake8
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[flake8]
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voice_conversion/so-vits-svc-paul-v2/.gitignore
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# Created by .ignore support plugin (hsz.mobi)
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voice_conversion/so-vits-svc-paul-v2/.gitpod.yml
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tasks:
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voice_conversion/so-vits-svc-paul-v2/.pre-commit-config.yaml
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# See https://pre-commit.com for more information
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voice_conversion/so-vits-svc-paul-v2/.readthedocs.yml
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# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
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version: 2
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voice_conversion/so-vits-svc-paul-v2/CHANGELOG.md
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# Changelog
<!--next-version-placeholder-->
## v3.12.1 (2023-04-30)
### Fix
* **utils:** Fix epoch variable name to log in checkpoint save/load functions ([#519](https://github.com/voicepaw/so-vits-svc-fork/issues/519)) ([`0530ea3`](https://github.com/voicepaw/so-vits-svc-fork/commit/0530ea34fa42d9af51c73872b02d6453427c5a00))
### Documentation
* Add Scorpi as a contributor for code ([#532](https://github.com/voicepaw/so-vits-svc-fork/issues/532)) ([`542d3a8`](https://github.com/voicepaw/so-vits-svc-fork/commit/542d3a8382d97064f13c1dcc4ba11107614dec3f))
## v3.12.0 (2023-04-30)
### Feature
* **preprocessing:** Add pre-classify command to manually classify files ([#527](https://github.com/voicepaw/so-vits-svc-fork/issues/527)) ([`7a0319c`](https://github.com/voicepaw/so-vits-svc-fork/commit/7a0319c65f42b0cc54d1d86ae5945d4a356b507a))
## v3.11.2 (2023-04-30)
### Fix
* **train:** Decouple lf0 predictor from speaker embeddings ([#528](https://github.com/voicepaw/so-vits-svc-fork/issues/528)) ([`7ab47f4`](https://github.com/voicepaw/so-vits-svc-fork/commit/7ab47f44e2ec77aa8c9e36b2e322d2dca0f94fb0))
## v3.11.1 (2023-04-30)
### Fix
* Specify encoding to utf-8 in read_text() and write_text() ([#524](https://github.com/voicepaw/so-vits-svc-fork/issues/524)) ([`e947336`](https://github.com/voicepaw/so-vits-svc-fork/commit/e94733678955430f4e0c8ee5a26627077c0ffad9))
### Documentation
* Add Highupech as a contributor for bug ([#526](https://github.com/voicepaw/so-vits-svc-fork/issues/526)) ([`8eedc24`](https://github.com/voicepaw/so-vits-svc-fork/commit/8eedc2439b6987f70c94033c3f375ea330498a64))
* **readme:** Fix typo in README.md ([`1773940`](https://github.com/voicepaw/so-vits-svc-fork/commit/1773940ae4a17a522ebc9fe6c1c70c3e02728341))
* Add acekagami as a contributor for translation ([#486](https://github.com/voicepaw/so-vits-svc-fork/issues/486)) ([`958b9fd`](https://github.com/voicepaw/so-vits-svc-fork/commit/958b9fdf5fd1d527b63ac488ad21db2ce90539aa))
* **readme_zh_cn:** Update README_zh_CN.md ([#477](https://github.com/voicepaw/so-vits-svc-fork/issues/477)) ([`1ccd594`](https://github.com/voicepaw/so-vits-svc-fork/commit/1ccd5941e5f17a273dad681301a287aafb7973d9))
## v3.11.0 (2023-04-23)
### Feature
* **gui:** Configurable output file (#452) ([#453](https://github.com/voicepaw/so-vits-svc-fork/issues/453)) ([`d2e3596`](https://github.com/voicepaw/so-vits-svc-fork/commit/d2e3596d5c0874918712488765e068f4010d62b9))
### Documentation
* Add alexanderkoumis as a contributor for code ([#468](https://github.com/voicepaw/so-vits-svc-fork/issues/468)) ([`5e032a3`](https://github.com/voicepaw/so-vits-svc-fork/commit/5e032a3e1eb36b0a7f99fd440b3e2a82f2345747))
## v3.10.5 (2023-04-22)
### Fix
* **utils:** Fix so-vits-svc style contentvec usage ([#455](https://github.com/voicepaw/so-vits-svc-fork/issues/455)) ([`6d35139`](https://github.com/voicepaw/so-vits-svc-fork/commit/6d351390354b17a2cd004bc9572d7dc1202f236c))
## v3.10.4 (2023-04-21)
### Fix
* **train:** Only save checkpoints on main device ([`1aaaac6`](https://github.com/voicepaw/so-vits-svc-fork/commit/1aaaac6328476249371799b92ced3edcbaac8d18))
### Documentation
* Add sbersier as a contributor for bug ([#437](https://github.com/voicepaw/so-vits-svc-fork/issues/437)) ([`58b936d`](https://github.com/voicepaw/so-vits-svc-fork/commit/58b936d669fbf5156f1ae1381393762994dd7414))
* Add escoolioinglesias as a contributor for video ([#436](https://github.com/voicepaw/so-vits-svc-fork/issues/436)) ([`69f097f`](https://github.com/voicepaw/so-vits-svc-fork/commit/69f097f388447d64b7807cf554a5c310c34b7ef0))
* Add GarrettConway as a contributor for review ([#435](https://github.com/voicepaw/so-vits-svc-fork/issues/435)) ([`c1e4ada`](https://github.com/voicepaw/so-vits-svc-fork/commit/c1e4ada97739bf0b360295335475fef7029fbe49))
* Add BlueAmulet as a contributor for maintenance ([#434](https://github.com/voicepaw/so-vits-svc-fork/issues/434)) ([`514ed84`](https://github.com/voicepaw/so-vits-svc-fork/commit/514ed84ffda901243c1bd6f39677eb020257f11f))
* Add guranon as a contributor for bug, ideas, and code ([#433](https://github.com/voicepaw/so-vits-svc-fork/issues/433)) ([`b9eb3fd`](https://github.com/voicepaw/so-vits-svc-fork/commit/b9eb3fdc350588b9528a74d5b7be8e80b2bfbd51))
* Add ZeroHackz as a contributor for bug, and code ([#432](https://github.com/voicepaw/so-vits-svc-fork/issues/432)) ([`66d5adc`](https://github.com/voicepaw/so-vits-svc-fork/commit/66d5adcf6dbb60fd6b6800162e3e16570a8dac1c))
* Add tybantarnusa as a contributor for bug ([#431](https://github.com/voicepaw/so-vits-svc-fork/issues/431)) ([`e6e57b3`](https://github.com/voicepaw/so-vits-svc-fork/commit/e6e57b3e0d97ac91cadde45d5f080ced873df959))
* Add Blacksingh as a contributor for bug ([#430](https://github.com/voicepaw/so-vits-svc-fork/issues/430)) ([`7bc76ba`](https://github.com/voicepaw/so-vits-svc-fork/commit/7bc76ba9355089ab94fce9231f5dbbdd54e849ee))
* Add escoolioinglesias as a contributor for bug, and userTesting ([#429](https://github.com/voicepaw/so-vits-svc-fork/issues/429)) ([`f00fe6e`](https://github.com/voicepaw/so-vits-svc-fork/commit/f00fe6e15cd12085cd01ae3c2676c195e7924429))
* Add outhipped as a contributor for bug ([#428](https://github.com/voicepaw/so-vits-svc-fork/issues/428)) ([`7497175`](https://github.com/voicepaw/so-vits-svc-fork/commit/74971752821a852154bbfc35c318bb05e7b1169c))
* Add yxlllc as a contributor for ideas, and code ([#427](https://github.com/voicepaw/so-vits-svc-fork/issues/427)) ([`42e35d2`](https://github.com/voicepaw/so-vits-svc-fork/commit/42e35d2a1f83be25e3fb0318e694163b0e936c59))
* Add Lordmau5 as a contributor for ideas, maintenance, and 2 more ([#426](https://github.com/voicepaw/so-vits-svc-fork/issues/426)) ([`352451c`](https://github.com/voicepaw/so-vits-svc-fork/commit/352451ccc9c1e1f800dc7697d5c705c0b9707c96))
* Add tonyco82 as a contributor for bug ([#425](https://github.com/voicepaw/so-vits-svc-fork/issues/425)) ([`036ce90`](https://github.com/voicepaw/so-vits-svc-fork/commit/036ce9052f145cf047434d472f775b563e503946))
* Add 75aosu as a contributor for bug ([#424](https://github.com/voicepaw/so-vits-svc-fork/issues/424)) ([`5afc28b`](https://github.com/voicepaw/so-vits-svc-fork/commit/5afc28bf918e1a62343f445a72487c1d932dc7b4))
* Add hxl9654 as a contributor for bug ([#423](https://github.com/voicepaw/so-vits-svc-fork/issues/423)) ([`0953f1f`](https://github.com/voicepaw/so-vits-svc-fork/commit/0953f1fd0dfbfa557f639eb8d917805f8891d7b0))
* Add DuctTapeGames as a contributor for bug ([#422](https://github.com/voicepaw/so-vits-svc-fork/issues/422)) ([`b0f4d39`](https://github.com/voicepaw/so-vits-svc-fork/commit/b0f4d39371ed2913ad792a46754469eb68c8c72d))
* Add Likkkez as a contributor for bug ([#421](https://github.com/voicepaw/so-vits-svc-fork/issues/421)) ([`4a12109`](https://github.com/voicepaw/so-vits-svc-fork/commit/4a12109b6a0b3cd2741f10d6e9027204603b0f27))
* Add AlonDan as a contributor for bug ([#420](https://github.com/voicepaw/so-vits-svc-fork/issues/420)) ([`662ec4b`](https://github.com/voicepaw/so-vits-svc-fork/commit/662ec4b39816b1a1311d56e3edaca31fb442bb8d))
* Add mmodeusher as a contributor for bug ([#419](https://github.com/voicepaw/so-vits-svc-fork/issues/419)) ([`6a78df9`](https://github.com/voicepaw/so-vits-svc-fork/commit/6a78df97d8191b62a04c9ec48b74cf1f00e47c30))
* Add Meldoner as a contributor for bug ([#418](https://github.com/voicepaw/so-vits-svc-fork/issues/418)) ([`5586bec`](https://github.com/voicepaw/so-vits-svc-fork/commit/5586becd35b456523cec1e1aa8c601cd1039dd1c))
## v3.10.3 (2023-04-19)
### Fix
* **train:** Don't save model when tuning for auto batch size ([`2311a35`](https://github.com/voicepaw/so-vits-svc-fork/commit/2311a35c36315123c87b7f20dde3c4dda723bea3))
## v3.10.2 (2023-04-19)
### Fix
* **train:** Properly stop training after `epochs` has been reached ([`f9bb3d8`](https://github.com/voicepaw/so-vits-svc-fork/commit/f9bb3d86605321288f11387bc853143378c3284e))
## v3.10.1 (2023-04-19)
### Fix
* **train:** Support ddp in Windows (gloo backend) ([#394](https://github.com/34j/so-vits-svc-fork/issues/394)) ([`bcb0507`](https://github.com/34j/so-vits-svc-fork/commit/bcb05078d8ca7a6ac681de919552b3a190b2cd9b))
## v3.10.0 (2023-04-18)
### Feature
* Replace `fairseq` with `transformers` ([#385](https://github.com/34j/so-vits-svc-fork/issues/385)) ([`a2fe0f3`](https://github.com/34j/so-vits-svc-fork/commit/a2fe0f376d33f02987c91a57bd90a794de90a0e1))
## v3.9.5 (2023-04-18)
### Fix
* **train:** Set persistent_workers = True in DataLoader for performance, do not save checkpoints, fix logging issue and multiple warning issues, do not do validation when global_step == 0 ([#384](https://github.com/34j/so-vits-svc-fork/issues/384)) ([`6cab9af`](https://github.com/34j/so-vits-svc-fork/commit/6cab9af86e3a96e79243fa890eb1c6c51fae4476))
## v3.9.4 (2023-04-18)
### Fix
* **gui:** Always use "spawn" context in ProcessPool ([#383](https://github.com/34j/so-vits-svc-fork/issues/383)) ([`5d7fb77`](https://github.com/34j/so-vits-svc-fork/commit/5d7fb774e8d5e97a9a31dbc891892e9f934f3884))
## v3.9.3 (2023-04-16)
### Fix
* **gui:** Fix subprocess errors in linux and fix wrong error logging ([#358](https://github.com/34j/so-vits-svc-fork/issues/358)) ([`fd67db6`](https://github.com/34j/so-vits-svc-fork/commit/fd67db6312944557c09afd7b1ccbb97987a03489))
## v3.9.2 (2023-04-16)
### Fix
* **train:** Fix y_mel length ([#356](https://github.com/34j/so-vits-svc-fork/issues/356)) ([`2d71992`](https://github.com/34j/so-vits-svc-fork/commit/2d71992d80ba4142d2d5a5df17c69c2f2ac553fd))
## v3.9.1 (2023-04-16)
### Fix
* **train:** Allow higher segment size ([#351](https://github.com/34j/so-vits-svc-fork/issues/351)) ([`09d5a52`](https://github.com/34j/so-vits-svc-fork/commit/09d5a52b9bfc8eba8857f2b6c804ecdb39b4b38b))
* **cluster:** Do not use weights_only in get_cluster_model() ([#354](https://github.com/34j/so-vits-svc-fork/issues/354)) ([`24c05d1`](https://github.com/34j/so-vits-svc-fork/commit/24c05d16c3b55f664699400496a7e0fd2fd84353))
## v3.9.0 (2023-04-16)
### Feature
* **train:** Add option to name ckpts by epochs ([#353](https://github.com/34j/so-vits-svc-fork/issues/353)) ([`bba24c4`](https://github.com/34j/so-vits-svc-fork/commit/bba24c4a62b935ed29572aa2c2c437d1b54aa2e2))
## v3.8.1 (2023-04-16)
### Fix
* Patch stft and add mps to get_optimal_device() ([#352](https://github.com/34j/so-vits-svc-fork/issues/352)) ([`da928aa`](https://github.com/34j/so-vits-svc-fork/commit/da928aa0bb1399bf5780526f8a7e9b674476a000))
## v3.8.0 (2023-04-15)
### Feature
* **train:** Automatically decide batch_size ([#342](https://github.com/34j/so-vits-svc-fork/issues/342)) ([`8ffa128`](https://github.com/34j/so-vits-svc-fork/commit/8ffa128aa209787fde8fb1f0e4ae5c96dfe31217))
## v3.7.3 (2023-04-15)
### Fix
* **gui:** Show errors raised in inference ([#341](https://github.com/34j/so-vits-svc-fork/issues/341)) ([`99833c5`](https://github.com/34j/so-vits-svc-fork/commit/99833c55045647b9a766042765b454cb3d7d18ce))
## v3.7.2 (2023-04-15)
### Fix
* **train:** Suppress pytorch logs for deprecated TypedStorage ([#336](https://github.com/34j/so-vits-svc-fork/issues/336)) ([`e67ac62`](https://github.com/34j/so-vits-svc-fork/commit/e67ac621296cf6667d05b51f23ce8cb9ef8a0855))
## v3.7.1 (2023-04-15)
### Fix
* **train:** Fix check for notebook / Colab ([#335](https://github.com/34j/so-vits-svc-fork/issues/335)) ([`7f69814`](https://github.com/34j/so-vits-svc-fork/commit/7f698141e1b65e901579a5dbbabf28bfae5cc91f))
## v3.7.0 (2023-04-14)
### Feature
* **train:** Add option to specify TensorBoardLogger version parameter support ([#331](https://github.com/34j/so-vits-svc-fork/issues/331)) ([`a685123`](https://github.com/34j/so-vits-svc-fork/commit/a685123a4063e08e0b021a1ad51098d3154b75de))
## v3.6.2 (2023-04-14)
### Fix
* Fix torch.load and save to use file objects and weights_only and remove unidecode ([#327](https://github.com/34j/so-vits-svc-fork/issues/327)) ([`4aad701`](https://github.com/34j/so-vits-svc-fork/commit/4aad701badc1eae5195e874dec40f9ed8dd40ee6))
## v3.6.1 (2023-04-14)
### Fix
* **train:** Fix gradient logging ([#320](https://github.com/34j/so-vits-svc-fork/issues/320)) ([`73ef3dc`](https://github.com/34j/so-vits-svc-fork/commit/73ef3dc94ccd4c0514ab33b0c5a65edf8b356484))
## v3.6.0 (2023-04-13)
### Feature
* **realtime:** Support sola algorithm ([#316](https://github.com/34j/so-vits-svc-fork/issues/316)) ([`0fcbf99`](https://github.com/34j/so-vits-svc-fork/commit/0fcbf9979862e945ca2427612a92549db2d627d0))
## v3.5.1 (2023-04-13)
### Fix
* **logger:** Do not use rich in notebook ([#317](https://github.com/34j/so-vits-svc-fork/issues/317)) ([`03c8240`](https://github.com/34j/so-vits-svc-fork/commit/03c824015872e3d7e4e5795b9d65fad4116d54e4))
## v3.5.0 (2023-04-13)
### Feature
* **gui:** Run inference in thread and disable button ([#279](https://github.com/34j/so-vits-svc-fork/issues/279)) ([`c55caa8`](https://github.com/34j/so-vits-svc-fork/commit/c55caa8019cc06fc6bd8851b0fd895b73cf926a4))
## v3.4.0 (2023-04-13)
### Feature
* **train:** Make num_workers configurable ([#315](https://github.com/34j/so-vits-svc-fork/issues/315)) ([`e8df714`](https://github.com/34j/so-vits-svc-fork/commit/e8df7146b0d1d3ee32af576c251f47d8fdd80bb3))
## v3.3.1 (2023-04-13)
### Performance
* **train:** Specify num_workers in DataLoader ([#313](https://github.com/34j/so-vits-svc-fork/issues/313)) ([`6042164`](https://github.com/34j/so-vits-svc-fork/commit/6042164a60f9990eb0636e37dd650bb0cdff032b))
## v3.3.0 (2023-04-13)
### Feature
* **train:** Use RichProgressBar ([#311](https://github.com/34j/so-vits-svc-fork/issues/311)) ([`17e937a`](https://github.com/34j/so-vits-svc-fork/commit/17e937aae9c90b513e4b7674f442a60161c84e83))
## v3.2.0 (2023-04-13)
### Feature
* **train:** Add optional `accumulate_grad_batches` config param ([#306](https://github.com/34j/so-vits-svc-fork/issues/306)) ([`1172b23`](https://github.com/34j/so-vits-svc-fork/commit/1172b2385cfe5239da3222cf93916436395e0f1a))
## v3.1.13 (2023-04-12)
### Fix
* **logger:** Fix too noisy logger ([#300](https://github.com/34j/so-vits-svc-fork/issues/300)) ([`bd0eb33`](https://github.com/34j/so-vits-svc-fork/commit/bd0eb33a66d77afff8328d08008f2643651c712a))
* **__main__:** Fix cli() not called in __main__ ([#299](https://github.com/34j/so-vits-svc-fork/issues/299)) ([`11f2d24`](https://github.com/34j/so-vits-svc-fork/commit/11f2d245137da240f5e8214e4b6ce4330d726143))
## v3.1.12 (2023-04-12)
### Fix
* **train:** Fix ddp not working ([#297](https://github.com/34j/so-vits-svc-fork/issues/297)) ([`bec43fc`](https://github.com/34j/so-vits-svc-fork/commit/bec43fcbedf6b16260411655b19cf780ddbafe8e))
## v3.1.11 (2023-04-12)
### Fix
* **logger:** Fix init_logger not showing debug messages in certain conditions as intended ([#296](https://github.com/34j/so-vits-svc-fork/issues/296)) ([`d3ab7d6`](https://github.com/34j/so-vits-svc-fork/commit/d3ab7d667c391ba1d8d1b34e2b66992256b3989d))
## v3.1.10 (2023-04-11)
### Fix
* **notebooks:** Improves inference ([#286](https://github.com/34j/so-vits-svc-fork/issues/286)) ([`d3228df`](https://github.com/34j/so-vits-svc-fork/commit/d3228df704b2e6a0746b3f842ca5f2240890d829))
* **train:** Step lr schedulers at end of epoch ([#291](https://github.com/34j/so-vits-svc-fork/issues/291)) ([`3af223e`](https://github.com/34j/so-vits-svc-fork/commit/3af223eeb5146abcbb8198d4c11e2c1895ece130))
## v3.1.9 (2023-04-10)
### Fix
* **train:** Fix fp16_run not being mix precision and fix bf16 errors ([#283](https://github.com/34j/so-vits-svc-fork/issues/283)) ([`b0dd0ed`](https://github.com/34j/so-vits-svc-fork/commit/b0dd0ed4014d32e9f19e335ec603bdab92c52039))
## v3.1.8 (2023-04-10)
### Fix
* **readme:** Fix wrong commands in "Before training" ([#280](https://github.com/34j/so-vits-svc-fork/issues/280)) ([`e056ad9`](https://github.com/34j/so-vits-svc-fork/commit/e056ad9ec22cbaa119f7c93cb60b5b8851e80a7e))
## v3.1.7 (2023-04-09)
### Fix
* **train:** Improve quality of training ([#274](https://github.com/34j/so-vits-svc-fork/issues/274)) ([`7ed71d6`](https://github.com/34j/so-vits-svc-fork/commit/7ed71d6fd89ca8bf2c4aefbb280e705b1d7ae6b8))
## v3.1.6 (2023-04-09)
### Fix
* **train:** Fix checkpoint not properly loaded ([#271](https://github.com/34j/so-vits-svc-fork/issues/271)) ([`0979147`](https://github.com/34j/so-vits-svc-fork/commit/0979147a234e08999a19dba4988a53886f61dade))
## v3.1.5 (2023-04-09)
### Fix
* **train:** Fix optim_d functions called in wrong order ([#269](https://github.com/34j/so-vits-svc-fork/issues/269)) ([`13d6346`](https://github.com/34j/so-vits-svc-fork/commit/13d63469b0a84ace0dc8848df47dc20538b98770))
## v3.1.4 (2023-04-09)
### Fix
* **train:** Add bf16 and fp16 support ([#268](https://github.com/34j/so-vits-svc-fork/issues/268)) ([`4229fd8`](https://github.com/34j/so-vits-svc-fork/commit/4229fd8ead64cf03caad9acd3d8f7f0fec3a7fee))
## v3.1.3 (2023-04-09)
### Fix
* **deps:** Update dependency starlette to v0.26.1 ([#256](https://github.com/34j/so-vits-svc-fork/issues/256)) ([`5eb574b`](https://github.com/34j/so-vits-svc-fork/commit/5eb574bec01430399df48e90e6112cef85e21945))
## v3.1.2 (2023-04-09)
### Fix
* Remove wrong test and trigger release ([#266](https://github.com/34j/so-vits-svc-fork/issues/266)) ([`9ea77e4`](https://github.com/34j/so-vits-svc-fork/commit/9ea77e4c5c6575844685998e237994d54be84bb9))
* **deps:** Remove pydantic constraints ([#263](https://github.com/34j/so-vits-svc-fork/issues/263)) ([`f446e3b`](https://github.com/34j/so-vits-svc-fork/commit/f446e3bbd62205b9c847e9ecdc46f519417b572a))
* Fix fastapi version to 0.88 ([#261](https://github.com/34j/so-vits-svc-fork/issues/261)) ([`a26f387`](https://github.com/34j/so-vits-svc-fork/commit/a26f387abea585c300cd1ed0c36c6b9afc731764))
* **utils:** Fix get_optimal_device ([#262](https://github.com/34j/so-vits-svc-fork/issues/262)) ([`79e4b5a`](https://github.com/34j/so-vits-svc-fork/commit/79e4b5a0abe20789335eaaf4a359880c099aaa35))
## v3.1.1 (2023-04-08)
### Fix
* **deps:** Update dependency fastapi to <0.96 ([#255](https://github.com/34j/so-vits-svc-fork/issues/255)) ([`29c8cc0`](https://github.com/34j/so-vits-svc-fork/commit/29c8cc05b7e5180058e03f2dc1f681e58cc67f09))
## v3.1.0 (2023-04-08)
### Feature
* Migrate to lightning ([#246](https://github.com/34j/so-vits-svc-fork/issues/246)) ([`824ecbd`](https://github.com/34j/so-vits-svc-fork/commit/824ecbd7222b9b9ada77c4fbbd7ae7f491049f21))
## v3.0.5 (2023-04-08)
### Fix
* **cluster:** Fix train_cluster ([#250](https://github.com/34j/so-vits-svc-fork/issues/250)) ([`b0c93e4`](https://github.com/34j/so-vits-svc-fork/commit/b0c93e49f9cdfdcd714575fc27011bf56ce4493d))
## v3.0.4 (2023-04-06)
### Fix
* **__main__:** Fix default config type to revert breaking changes ([#243](https://github.com/34j/so-vits-svc-fork/issues/243)) ([`e05c0b5`](https://github.com/34j/so-vits-svc-fork/commit/e05c0b52b6affac5e4483c0938e04584e1bd8d98))
## v3.0.3 (2023-04-05)
### Fix
* Fix issues when loading legacy checkpoint and fix pre-hubert n_jobs ([#236](https://github.com/34j/so-vits-svc-fork/issues/236)) ([`15f1e7f`](https://github.com/34j/so-vits-svc-fork/commit/15f1e7ffca80cb551316affae546ea72e8cccb34))
## v3.0.2 (2023-04-04)
### Performance
* Move methods from dataloader to pre-hubert ([#218](https://github.com/34j/so-vits-svc-fork/issues/218)) ([`d5a4456`](https://github.com/34j/so-vits-svc-fork/commit/d5a4456ebd5b6659ca037ee2f43480a00d7915f6))
## v3.0.1 (2023-04-03)
### Fix
* Remove possible leak in unused code ([#203](https://github.com/34j/so-vits-svc-fork/issues/203)) ([`e921c3d`](https://github.com/34j/so-vits-svc-fork/commit/e921c3dc018ea783b4c26375a04f499a45ad9df0))
### Performance
* **utils:** Better implementation of repeat_expand_2d ([#216](https://github.com/34j/so-vits-svc-fork/issues/216)) ([`ef30a9d`](https://github.com/34j/so-vits-svc-fork/commit/ef30a9d5ae60fdde5f6b44d6cea8cee0a40dd3e9))
## v3.0.0 (2023-04-03)
### Feature
* Add quickvc, fix usage of ContentVec, remove onnx support ([#197](https://github.com/34j/so-vits-svc-fork/issues/197)) ([`1a6c021`](https://github.com/34j/so-vits-svc-fork/commit/1a6c021cd102b48b44e006decebc165062df8a95))
### Breaking
* The module structure has been completely changed, and some of the default values for configurations have also been changed. ([`1a6c021`](https://github.com/34j/so-vits-svc-fork/commit/1a6c021cd102b48b44e006decebc165062df8a95))
### Documentation
* **readme:** Update allcontributors link for @MashiroSA ([#210](https://github.com/34j/so-vits-svc-fork/issues/210)) ([`650524b`](https://github.com/34j/so-vits-svc-fork/commit/650524bb37997326e924814632c6202b76660f77))
* **readme:** Add paperspace referral ([#209](https://github.com/34j/so-vits-svc-fork/issues/209)) ([`7280012`](https://github.com/34j/so-vits-svc-fork/commit/7280012df66b5ea71291e5a80bb22451f0ca236e))
* **readme:** Add paperspace link and add more description, add a link for zh-CN docs ([#208](https://github.com/34j/so-vits-svc-fork/issues/208)) ([`bc4b122`](https://github.com/34j/so-vits-svc-fork/commit/bc4b1229e4ad9c046fda38334c4c6d22548356c2))
## v2.1.5 (2023-04-01)
### Fix
* **deps:** Update dependency tensorboard to v2.12.1 ([#199](https://github.com/34j/so-vits-svc-fork/issues/199)) ([`0ccda1c`](https://github.com/34j/so-vits-svc-fork/commit/0ccda1ccb34b8125abe369f738b06de7b77c8efc))
## v2.1.4 (2023-03-31)
### Fix
* **deps:** Update dependency gradio to v3.24.1 ([#196](https://github.com/34j/so-vits-svc-fork/issues/196)) ([`4fa141b`](https://github.com/34j/so-vits-svc-fork/commit/4fa141b210cb9b80bc7f75176fb01b18352c91cd))
## v2.1.3 (2023-03-31)
### Fix
* **deps:** Update dependency gradio to v3.24.0 ([#192](https://github.com/34j/so-vits-svc-fork/issues/192)) ([`4e441cb`](https://github.com/34j/so-vits-svc-fork/commit/4e441cb30429e4a47afd261d69e32ec5f86564c9))
### Documentation
* Add sbersier as a contributor for ideas, and userTesting ([#185](https://github.com/34j/so-vits-svc-fork/issues/185)) ([`a655bf4`](https://github.com/34j/so-vits-svc-fork/commit/a655bf47dde4ad2506283997987bce3a09229c57))
* Add ColdCawfee as a contributor for bug ([#177](https://github.com/34j/so-vits-svc-fork/issues/177)) ([`87a09e6`](https://github.com/34j/so-vits-svc-fork/commit/87a09e654a0e8f064293750779b743abf2897ebb))
## v2.1.2 (2023-03-28)
### Fix
* **gui:** Fix wrong devices set as default ([#176](https://github.com/34j/so-vits-svc-fork/issues/176)) ([`6265f8f`](https://github.com/34j/so-vits-svc-fork/commit/6265f8f93e8facd4f58aab906bfcb23e05d4032b))
* **__main__:** Fix -h option overridden ([#175](https://github.com/34j/so-vits-svc-fork/issues/175)) ([`52f1cfe`](https://github.com/34j/so-vits-svc-fork/commit/52f1cfe1f08bd63966b0d1d7c025abed17cb36a6))
### Documentation
* Add xieyumc as a contributor for doc ([#174](https://github.com/34j/so-vits-svc-fork/issues/174)) ([`29474d9`](https://github.com/34j/so-vits-svc-fork/commit/29474d9dc77555fe5a55427278d44dfea7ece5ef))
* **readme_zh_cn:** Update README_zh_CN.md ([#173](https://github.com/34j/so-vits-svc-fork/issues/173)) ([`f94a14c`](https://github.com/34j/so-vits-svc-fork/commit/f94a14cb63e2afd40cba3e94f84077643d9a7560))
## v2.1.1 (2023-03-27)
### Fix
* **deps:** Update dependency rich to v13.3.3 ([#171](https://github.com/34j/so-vits-svc-fork/issues/171)) ([`8bdefa9`](https://github.com/34j/so-vits-svc-fork/commit/8bdefa9636e13fb0a24058a589675a20655357f4))
### Documentation
* Add nerdyrodent as a contributor for video ([#168](https://github.com/34j/so-vits-svc-fork/issues/168)) ([`78ab661`](https://github.com/34j/so-vits-svc-fork/commit/78ab661af198d87ce2ca5525fa262c639ed03cdc))
* Add heyfixit as a contributor for doc ([#167](https://github.com/34j/so-vits-svc-fork/issues/167)) ([`32a2a63`](https://github.com/34j/so-vits-svc-fork/commit/32a2a63b375300be6d67be56035005956003bdfd))
* Add Desuka-art as a contributor for bug ([#165](https://github.com/34j/so-vits-svc-fork/issues/165)) ([`fe3c6bf`](https://github.com/34j/so-vits-svc-fork/commit/fe3c6bf8270fc219cdaeef05b7deacdbfc4df313))
* Add ruckusmattster as a contributor for bug ([#163](https://github.com/34j/so-vits-svc-fork/issues/163)) ([`2b971db`](https://github.com/34j/so-vits-svc-fork/commit/2b971db5c7a332c8321e99bd77bb956a0ee3ec88))
* Add pierluigizagaria as a contributor for userTesting ([#160](https://github.com/34j/so-vits-svc-fork/issues/160)) ([`6fabe8d`](https://github.com/34j/so-vits-svc-fork/commit/6fabe8d10b684caa236331a157455db1da686f8f))
* Add Satisfy256 as a contributor for bug ([#158](https://github.com/34j/so-vits-svc-fork/issues/158)) ([`ee72aee`](https://github.com/34j/so-vits-svc-fork/commit/ee72aee12f23fee458599b8b7fa4f0ed27d33b1c))
* Add DL909 as a contributor for bug ([#152](https://github.com/34j/so-vits-svc-fork/issues/152)) ([`a5e6651`](https://github.com/34j/so-vits-svc-fork/commit/a5e6651a8f537961caf53adbb8bc52c1412c0762))
## v2.1.0 (2023-03-27)
### Feature
* **__main__:** Add an option to launch tensorboard in `train` command ([#149](https://github.com/34j/so-vits-svc-fork/issues/149)) ([`ef22cce`](https://github.com/34j/so-vits-svc-fork/commit/ef22cceaeb7f06ea53b2151ef9c962d1040de20d))
## v2.0.0 (2023-03-27)
### Fix
* Fix preprocessing and convert bool options to flags, use `unidecode` to decode non-ASCII filenames in `pre-resample` ([#147](https://github.com/34j/so-vits-svc-fork/issues/147)) ([`98d7ee2`](https://github.com/34j/so-vits-svc-fork/commit/98d7ee22a40104468285324cc6ec21c707c30d54))
### Breaking
* Preprocessing default parameters changed and some options became flags. ([`98d7ee2`](https://github.com/34j/so-vits-svc-fork/commit/98d7ee22a40104468285324cc6ec21c707c30d54))
### Documentation
* **readme:** Add yt tutorial vid link ([#143](https://github.com/34j/so-vits-svc-fork/issues/143)) ([`1694f44`](https://github.com/34j/so-vits-svc-fork/commit/1694f449e5a9f7b9da71e9a4c2764830c5268de3))
## v1.4.3 (2023-03-26)
### Performance
* **preprocess:** Specify samplerate to reduce memory usage ([#137](https://github.com/34j/so-vits-svc-fork/issues/137)) ([`6217eda`](https://github.com/34j/so-vits-svc-fork/commit/6217eda0ec3bac27e408fcd0466a6b658cf718c5))
## v1.4.2 (2023-03-26)
### Fix
* **logging:** Initialize logging in logger file and move version log ([#131](https://github.com/34j/so-vits-svc-fork/issues/131)) ([`441d51f`](https://github.com/34j/so-vits-svc-fork/commit/441d51f8efa84144d8a9f8fa02f2adaaf15295c0))
* **preprocess:** Fix dtype in sf.read() to save memory and fix preprocess_resample ([#132](https://github.com/34j/so-vits-svc-fork/issues/132)) ([`0af1e13`](https://github.com/34j/so-vits-svc-fork/commit/0af1e13a468ad282266a595b8d3c77d62aa938dc))
* Fix audio resampled to 22kHz ([#130](https://github.com/34j/so-vits-svc-fork/issues/130)) ([`4203f37`](https://github.com/34j/so-vits-svc-fork/commit/4203f374c5625369518063888e1ca70d1af4f694))
### Documentation
* Update notebook and README.md ([#128](https://github.com/34j/so-vits-svc-fork/issues/128)) ([`38d9744`](https://github.com/34j/so-vits-svc-fork/commit/38d97449d5b443167926f409f904f4b40c6e0f03))
## v1.4.1 (2023-03-26)
### Fix
* **__main__,gui:** Fix some parameters not passed ([#124](https://github.com/34j/so-vits-svc-fork/issues/124)) ([`6cfe3d3`](https://github.com/34j/so-vits-svc-fork/commit/6cfe3d3f567c03e1c59065ff827f564a13a7aaaf))
## v1.4.0 (2023-03-26)
### Feature
* **preprocessing:** Add 2 more preprocessing commands ([#123](https://github.com/34j/so-vits-svc-fork/issues/123)) ([`45eba0f`](https://github.com/34j/so-vits-svc-fork/commit/45eba0f25db1346757fcd9134ccb3a62125a05a9))
### Documentation
* Add BlueAmulet as a contributor for code ([#119](https://github.com/34j/so-vits-svc-fork/issues/119)) ([`6a7e8ba`](https://github.com/34j/so-vits-svc-fork/commit/6a7e8ba827ee69f1ceca60b83dfbae437bbe6667))
## v1.3.5 (2023-03-26)
### Fix
* **data_utils:** Allow float32 audio to be processed properly ([#115](https://github.com/34j/so-vits-svc-fork/issues/115)) ([`13943b6`](https://github.com/34j/so-vits-svc-fork/commit/13943b693d177cf5417127647a3280a9e5ff9ca5))
## v1.3.4 (2023-03-25)
### Fix
* Change default f0 method from crepe to dio ([#100](https://github.com/34j/so-vits-svc-fork/issues/100)) ([`baf58d2`](https://github.com/34j/so-vits-svc-fork/commit/baf58d286c286c0064fd015e0e8f0b9e690021f7))
## v1.3.3 (2023-03-25)
### Fix
* Fix old checkpoint deletion by sorting the models properly (#65) ([#89](https://github.com/34j/so-vits-svc-fork/issues/89)) ([`287dc94`](https://github.com/34j/so-vits-svc-fork/commit/287dc94be719147023af0ecfe7e92b16a8e98fc5))
### Documentation
* Add Lordmau5 as a contributor for bug, and code ([#102](https://github.com/34j/so-vits-svc-fork/issues/102)) ([`4df46ee`](https://github.com/34j/so-vits-svc-fork/commit/4df46eed47378f41d76c7637f540779db3bb54a3))
## v1.3.2 (2023-03-24)
### Fix
* **gui:** Fix devices list and fix tqdm error in gui ([#99](https://github.com/34j/so-vits-svc-fork/issues/99)) ([`59724cd`](https://github.com/34j/so-vits-svc-fork/commit/59724cd2afc6a8d5ef6ea4b7fa8c012e21fc4af6))
### Documentation
* Add MashiroSA as a contributor for doc, and bug ([#98](https://github.com/34j/so-vits-svc-fork/issues/98)) ([`495b7cb`](https://github.com/34j/so-vits-svc-fork/commit/495b7cbfc9f9468d49bc3f57efe6c5c076dcb0d3))
* **notebook:** Fix cluster inference command and improve cluster training command ([#93](https://github.com/34j/so-vits-svc-fork/issues/93)) ([`7642594`](https://github.com/34j/so-vits-svc-fork/commit/7642594472bd660fe046c45909f0475398af199e))
## v1.3.1 (2023-03-24)
### Fix
* **gui:** Fix defaut for auto_play ([#97](https://github.com/34j/so-vits-svc-fork/issues/97)) ([`07920a4`](https://github.com/34j/so-vits-svc-fork/commit/07920a4954e1a14d47fcb2687f050d49d03da415))
* **infer_tool:** Fix speaker not automaticlly set to the first one if not found in cluster inference ([#96](https://github.com/34j/so-vits-svc-fork/issues/96)) ([`a643e4f`](https://github.com/34j/so-vits-svc-fork/commit/a643e4f26b59f12f00b316467edad876467dad49))
### Documentation
* **notebook:** Add cluster training and inference ([#84](https://github.com/34j/so-vits-svc-fork/issues/84)) ([`9ffb621`](https://github.com/34j/so-vits-svc-fork/commit/9ffb6216f418d8c5a4a9f1bdd79fc2cebb885db1))
## v1.3.0 (2023-03-23)
### Feature
* **gui:** Better error handling ([#85](https://github.com/34j/so-vits-svc-fork/issues/85)) ([`985704b`](https://github.com/34j/so-vits-svc-fork/commit/985704b1afa8af15fe8eab5e3fc838465f5162c8))
## v1.2.11 (2023-03-23)
### Fix
* **gui,onnx:** Fix onnx export and fix gui ([#83](https://github.com/34j/so-vits-svc-fork/issues/83)) ([`3e9a47d`](https://github.com/34j/so-vits-svc-fork/commit/3e9a47dd4faa938a6aaebf2d7c1c0b9d68cc97d3))
## v1.2.10 (2023-03-23)
### Fix
* **cluster:** Fix cluster not working ([#82](https://github.com/34j/so-vits-svc-fork/issues/82)) ([`29b209c`](https://github.com/34j/so-vits-svc-fork/commit/29b209cf7060deb7f15ae28fe2e520bb20a236f4))
## v1.2.9 (2023-03-23)
### Fix
* **gui:** Fix speakers and devices not updated and fix default presets ([#80](https://github.com/34j/so-vits-svc-fork/issues/80)) ([`a851150`](https://github.com/34j/so-vits-svc-fork/commit/a8511508b0d2b3a62e7b77833280e4264997d9ed))
## v1.2.8 (2023-03-22)
### Fix
* **deps:** Update dependency torchcrepe to v0.0.18 ([#76](https://github.com/34j/so-vits-svc-fork/issues/76)) ([`4fda479`](https://github.com/34j/so-vits-svc-fork/commit/4fda4799f017e7de57de36c95cd8d64ab6f9b446))
### Documentation
* **readme:** Shorten docs ([#75](https://github.com/34j/so-vits-svc-fork/issues/75)) ([`e0c1572`](https://github.com/34j/so-vits-svc-fork/commit/e0c1572d057032735c3118e9137be8e4399c6251))
## v1.2.7 (2023-03-22)
### Fix
* Fix clean_checkpoints ([#66](https://github.com/34j/so-vits-svc-fork/issues/66)) ([`e5169bf`](https://github.com/34j/so-vits-svc-fork/commit/e5169bf8121578a6cc3ed1bccd1b47a6281cafe4))
## v1.2.6 (2023-03-22)
### Fix
* **gui:** Disable checkbox if CUDA is not available and show errors for VC ([#71](https://github.com/34j/so-vits-svc-fork/issues/71)) ([`3fdd983`](https://github.com/34j/so-vits-svc-fork/commit/3fdd9836c3b60d2e737fc7e40efe42a9cc84888e))
### Documentation
* Add BlueAmulet as a contributor for question ([#74](https://github.com/34j/so-vits-svc-fork/issues/74)) ([`8d073e3`](https://github.com/34j/so-vits-svc-fork/commit/8d073e3e0798a0739cea5b979cf6cfd361f3e6d3))
* Add GarrettConway as a contributor for doc ([#73](https://github.com/34j/so-vits-svc-fork/issues/73)) ([`6c6cbc6`](https://github.com/34j/so-vits-svc-fork/commit/6c6cbc6ac8a97ecb71d789a5782bb8db2c4c52f8))
* **readme:** Update README.md regarding installation, update. WSL audio support ([#72](https://github.com/34j/so-vits-svc-fork/issues/72)) ([`4f1323b`](https://github.com/34j/so-vits-svc-fork/commit/4f1323b3d12a080f38a195bf494db7086dbfa7e4))
## v1.2.5 (2023-03-22)
### Fix
* Fix RTF calculation ([#70](https://github.com/34j/so-vits-svc-fork/issues/70)) ([`fb25500`](https://github.com/34j/so-vits-svc-fork/commit/fb25500f4e3e70e5d71462715b83fb3bedcf8bd5))
## v1.2.4 (2023-03-22)
### Fix
* **utils:** Fix latest_checkpoint_path ([#64](https://github.com/34j/so-vits-svc-fork/issues/64)) ([`00b9f4a`](https://github.com/34j/so-vits-svc-fork/commit/00b9f4acd005cdb801b3f41df6e25b0b8799d631))
## v1.2.3 (2023-03-21)
### Fix
* **deps:** Update dependency onnxsim to v0.4.19 ([#62](https://github.com/34j/so-vits-svc-fork/issues/62)) ([`f8a4cf6`](https://github.com/34j/so-vits-svc-fork/commit/f8a4cf61bad5d0d55a7334af8f022114605e7038))
## v1.2.2 (2023-03-21)
### Fix
* **deps:** Update dependency onnxoptimizer to v0.3.10 ([#61](https://github.com/34j/so-vits-svc-fork/issues/61)) ([`d0137f9`](https://github.com/34j/so-vits-svc-fork/commit/d0137f920083a08173d58e35492b9b9fb925e41f))
### Documentation
* **readme:** Add links for pretrained models and fix GUI pic height ([#60](https://github.com/34j/so-vits-svc-fork/issues/60)) ([`34ac39f`](https://github.com/34j/so-vits-svc-fork/commit/34ac39f0c9ce89f2effdd18f3fc4ab91e72b3f82))
* **notebook:** Add more explanation to notebook ([#59](https://github.com/34j/so-vits-svc-fork/issues/59)) ([`9b3c483`](https://github.com/34j/so-vits-svc-fork/commit/9b3c4835e063d26d1e66d172cf592e69e30d59b8))
## v1.2.1 (2023-03-21)
### Fix
* Use librosa.load() instead of soundfile.read() ([#58](https://github.com/34j/so-vits-svc-fork/issues/58)) ([`b343106`](https://github.com/34j/so-vits-svc-fork/commit/b34310662b2bac53884df396932f72366132ea01))
* **gui:** Fix window too big to show in a FHD environment ([#57](https://github.com/34j/so-vits-svc-fork/issues/57)) ([`259e6e6`](https://github.com/34j/so-vits-svc-fork/commit/259e6e6eb6ebfd9027b1813756d67d1a516e0214))
## v1.2.0 (2023-03-21)
### Feature
* Add presets ([#55](https://github.com/34j/so-vits-svc-fork/issues/55)) ([`e8adcc6`](https://github.com/34j/so-vits-svc-fork/commit/e8adcc621f6caf5f4b20846575b3559c032ed47f))
## v1.1.1 (2023-03-21)
### Fix
* **deps:** Update dependency gradio to v3.23.0 ([#54](https://github.com/34j/so-vits-svc-fork/issues/54)) ([`a2bdb48`](https://github.com/34j/so-vits-svc-fork/commit/a2bdb48b436d206b30bb72409852c0b30d6811e9))
## v1.1.0 (2023-03-21)
### Feature
* Enhance RealtimeVC ([#52](https://github.com/34j/so-vits-svc-fork/issues/52)) ([`81551ce`](https://github.com/34j/so-vits-svc-fork/commit/81551ce9c6fb7924d184c3c5a4cf9035168b28d2))
### Documentation
* Update gui screenshot ([#53](https://github.com/34j/so-vits-svc-fork/issues/53)) ([`58d06aa`](https://github.com/34j/so-vits-svc-fork/commit/58d06aa7460dd75ef793da295bf7651ae9940814))
## v1.0.2 (2023-03-21)
### Fix
* **deps:** Update dependency scipy to v1.10.1 ([#35](https://github.com/34j/so-vits-svc-fork/issues/35)) ([`e0253bf`](https://github.com/34j/so-vits-svc-fork/commit/e0253bf1e655f86be605395a18f343763d975101))
## v1.0.1 (2023-03-20)
### Documentation
* Add ThrowawayAccount01 as a contributor for bug ([#47](https://github.com/34j/so-vits-svc-fork/issues/47)) ([`15e31fa`](https://github.com/34j/so-vits-svc-fork/commit/15e31fa806249d45235918fa62a48a86c43538cb))
* Add BlueAmulet as a contributor for ideas ([#46](https://github.com/34j/so-vits-svc-fork/issues/46)) ([`a3bcb2b`](https://github.com/34j/so-vits-svc-fork/commit/a3bcb2be2992c98bcc2485082c19009c74cb3194))
### Performance
* **inference_main:** Do dummy inference before running vc ([#45](https://github.com/34j/so-vits-svc-fork/issues/45)) ([`4066c43`](https://github.com/34j/so-vits-svc-fork/commit/4066c4334b107062d2daa7c9dc00600a56c6e553))
## v1.0.0 (2023-03-20)
### Fix
* Fix default dataset path ([#43](https://github.com/34j/so-vits-svc-fork/issues/43)) ([`ac47fed`](https://github.com/34j/so-vits-svc-fork/commit/ac47fede2581d375c2be9c28102961f19f5a9aa1))
### Breaking
* the behaviour of preprocess_resample changes when there is a folder ./dataset_raw/44k and "44k" is no longer allowed as a speaker name in some conditions ([`ac47fed`](https://github.com/34j/so-vits-svc-fork/commit/ac47fede2581d375c2be9c28102961f19f5a9aa1))
## v0.8.2 (2023-03-20)
### Fix
* Fix compute_f0_crepe returning wrong length ([#42](https://github.com/34j/so-vits-svc-fork/issues/42)) ([`afb42b0`](https://github.com/34j/so-vits-svc-fork/commit/afb42b019ccd133876a2c55cf01007950a733d8c))
## v0.8.1 (2023-03-20)
### Fix
* **deps:** Update dependency librosa to v0.10.0 ([#40](https://github.com/34j/so-vits-svc-fork/issues/40)) ([`8e92f71`](https://github.com/34j/so-vits-svc-fork/commit/8e92f71b2820628f0f8583e6bc455d8f753f4302))
## v0.8.0 (2023-03-20)
### Feature
* Add more f0 calculation methods ([#39](https://github.com/34j/so-vits-svc-fork/issues/39)) ([`6b3b20d`](https://github.com/34j/so-vits-svc-fork/commit/6b3b20dfd609d81cb1184b7c8e8865a58f8d45f9))
## v0.7.1 (2023-03-20)
### Fix
* **deps:** Update dependency gradio to v3.22.1 ([#33](https://github.com/34j/so-vits-svc-fork/issues/33)) ([`f09fc23`](https://github.com/34j/so-vits-svc-fork/commit/f09fc23ca82519cc095509d4d4760561424a17ec))
## v0.7.0 (2023-03-20)
### Feature
* **preprocessing:** Allow nested dataset ([#19](https://github.com/34j/so-vits-svc-fork/issues/19)) ([`0433151`](https://github.com/34j/so-vits-svc-fork/commit/0433151d94c4da8e84a0183bdd47f1e08ea3c462))
## v0.6.3 (2023-03-20)
### Fix
* **deps:** Update dependency torch to v1.13.1 ([#27](https://github.com/34j/so-vits-svc-fork/issues/27)) ([`8826d68`](https://github.com/34j/so-vits-svc-fork/commit/8826d6870e223e7969baa069bf12235e0deec0b7))
* **deps:** Update dependency torchaudio to v0.13.1 ([#28](https://github.com/34j/so-vits-svc-fork/issues/28)) ([`989f5d9`](https://github.com/34j/so-vits-svc-fork/commit/989f5d903b47ba9b0ea1d0fe37cbfe76edf0a811))
### Documentation
* **readme:** Update notes about VRAM caps ([#18](https://github.com/34j/so-vits-svc-fork/issues/18)) ([`0a245f4`](https://github.com/34j/so-vits-svc-fork/commit/0a245f4ee69bd0d4371836367becf0fe409431e2))
## v0.6.2 (2023-03-19)
### Fix
* Use hubert preprocess force_rebuild argument ([#15](https://github.com/34j/so-vits-svc-fork/issues/15)) ([`87cf807`](https://github.com/34j/so-vits-svc-fork/commit/87cf807496248e2c7b859069f81aa040e86aec59))
### Documentation
* Add GarrettConway as a contributor for bug ([#17](https://github.com/34j/so-vits-svc-fork/issues/17)) ([`31d9671`](https://github.com/34j/so-vits-svc-fork/commit/31d9671207143fd06b8db148802d1e27874151ce))
* **notebook:** Launch tensorboard ([#16](https://github.com/34j/so-vits-svc-fork/issues/16)) ([`52229ba`](https://github.com/34j/so-vits-svc-fork/commit/52229ba0fe9458e37b45287c0a716c7cd36adbd6))
* Add 34j as a contributor for example, infra, and 6 more ([#14](https://github.com/34j/so-vits-svc-fork/issues/14)) ([`1b90378`](https://github.com/34j/so-vits-svc-fork/commit/1b903783b4b89f2f5a4fc2e1b47f3eade0c0402f))
* Add GarrettConway as a contributor for code ([#13](https://github.com/34j/so-vits-svc-fork/issues/13)) ([`716813f`](https://github.com/34j/so-vits-svc-fork/commit/716813fbff85ab4609d8ec3f374b78c6551877e5))
## v0.6.1 (2023-03-19)
### Performance
* **preprocessing:** Better performance ([#12](https://github.com/34j/so-vits-svc-fork/issues/12)) ([`668c8e1`](https://github.com/34j/so-vits-svc-fork/commit/668c8e1f18cefb0ebd2fb2f1d6572ce4d37d1102))
## v0.6.0 (2023-03-18)
### Feature
* Configurable input and output devices ([#11](https://github.com/34j/so-vits-svc-fork/issues/11)) ([`a822a60`](https://github.com/34j/so-vits-svc-fork/commit/a822a6098d322ff37725eee19d17758f72a6db49))
### Documentation
* **notebook:** Fix notebook ([#9](https://github.com/34j/so-vits-svc-fork/issues/9)) ([`427b4c1`](https://github.com/34j/so-vits-svc-fork/commit/427b4c1c6e0482345b17fedb018f7a18db68ccc5))
* Update notebook ([#8](https://github.com/34j/so-vits-svc-fork/issues/8)) ([`ae3e471`](https://github.com/34j/so-vits-svc-fork/commit/ae3e4710aac41555f00ddcdfbcf5a5e925afb718))
## v0.5.0 (2023-03-18)
### Feature
* **gui:** Remember last directory (misc) ([`92558da`](https://github.com/34j/so-vits-svc-fork/commit/92558da2f0e4eb24a8de412fb7e22dc3530b648a))
* **__main__:** Show defaults ([`3d298df`](https://github.com/34j/so-vits-svc-fork/commit/3d298df91bdfca230959603da74331b5eef4d487))
### Fix
* **__main__:** Fix option names ([`7ff34fe`](https://github.com/34j/so-vits-svc-fork/commit/7ff34fe623dde6b0a684c45cf33dc54118f9a800))
### Documentation
* **readme:** Update README.md ([#6](https://github.com/34j/so-vits-svc-fork/issues/6)) ([`b988101`](https://github.com/34j/so-vits-svc-fork/commit/b98810194703b6bb0ede03a00c460eeecdab5131))
## v0.4.1 (2023-03-18)
### Fix
* Call init_logger() ([#5](https://github.com/34j/so-vits-svc-fork/issues/5)) ([`e6378f1`](https://github.com/34j/so-vits-svc-fork/commit/e6378f12e747e618ff90ece1552d09c0d0714d41))
## v0.4.0 (2023-03-18)
### Feature
* Enhance realtime algorythm ([#4](https://github.com/34j/so-vits-svc-fork/issues/4)) ([`d789a12`](https://github.com/34j/so-vits-svc-fork/commit/d789a12308784473ae5d09e0b73fa15bf7554de1))
## v0.3.0 (2023-03-17)
### Feature
* Add gui ([#3](https://github.com/34j/so-vits-svc-fork/issues/3)) ([`34aec2b`](https://github.com/34j/so-vits-svc-fork/commit/34aec2b98ee4ef82ef488129b61a7952af5226a3))
### Documentation
* Update notebook ([`7b74606`](https://github.com/34j/so-vits-svc-fork/commit/7b74606508cfb7e45224cbd76f3de9c43c8b4309))
## v0.2.1 (2023-03-17)
### Fix
* **notebook:** Fix notebook ([`3ed00cc`](https://github.com/34j/so-vits-svc-fork/commit/3ed00cc66d4f66e045f61fc14937cb9160eee556))
## v0.2.0 (2023-03-17)
### Feature
* Realtime inference ([#2](https://github.com/34j/so-vits-svc-fork/issues/2)) ([`4dea1ae`](https://github.com/34j/so-vits-svc-fork/commit/4dea1ae51fe2e47a3f41556bdbe3fefd033d729a))
## v0.1.0 (2023-03-17)
### Feature
* Main feat ([#1](https://github.com/34j/so-vits-svc-fork/issues/1)) ([`faa990c`](https://github.com/34j/so-vits-svc-fork/commit/faa990ce6411d8b4e8b3d2d48c4b532b76ff7800))
voice_conversion/so-vits-svc-paul-v2/CONTRIBUTING.md
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# Contributing
Contributions are welcome, and they are greatly appreciated! Every little helps, and credit will always be given.
You can contribute in many ways:
## Types of Contributions
### Report Bugs
Report bugs to [our issue page][gh-issues]. If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
### Fix Bugs
Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.
### Implement Features
Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.
### Write Documentation
SoftVC VITS Singing Voice Conversion Fork could always use more documentation, whether as part of the official SoftVC VITS Singing Voice Conversion Fork docs, in docstrings, or even on the web in blog posts, articles, and such.
### Submit Feedback
The best way to send feedback [our issue page][gh-issues] on GitHub. If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that contributions are welcome 😊
## Get Started!
Ready to contribute? Here's how to set yourself up for local development.
1. Fork the repo on GitHub.
2. Clone your fork locally:
```shell
$ git clone git@github.com:your_name_here/so-vits-svc-fork.git
```
3. Install the project dependencies with [Poetry](https://python-poetry.org):
```shell
$ poetry install
```
4. Create a branch for local development:
```shell
$ git checkout -b name-of-your-bugfix-or-feature
```
Now you can make your changes locally.
5. When you're done making changes, check that your changes pass our tests:
```shell
$ poetry run pytest
```
6. Linting is done through [pre-commit](https://pre-commit.com). Provided you have the tool installed globally, you can run them all as one-off:
```shell
$ pre-commit run -a
```
Or better, install the hooks once and have them run automatically each time you commit:
```shell
$ pre-commit install
```
7. Commit your changes and push your branch to GitHub:
```shell
$ git add .
$ git commit -m "feat(something): your detailed description of your changes"
$ git push origin name-of-your-bugfix-or-feature
```
Note: the commit message should follow [the conventional commits](https://www.conventionalcommits.org). We run [`commitlint` on CI](https://github.com/marketplace/actions/commit-linter) to validate it, and if you've installed pre-commit hooks at the previous step, the message will be checked at commit time.
8. Submit a pull request through the GitHub website or using the GitHub CLI (if you have it installed):
```shell
$ gh pr create --fill
```
## Pull Request Guidelines
We like to have the pull request open as soon as possible, that's a great place to discuss any piece of work, even unfinished. You can use draft pull request if it's still a work in progress. Here are a few guidelines to follow:
1. Include tests for feature or bug fixes.
2. Update the documentation for significant features.
3. Ensure tests are passing on CI.
## Tips
To run a subset of tests:
```shell
$ pytest tests
```
## Making a new release
The deployment should be automated and can be triggered from the Semantic Release workflow in GitHub. The next version will be based on [the commit logs](https://python-semantic-release.readthedocs.io/en/latest/commit-log-parsing.html#commit-log-parsing). This is done by [python-semantic-release](https://python-semantic-release.readthedocs.io/en/latest/index.html) via a GitHub action.
[gh-issues]: https://github.com/34j/so-vits-svc-fork/issues
voice_conversion/so-vits-svc-paul-v2/Dockerfile
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FROM nvidia/cuda:12.0.1-runtime-ubuntu22.04
ENV DEBIAN_FRONTEND=noninteractive
# NB: ffmpeg for transcoding opus -> wav
RUN apt-get update && apt-get install -y \
build-essential \
curl \
ffmpeg \
htop \
libsndfile1 \
nvidia-driver-515 \
python3-pip \
python3.10 \
python3.10-dev \
python3.10-venv \
vim \
wget \
--no-install-recommends \
&& apt-get clean autoclean && apt-get autoremove -y && rm -rf /var/lib/{apt,dpkg,cache,log}/
RUN mkdir /install
WORKDIR /install
COPY requirements.txt .
#RUN pip install -r requirements-without-poetry.txt
RUN python3 -m venv python \
&& . python/bin/activate \
&& pip install -r requirements.txt
WORKDIR /
COPY . /models/voice_conversion/so-vits-svc
voice_conversion/so-vits-svc-paul-v2/LICENSE
+223
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@@ -0,0 +1,223 @@
MIT License
Copyright (c) 2023 34j and contributors
Copyright (c) 2021 Jingyi Li
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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voice_conversion/so-vits-svc-paul-v2/README.md
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so-vits-svc
===========
Original docs are in [README\_ORIGINAL.md](./README_ORIGINAL.md).
This is a snapshot of `f9f16d67e44c127b9cf6d02d6eb5a235d28b70d2` (branch `tag v3.12.1`). It was taken
from voicepaw's fork at https://github.com/voicepaw/so-vits-svc-fork
Dev Installation
----------------
`pip install -e .`
There is also a Dockerfile available so if you prefer:
```sh
docker build -t so-vits-svc .`
docker run -it --privileged --gpus 0 so-vits-svc
```
Usage
-----
```
Usage: svc infer [OPTIONS] INPUT_PATH
Inference
Options:
-o, --output-path PATH path to output dir
-s, --speaker TEXT speaker name
-m, --model-path PATH path to model [default: logs/44k]
-c, --config-path PATH path to config [default: configs/44k/config.json]
-k, --cluster-model-path PATH path to cluster model
-t, --transpose INTEGER transpose [default: 0]
-db, --db-thresh INTEGER threshold (DB) (RELATIVE) [default: -20]
-fm, --f0-method [crepe|crepe-tiny|parselmouth|dio|harvest]
f0 prediction method [default: dio]
-a, --auto-predict-f0 / -na, --no-auto-predict-f0
auto predict f0 [default: na]
-r, --cluster-infer-ratio FLOAT
cluster infer ratio [default: 0]
-n, --noise-scale FLOAT noise scale [default: 0.4]
-p, --pad-seconds FLOAT pad seconds [default: 0.5]
-d, --device TEXT device [default: cuda:0]
-ch, --chunk-seconds FLOAT chunk seconds [default: 0.5]
-ab, --absolute-thresh / -nab, --no-absolute-thresh
absolute thresh [default: nab]
-h, --help Show this message and exit.```
```
voice_conversion/so-vits-svc-paul-v2/README_ORIGINAL.md
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# SoftVC VITS Singing Voice Conversion Fork
[简体中文](README_zh_CN.md)
<p align="center">
<a href="https://github.com/34j/so-vits-svc-fork/actions/workflows/ci.yml?query=branch%3Amain">
<img src="https://img.shields.io/github/actions/workflow/status/34j/so-vits-svc-fork/ci.yml?branch=main&label=CI&logo=github&style=flat-square" alt="CI Status" >
</a>
<a href="https://so-vits-svc-fork.readthedocs.io">
<img src="https://img.shields.io/readthedocs/so-vits-svc-fork.svg?logo=read-the-docs&logoColor=fff&style=flat-square" alt="Documentation Status">
</a>
<a href="https://codecov.io/gh/34j/so-vits-svc-fork">
<img src="https://img.shields.io/codecov/c/github/34j/so-vits-svc-fork.svg?logo=codecov&logoColor=fff&style=flat-square" alt="Test coverage percentage">
</a>
</p>
<p align="center">
<a href="https://python-poetry.org/">
<img src="https://img.shields.io/badge/packaging-poetry-299bd7?style=flat-square&logo=data:image/png;base64,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" alt="Poetry">
</a>
<a href="https://github.com/ambv/black">
<img src="https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square" alt="black">
</a>
<a href="https://github.com/pre-commit/pre-commit">
<img src="https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=flat-square" alt="pre-commit">
</a>
</p>
<p align="center">
<a href="https://pypi.org/project/so-vits-svc-fork/">
<img src="https://img.shields.io/pypi/v/so-vits-svc-fork.svg?logo=python&logoColor=fff&style=flat-square" alt="PyPI Version">
</a>
<img src="https://img.shields.io/pypi/pyversions/so-vits-svc-fork.svg?style=flat-square&logo=python&amp;logoColor=fff" alt="Supported Python versions">
<img src="https://img.shields.io/pypi/l/so-vits-svc-fork.svg?style=flat-square" alt="License">
</p>
A fork of [`so-vits-svc`](https://github.com/svc-develop-team/so-vits-svc) with **realtime support** and **greatly improved interface**. Based on branch `4.0` (v1) and the models are compatible.
## Features not available in the original repo
- **Realtime voice conversion** (enhanced in v1.1.0)
- Integrates [`QuickVC`](https://github.com/quickvc/QuickVC-VoiceConversion)
- Fixed misuse of [`ContentVec`](https://github.com/auspicious3000/contentvec) in the original repository.[^c]
- More accurate pitch estimation using [`CREPE`](https://github.com/marl/crepe/).
- GUI and unified CLI available
- ~2x faster training
- Ready to use just by installing with `pip`.
- Automatically download pretrained models. No need to install `fairseq`.
- Code completely formatted with black, isort, autoflake etc.
[^c]: [#206](https://github.com/34j/so-vits-svc-fork/issues/206)
## Installation
### One click easy installation
<a href="https://github.com/34j/so-vits-svc-fork/releases/download/v1.3.2/install.bat" download>
<img src="https://img.shields.io/badge/.bat-download-blue?style=flat-square&logo=windows" alt="Download .bat">
</a>
### Manual installation
<details>
<summary>Creating a virtual environment</summary>
Windows:
```shell
py -3.10 -m venv venv
venv\Scripts\activate
```
Linux/MacOS:
```shell
python3.10 -m venv venv
source venv/bin/activate
```
Anaconda:
```shell
conda create -n so-vits-svc-fork python=3.10 pip
conda activate so-vits-svc-fork
```
Installing without creating a virtual environment may cause a `PermissionError` if Python is installed in Program Files, etc.
</details>
Install this via pip (or your favourite package manager that uses pip):
```shell
python -m pip install -U pip setuptools wheel
pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -U so-vits-svc-fork
```
<details>
<summary>Notes</summary>
- If no GPU is available or using MacOS, simply remove `pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118`. MPS is probably supported.
- If you are using an AMD GPU on Linux, replace `--index-url https://download.pytorch.org/whl/cu118` with `--index-url https://download.pytorch.org/whl/rocm5.4.2`. AMD GPUs are not supported on Windows ([#120](https://github.com/34j/so-vits-svc-fork/issues/120)).
</details>
### Update
Please update this package regularly to get the latest features and bug fixes.
```shell
pip install -U so-vits-svc-fork
```
## Usage
### Inference
#### GUI
![GUI](https://raw.githubusercontent.com/34j/so-vits-svc-fork/main/docs/_static/gui.png)
GUI launches with the following command:
```shell
svcg
```
#### CLI
- Realtime (from microphone)
```shell
svc vc
```
- File
```shell
svc infer source.wav
```
[Pretrained models](https://huggingface.co/models?search=so-vits-svc-4.0) are available on Hugging Face.
#### Notes
- If using WSL, please note that WSL requires additional setup to handle audio and the GUI will not work without finding an audio device.
- In real-time inference, if there is noise on the inputs, the HuBERT model will react to those as well. Consider using realtime noise reduction applications such as [RTX Voice](https://www.nvidia.com/en-us/geforce/guides/nvidia-rtx-voice-setup-guide/) in this case.
### Training
#### Before training
- If your dataset has BGM, please remove the BGM using software such as [Ultimate Vocal Remover](https://ultimatevocalremover.com/). `3_HP-Vocal-UVR.pth` or `UVR-MDX-NET Main` is recommended. [^1]
- If your dataset is a long audio file with a single speaker, use `svc pre-split` to split the dataset into multiple files (using `librosa`).
- If your dataset is a long audio file with multiple speakers, use `svc pre-sd` to split the dataset into multiple files (using `pyannote.audio`). Further manual classification may be necessary due to accuracy issues. If speakers speak with a variety of speech styles, set --min-speakers larger than the actual number of speakers. Due to unresolved dependencies, please install `pyannote.audio` manually: `pip install pyannote-audio`.
- To manually classify audio files, `svc pre-classify` is available. Up and down arrow keys can be used to change the playback speed.
[^1]: https://ytpmv.info/how-to-use-uvr/
#### Cloud
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/34j/so-vits-svc-fork/blob/main/notebooks/so-vits-svc-fork-4.0.ipynb)
[![Open In Paperspace](https://img.shields.io/badge/Open%20in-Paperspace-blue?style=flat-square&logo=paperspace)](https://console.paperspace.com/github/34j/so-vits-svc-fork-paperspace/blob/main/so-vits-svc-fork-4.0-paperspace.ipynb)
[![Paperspace Referral](<https://img.shields.io/badge/Referral%20($10)-9VJN74I-blue?style=flat-square&logo=paperspace>)](https://www.paperspace.com/?r=9VJN74I)[^p]
If you do not have access to a GPU with more than 10 GB of VRAM, the free plan of Google Colab is recommended for light users and the Pro/Growth plan of Paperspace is recommended for heavy users. Conversely, if you have access to a high-end GPU, the use of cloud services is not recommended.
[^p]: If you register a referral code and then add a payment method, you may save about $5 on your first month's monthly billing. Note that both referral rewards are Paperspace credits and not cash. It was a tough decision but inserted because debugging and training the initial model requires a large amount of computing power and the developer is a student.
#### Local
Place your dataset like `dataset_raw/{speaker_id}/**/{wav_file}.{any_format}` (subfolders and non-ASCII filenames are acceptable) and run:
```shell
svc pre-resample
svc pre-config
svc pre-hubert
svc train -t
```
#### Notes
- Dataset audio duration per file should be <~ 10s.
- It is recommended to increase the `batch_size` as much as possible in `config.json` before the `train` command to match the VRAM capacity. Setting `batch_size` to `auto-{init_batch_size}-{max_n_trials}` (or simply `auto`) will automatically increase `batch_size` until OOM error occurs, but may not be useful in some cases.
- To use `CREPE`, replace `svc pre-hubert` with `svc pre-hubert -fm crepe`.
- To use `ContentVec` correctly, replace `svc pre-config` with `-t so-vits-svc-4.0v1`. Training may take slightly longer because some weights are reset due to reusing legacy initial generator weights.
- To use `MS-iSTFT Decoder`, replace `svc pre-config` with `svc pre-config -t quickvc`.
- Silence removal and volume normalization are automatically performed (as in the upstream repo) and are not required.
- If you have trained on a large, copyright-free dataset, consider releasing it as an initial model.
- For further details (e.g. parameters, etc.), you can see the [Wiki](https://github.com/34j/so-vits-svc-fork/wiki) or [Discussions](https://github.com/34j/so-vits-svc-fork/discussions).
### Further help
For more details, run `svc -h` or `svc <subcommand> -h`.
```shell
> svc -h
Usage: svc [OPTIONS] COMMAND [ARGS]...
so-vits-svc allows any folder structure for training data.
However, the following folder structure is recommended.
When training: dataset_raw/{speaker_name}/**/{wav_name}.{any_format}
When inference: configs/44k/config.json, logs/44k/G_XXXX.pth
If the folder structure is followed, you DO NOT NEED TO SPECIFY model path, config path, etc.
(The latest model will be automatically loaded.)
To train a model, run pre-resample, pre-config, pre-hubert, train.
To infer a model, run infer.
Options:
-h, --help Show this message and exit.
Commands:
clean Clean up files, only useful if you are using the default file structure
infer Inference
onnx Export model to onnx (currently not working)
pre-classify Classify multiple audio files into multiple files
pre-config Preprocessing part 2: config
pre-hubert Preprocessing part 3: hubert If the HuBERT model is not found, it will be...
pre-resample Preprocessing part 1: resample
pre-sd Speech diarization using pyannote.audio
pre-split Split audio files into multiple files
train Train model If D_0.pth or G_0.pth not found, automatically download from hub.
train-cluster Train k-means clustering
vc Realtime inference from microphone
```
#### External Links
[Video Tutorial](https://www.youtube.com/watch?v=tZn0lcGO5OQ)
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
<!-- prettier-ignore-start -->
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
<tbody>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/34j"><img src="https://avatars.githubusercontent.com/u/55338215?v=4?s=80" width="80px;" alt="34j"/><br /><sub><b>34j</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=34j" title="Code">💻</a> <a href="#ideas-34j" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=34j" title="Documentation">📖</a> <a href="#example-34j" title="Examples">💡</a> <a href="#infra-34j" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#maintenance-34j" title="Maintenance">🚧</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/pulls?q=is%3Apr+reviewed-by%3A34j" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=34j" title="Tests">⚠️</a> <a href="#tutorial-34j" title="Tutorials">✅</a> <a href="#promotion-34j" title="Promotion">📣</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3A34j" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/GarrettConway"><img src="https://avatars.githubusercontent.com/u/22782004?v=4?s=80" width="80px;" alt="GarrettConway"/><br /><sub><b>GarrettConway</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=GarrettConway" title="Code">💻</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AGarrettConway" title="Bug reports">🐛</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=GarrettConway" title="Documentation">📖</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/pulls?q=is%3Apr+reviewed-by%3AGarrettConway" title="Reviewed Pull Requests">👀</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/BlueAmulet"><img src="https://avatars.githubusercontent.com/u/43395286?v=4?s=80" width="80px;" alt="BlueAmulet"/><br /><sub><b>BlueAmulet</b></sub></a><br /><a href="#ideas-BlueAmulet" title="Ideas, Planning, & Feedback">🤔</a> <a href="#question-BlueAmulet" title="Answering Questions">💬</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=BlueAmulet" title="Code">💻</a> <a href="#maintenance-BlueAmulet" title="Maintenance">🚧</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ThrowawayAccount01"><img src="https://avatars.githubusercontent.com/u/125531852?v=4?s=80" width="80px;" alt="ThrowawayAccount01"/><br /><sub><b>ThrowawayAccount01</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AThrowawayAccount01" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/MashiroSA"><img src="https://avatars.githubusercontent.com/u/40637516?v=4?s=80" width="80px;" alt="緋"/><br /><sub><b>緋</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=MashiroSA" title="Documentation">📖</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AMashiroSA" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Lordmau5"><img src="https://avatars.githubusercontent.com/u/1345036?v=4?s=80" width="80px;" alt="Lordmau5"/><br /><sub><b>Lordmau5</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ALordmau5" title="Bug reports">🐛</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=Lordmau5" title="Code">💻</a> <a href="#ideas-Lordmau5" title="Ideas, Planning, & Feedback">🤔</a> <a href="#maintenance-Lordmau5" title="Maintenance">🚧</a> <a href="#question-Lordmau5" title="Answering Questions">💬</a> <a href="#userTesting-Lordmau5" title="User Testing">📓</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/DL909"><img src="https://avatars.githubusercontent.com/u/71912115?v=4?s=80" width="80px;" alt="DL909"/><br /><sub><b>DL909</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ADL909" title="Bug reports">🐛</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Satisfy256"><img src="https://avatars.githubusercontent.com/u/101394399?v=4?s=80" width="80px;" alt="Satisfy256"/><br /><sub><b>Satisfy256</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ASatisfy256" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/pierluigizagaria"><img src="https://avatars.githubusercontent.com/u/57801386?v=4?s=80" width="80px;" alt="Pierluigi Zagaria"/><br /><sub><b>Pierluigi Zagaria</b></sub></a><br /><a href="#userTesting-pierluigizagaria" title="User Testing">📓</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ruckusmattster"><img src="https://avatars.githubusercontent.com/u/77196088?v=4?s=80" width="80px;" alt="ruckusmattster"/><br /><sub><b>ruckusmattster</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aruckusmattster" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Desuka-art"><img src="https://avatars.githubusercontent.com/u/111822082?v=4?s=80" width="80px;" alt="Desuka-art"/><br /><sub><b>Desuka-art</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ADesuka-art" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/heyfixit"><img src="https://avatars.githubusercontent.com/u/41658450?v=4?s=80" width="80px;" alt="heyfixit"/><br /><sub><b>heyfixit</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=heyfixit" title="Documentation">📖</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://www.youtube.com/c/NerdyRodent"><img src="https://avatars.githubusercontent.com/u/74688049?v=4?s=80" width="80px;" alt="Nerdy Rodent"/><br /><sub><b>Nerdy Rodent</b></sub></a><br /><a href="#video-nerdyrodent" title="Videos">📹</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/xieyumc"><img src="https://avatars.githubusercontent.com/u/47858007?v=4?s=80" width="80px;" alt="谢宇"/><br /><sub><b>谢宇</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=xieyumc" title="Documentation">📖</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ColdCawfee"><img src="https://avatars.githubusercontent.com/u/79474598?v=4?s=80" width="80px;" alt="ColdCawfee"/><br /><sub><b>ColdCawfee</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AColdCawfee" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/sbersier"><img src="https://avatars.githubusercontent.com/u/34165937?v=4?s=80" width="80px;" alt="sbersier"/><br /><sub><b>sbersier</b></sub></a><br /><a href="#ideas-sbersier" title="Ideas, Planning, & Feedback">🤔</a> <a href="#userTesting-sbersier" title="User Testing">📓</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Asbersier" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Meldoner"><img src="https://avatars.githubusercontent.com/u/43951115?v=4?s=80" width="80px;" alt="Meldoner"/><br /><sub><b>Meldoner</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AMeldoner" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/mmodeusher"><img src="https://avatars.githubusercontent.com/u/46575920?v=4?s=80" width="80px;" alt="mmodeusher"/><br /><sub><b>mmodeusher</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Ammodeusher" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/AlonDan"><img src="https://avatars.githubusercontent.com/u/21152334?v=4?s=80" width="80px;" alt="AlonDan"/><br /><sub><b>AlonDan</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AAlonDan" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Likkkez"><img src="https://avatars.githubusercontent.com/u/44336181?v=4?s=80" width="80px;" alt="Likkkez"/><br /><sub><b>Likkkez</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ALikkkez" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/DuctTapeGames"><img src="https://avatars.githubusercontent.com/u/84365142?v=4?s=80" width="80px;" alt="Duct Tape Games"/><br /><sub><b>Duct Tape Games</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ADuctTapeGames" title="Bug reports">🐛</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://tec.hxlxz.com/"><img src="https://avatars.githubusercontent.com/u/6624983?v=4?s=80" width="80px;" alt="Xianglong He"/><br /><sub><b>Xianglong He</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Ahxl9654" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/75aosu"><img src="https://avatars.githubusercontent.com/u/79185331?v=4?s=80" width="80px;" alt="75aosu"/><br /><sub><b>75aosu</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3A75aosu" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/tonyco82"><img src="https://avatars.githubusercontent.com/u/56610534?v=4?s=80" width="80px;" alt="tonyco82"/><br /><sub><b>tonyco82</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Atonyco82" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/yxlllc"><img src="https://avatars.githubusercontent.com/u/33565655?v=4?s=80" width="80px;" alt="yxlllc"/><br /><sub><b>yxlllc</b></sub></a><br /><a href="#ideas-yxlllc" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=yxlllc" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/outhipped"><img src="https://avatars.githubusercontent.com/u/116147475?v=4?s=80" width="80px;" alt="outhipped"/><br /><sub><b>outhipped</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aouthipped" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/escoolioinglesias"><img src="https://avatars.githubusercontent.com/u/73505402?v=4?s=80" width="80px;" alt="escoolioinglesias"/><br /><sub><b>escoolioinglesias</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aescoolioinglesias" title="Bug reports">🐛</a> <a href="#userTesting-escoolioinglesias" title="User Testing">📓</a> <a href="#video-escoolioinglesias" title="Videos">📹</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Blacksingh"><img src="https://avatars.githubusercontent.com/u/130872856?v=4?s=80" width="80px;" alt="Blacksingh"/><br /><sub><b>Blacksingh</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ABlacksingh" title="Bug reports">🐛</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="http://tybantarnusa.com"><img src="https://avatars.githubusercontent.com/u/9532857?v=4?s=80" width="80px;" alt="Mgs. M. Thoyib Antarnusa"/><br /><sub><b>Mgs. M. Thoyib Antarnusa</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Atybantarnusa" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ZeroHackz"><img src="https://avatars.githubusercontent.com/u/15729496?v=4?s=80" width="80px;" alt="Exosfeer"/><br /><sub><b>Exosfeer</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AZeroHackz" title="Bug reports">🐛</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=ZeroHackz" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/guranon"><img src="https://avatars.githubusercontent.com/u/130421189?v=4?s=80" width="80px;" alt="guranon"/><br /><sub><b>guranon</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aguranon" title="Bug reports">🐛</a> <a href="#ideas-guranon" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=guranon" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/alexanderkoumis"><img src="https://avatars.githubusercontent.com/u/5108856?v=4?s=80" width="80px;" alt="Alexander Koumis"/><br /><sub><b>Alexander Koumis</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=alexanderkoumis" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/acekagami"><img src="https://avatars.githubusercontent.com/u/127201056?v=4?s=80" width="80px;" alt="acekagami"/><br /><sub><b>acekagami</b></sub></a><br /><a href="#translation-acekagami" title="Translation">🌍</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Highupech"><img src="https://avatars.githubusercontent.com/u/114140670?v=4?s=80" width="80px;" alt="Highupech"/><br /><sub><b>Highupech</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AHighupech" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Scorpi"><img src="https://avatars.githubusercontent.com/u/969654?v=4?s=80" width="80px;" alt="Scorpi"/><br /><sub><b>Scorpi</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=Scorpi" title="Code">💻</a></td>
</tr>
</tbody>
</table>
<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->
<!-- ALL-CONTRIBUTORS-LIST:END -->
<!-- prettier-ignore-end -->
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
voice_conversion/so-vits-svc-paul-v2/README_zh_CN.md
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# SoftVC VITS Singing Voice Conversion
<p align="center">
<a href="https://github.com/34j/so-vits-svc-fork/actions/workflows/ci.yml?query=branch%3Amain">
<img src="https://img.shields.io/github/actions/workflow/status/34j/so-vits-svc-fork/ci.yml?branch=main&label=CI&logo=github&style=flat-square" alt="CI Status" >
</a>
<a href="https://so-vits-svc-fork.readthedocs.io">
<img src="https://img.shields.io/readthedocs/so-vits-svc-fork.svg?logo=read-the-docs&logoColor=fff&style=flat-square" alt="Documentation Status">
</a>
<a href="https://codecov.io/gh/34j/so-vits-svc-fork">
<img src="https://img.shields.io/codecov/c/github/34j/so-vits-svc-fork.svg?logo=codecov&logoColor=fff&style=flat-square" alt="Test coverage percentage">
</a>
</p>
<p align="center">
<a href="https://python-poetry.org/">
<img src="https://img.shields.io/badge/packaging-poetry-299bd7?style=flat-square&logo=data:image/png;base64,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" alt="Poetry">
</a>
<a href="https://github.com/ambv/black">
<img src="https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square" alt="black">
</a>
<a href="https://github.com/pre-commit/pre-commit">
<img src="https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=flat-square" alt="pre-commit">
</a>
</p>
<p align="center">
<a href="https://pypi.org/project/so-vits-svc-fork/">
<img src="https://img.shields.io/pypi/v/so-vits-svc-fork.svg?logo=python&logoColor=fff&style=flat-square" alt="PyPI Version">
</a>
<img src="https://img.shields.io/pypi/pyversions/so-vits-svc-fork.svg?style=flat-square&logo=python&amp;logoColor=fff" alt="Supported Python versions">
<img src="https://img.shields.io/pypi/l/so-vits-svc-fork.svg?style=flat-square" alt="License">
</p>
基于 [`so-vits-svc4.0(V1)`](https://github.com/svc-develop-team/so-vits-svc)的一个分支，支持实时推理和图形化推理界面，且兼容其模型。
## 新功能
- **实时语音转换** (增强版本 v1.1.0)
- 与[`QuickVC`](https://github.com/quickvc/QuickVC-VoiceConversion)相结合
- 修复了原始版本中对 [`ContentVec`](https://github.com/auspicious3000/contentvec) 的误用[^c]
- 使用 CREPE 进行更准确的音高推测
- 图形化界面和统一命令行界面
- 相比之前双倍的训练速度
- 只需使用 `pip` 安装即可使用，不需要安装 `fairseq`
- 自动下载预训练模型和 HuBERT 模型
- 使用 black、isort、autoflake 等完全格式化的代码
[^c]: [#206](https://github.com/34j/so-vits-svc-fork/issues/206)
## 安装教程
### 可以使用 bat 一键安装
<a href="https://github.com/xieyumc/so-vits-svc-fork-cn/releases/download/install/install-cn.bat" download>
<img src="https://img.shields.io/badge/.bat-download-blue?style=flat-square&logo=windows" alt="Download .bat">
</a>
### 本 bat 汉化基于英文版，对原版进行了一些本地工作和优化，如安装过程有问题，可以尝试安装原版
<a href="https://github.com/34j/so-vits-svc-fork/releases/download/v1.3.2/install.bat" download>
<img src="https://img.shields.io/badge/.bat-download-blue?style=flat-square&logo=windows" alt="Download .bat">
</a>
### 手动安装
<details>
<summary>创建一个虚拟环境</summary>
Windows:
```shell
py -3.10 -m venv venv
venv\Scripts\activate
```
Linux/MacOS:
```shell
python3.10 -m venv venv
source venv/bin/activate
```
Anaconda:
```shell
conda create -n so-vits-svc-fork python=3.10 pip
conda activate so-vits-svc-fork
```
如果 Python 安装在 Program Files，在安装时未创造虚拟环境可能会导致`PermissionError`
</details>
### 安装
通过 pip 安装 (或者通过包管理器使用 pip 安装):
```shell
python -m pip install -U pip setuptools wheel
pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -U so-vits-svc-fork
```
- 如果没有可用 GPU 或使用 MacOS, 不需要执行 `pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118`. MPS 可能已经安装了.
- 如果在 Linux 下使用 AMD GPU, 请使用此命令 `--index-url https://download.pytorch.org/whl/rocm5.4.2`
替换掉 `--index-url https://download.pytorch.org/whl/cu118` . Windows 下不支持 AMD GPUs (#120).
### 更新
请经常更新以获取最新功能和修复错误:
```shell
pip install -U so-vits-svc-fork
```
## 使用教程
### 推理
#### 图形化界面
![GUI](https://raw.githubusercontent.com/34j/so-vits-svc-fork/main/docs/_static/gui.png)
请使用以下命令运行图形化界面:
```shell
svcg
```
#### 命令行界面
- 实时转换 (输入源为麦克风)
```shell
svc vc
```
- 从文件转换
```shell
svc infer source.wav
```
[预训练模型](https://huggingface.co/models?search=so-vits-svc-4.0) 可以在 HuggingFace 获得。
#### 注意
- 如果使用 WSL, 请注意 WSL 需要额外设置来处理音频，如果 GUI 找不到音频设备将不能正常工作。
- 在实时语音转换中, 如果输入源有杂音, HuBERT
模型依然会把杂音进行推理.可以考虑使用实时噪音减弱程序比如 [RTX Voice](https://www.nvidia.com/en-us/geforce/guides/nvidia-rtx-voice-setup-guide/)
来解决.
### 训练
#### 预处理
- 如果数据集有 BGM,请用例如[Ultimate Vocal Remover](https://ultimatevocalremover.com/)等软件去除 BGM.
推荐使用`3_HP-Vocal-UVR.pth` 或者 `UVR-MDX-NET Main` . [^1]
- 如果数据集是包含单个歌手的长音频文件, 使用 `svc pre-split` 将数据集拆分为多个文件 (使用 `librosa`).
- 如果数据集是包含多个歌手的长音频文件, 使用 `svc pre-sd` 将数据集拆分为多个文件 (使用 `pyannote.audio`)
。为了提高准确率，可能需要手动进行分类。如果歌手的声线多样,请把 --min-speakers 设置为大于实际说话者数量. 如果出现依赖未安装,
请通过 `pip install pyannote-audio`来安装 `pyannote.audio`。
[^1]: https://ytpmv.info/how-to-use-uvr/
#### 云端
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/34j/so-vits-svc-fork/blob/main/notebooks/so-vits-svc-fork-4.0.ipynb)
[![Open In Paperspace](https://img.shields.io/badge/Open%20in-Paperspace-blue?style=flat-square&logo=paperspace)](https://console.paperspace.com/github/34j/so-vits-svc-fork-paperspace/blob/main/so-vits-svc-fork-4.0-paperspace.ipynb)
[![Paperspace Referral](<https://img.shields.io/badge/Referral%20($10)-9VJN74I-blue?style=flat-square&logo=paperspace>)](https://www.paperspace.com/?r=9VJN74I)[^p]
如果你无法获取 10GB 显存以上的显卡，对于轻量用户，推荐使用 Google Colab 的免费方案；而重度用户，则建议使用 Paperspace 的 Pro/Growth Plan。当然，如果你有高端的显卡，就没必要使用云服务了。
[^p]: If you register a referral code and then add a payment method, you may save about $5 on your first month's monthly billing. Note that both referral rewards are Paperspace credits and not cash. It was a tough decision but inserted because debugging and training the initial model requires a large amount of computing power and the developer is a student.
#### 本地
将数据集处理成 `dataset_raw/{speaker_id}/**/{wav_file}.{any_format}` 的格式(可以使用子文件夹和非 ASCII 文件名)然后运行:
```shell
svc pre-resample
svc pre-config
svc pre-hubert
svc train -t
```
#### 注意
- 数据集的每个文件应该小于 10s，不然显存会爆。
- 建议在执行 `train` 命令之前提高 `config.json` 中的 `batch_size` 以匹配显存容量。 将`batch_size`设为`auto-{init_batch_size}-{max_n_trials}`（或者只需设为`auto`）就会自动提高`batch_size`，直到爆显存为止（不过自动调高 batch_size 有概率失效）
- 如果想要 f0 的推理方式为 `CREPE`, 用 `svc pre-hubert -fm crepe` 替换 `svc pre-hubert`.
- 若想正确使用`ContentVec`，用 `-t so-vits-svc-4.0v1`替换`svc pre-config`。由于复用 generator weights，一些 weights 会被重置而导致训练时间稍微延长.
- 若要使用`MS-iSTFT Decoder`，用 `svc pre-config -t quickvc`替换 `svc pre-config`.
- 在原始仓库中，会自动移除静音和进行音量平衡，且这个操作并不是必须要处理的。
- 倘若你已经大规模训练了一个免费公开版权的数据集，可以考虑将其作为底模发布。
- 对于更多细节（比如参数等），详见[Wiki](https://github.com/34j/so-vits-svc-fork/wiki) 或 [Discussions](https://github.com/34j/so-vits-svc-fork/discussions).
### 帮助
更多命令, 运行 `svc -h` 或者 `svc <subcommand> -h`
```shell
> svc -h
用法: svc [OPTIONS] COMMAND [ARGS]...
so-vits-svc 允许任何文件夹结构用于训练数据
但是, 建议使用以下文件夹结构
训练: dataset_raw/{speaker_name}/**/{wav_name}.{any_format}
推理: configs/44k/config.json, logs/44k/G_XXXX.pth
如果遵循文件夹结构,则无需指定模型路径,配置路径等,将自动加载最新模型
若要要训练模型, 运行 pre-resample, pre-config, pre-hubert, train.
若要要推理模型, 运行 infer.
可选:
-h, --help 显示信息并退出
命令:
clean 清理文件,仅在使用默认文件结构时有用
infer 推理
onnx 导出模型到onnx
pre-config 预处理第 2 部分: config
pre-hubert 预处理第 3 部分: 如果没有找到 HuBERT 模型,则会...
pre-resample 预处理第 1 部分: resample
pre-sd Speech diarization 使用 pyannote.audio
pre-split 将音频文件拆分为多个文件
train 训练模型 如果 D_0.pth 或 G_0.pth 没有找到,自动从集线器下载.
train-cluster 训练 k-means 聚类模型
vc 麦克风实时推理
```
#### 补充链接
[视频教程](https://www.youtube.com/watch?v=tZn0lcGO5OQ)
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
<!-- prettier-ignore-start -->
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
<tbody>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/34j"><img src="https://avatars.githubusercontent.com/u/55338215?v=4?s=80" width="80px;" alt="34j"/><br /><sub><b>34j</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=34j" title="Code">💻</a> <a href="#ideas-34j" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=34j" title="Documentation">📖</a> <a href="#example-34j" title="Examples">💡</a> <a href="#infra-34j" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#maintenance-34j" title="Maintenance">🚧</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/pulls?q=is%3Apr+reviewed-by%3A34j" title="Reviewed Pull Requests">👀</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=34j" title="Tests">⚠️</a> <a href="#tutorial-34j" title="Tutorials">✅</a> <a href="#promotion-34j" title="Promotion">📣</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3A34j" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/GarrettConway"><img src="https://avatars.githubusercontent.com/u/22782004?v=4?s=80" width="80px;" alt="GarrettConway"/><br /><sub><b>GarrettConway</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=GarrettConway" title="Code">💻</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AGarrettConway" title="Bug reports">🐛</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=GarrettConway" title="Documentation">📖</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/pulls?q=is%3Apr+reviewed-by%3AGarrettConway" title="Reviewed Pull Requests">👀</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/BlueAmulet"><img src="https://avatars.githubusercontent.com/u/43395286?v=4?s=80" width="80px;" alt="BlueAmulet"/><br /><sub><b>BlueAmulet</b></sub></a><br /><a href="#ideas-BlueAmulet" title="Ideas, Planning, & Feedback">🤔</a> <a href="#question-BlueAmulet" title="Answering Questions">💬</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=BlueAmulet" title="Code">💻</a> <a href="#maintenance-BlueAmulet" title="Maintenance">🚧</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ThrowawayAccount01"><img src="https://avatars.githubusercontent.com/u/125531852?v=4?s=80" width="80px;" alt="ThrowawayAccount01"/><br /><sub><b>ThrowawayAccount01</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AThrowawayAccount01" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/MashiroSA"><img src="https://avatars.githubusercontent.com/u/40637516?v=4?s=80" width="80px;" alt="緋"/><br /><sub><b>緋</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=MashiroSA" title="Documentation">📖</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AMashiroSA" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Lordmau5"><img src="https://avatars.githubusercontent.com/u/1345036?v=4?s=80" width="80px;" alt="Lordmau5"/><br /><sub><b>Lordmau5</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ALordmau5" title="Bug reports">🐛</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=Lordmau5" title="Code">💻</a> <a href="#ideas-Lordmau5" title="Ideas, Planning, & Feedback">🤔</a> <a href="#maintenance-Lordmau5" title="Maintenance">🚧</a> <a href="#question-Lordmau5" title="Answering Questions">💬</a> <a href="#userTesting-Lordmau5" title="User Testing">📓</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/DL909"><img src="https://avatars.githubusercontent.com/u/71912115?v=4?s=80" width="80px;" alt="DL909"/><br /><sub><b>DL909</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ADL909" title="Bug reports">🐛</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Satisfy256"><img src="https://avatars.githubusercontent.com/u/101394399?v=4?s=80" width="80px;" alt="Satisfy256"/><br /><sub><b>Satisfy256</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ASatisfy256" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/pierluigizagaria"><img src="https://avatars.githubusercontent.com/u/57801386?v=4?s=80" width="80px;" alt="Pierluigi Zagaria"/><br /><sub><b>Pierluigi Zagaria</b></sub></a><br /><a href="#userTesting-pierluigizagaria" title="User Testing">📓</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ruckusmattster"><img src="https://avatars.githubusercontent.com/u/77196088?v=4?s=80" width="80px;" alt="ruckusmattster"/><br /><sub><b>ruckusmattster</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aruckusmattster" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Desuka-art"><img src="https://avatars.githubusercontent.com/u/111822082?v=4?s=80" width="80px;" alt="Desuka-art"/><br /><sub><b>Desuka-art</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ADesuka-art" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/heyfixit"><img src="https://avatars.githubusercontent.com/u/41658450?v=4?s=80" width="80px;" alt="heyfixit"/><br /><sub><b>heyfixit</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=heyfixit" title="Documentation">📖</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://www.youtube.com/c/NerdyRodent"><img src="https://avatars.githubusercontent.com/u/74688049?v=4?s=80" width="80px;" alt="Nerdy Rodent"/><br /><sub><b>Nerdy Rodent</b></sub></a><br /><a href="#video-nerdyrodent" title="Videos">📹</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/xieyumc"><img src="https://avatars.githubusercontent.com/u/47858007?v=4?s=80" width="80px;" alt="谢宇"/><br /><sub><b>谢宇</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=xieyumc" title="Documentation">📖</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ColdCawfee"><img src="https://avatars.githubusercontent.com/u/79474598?v=4?s=80" width="80px;" alt="ColdCawfee"/><br /><sub><b>ColdCawfee</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AColdCawfee" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/sbersier"><img src="https://avatars.githubusercontent.com/u/34165937?v=4?s=80" width="80px;" alt="sbersier"/><br /><sub><b>sbersier</b></sub></a><br /><a href="#ideas-sbersier" title="Ideas, Planning, & Feedback">🤔</a> <a href="#userTesting-sbersier" title="User Testing">📓</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Asbersier" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Meldoner"><img src="https://avatars.githubusercontent.com/u/43951115?v=4?s=80" width="80px;" alt="Meldoner"/><br /><sub><b>Meldoner</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AMeldoner" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/mmodeusher"><img src="https://avatars.githubusercontent.com/u/46575920?v=4?s=80" width="80px;" alt="mmodeusher"/><br /><sub><b>mmodeusher</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Ammodeusher" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/AlonDan"><img src="https://avatars.githubusercontent.com/u/21152334?v=4?s=80" width="80px;" alt="AlonDan"/><br /><sub><b>AlonDan</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AAlonDan" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Likkkez"><img src="https://avatars.githubusercontent.com/u/44336181?v=4?s=80" width="80px;" alt="Likkkez"/><br /><sub><b>Likkkez</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ALikkkez" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/DuctTapeGames"><img src="https://avatars.githubusercontent.com/u/84365142?v=4?s=80" width="80px;" alt="Duct Tape Games"/><br /><sub><b>Duct Tape Games</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ADuctTapeGames" title="Bug reports">🐛</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://tec.hxlxz.com/"><img src="https://avatars.githubusercontent.com/u/6624983?v=4?s=80" width="80px;" alt="Xianglong He"/><br /><sub><b>Xianglong He</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Ahxl9654" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/75aosu"><img src="https://avatars.githubusercontent.com/u/79185331?v=4?s=80" width="80px;" alt="75aosu"/><br /><sub><b>75aosu</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3A75aosu" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/tonyco82"><img src="https://avatars.githubusercontent.com/u/56610534?v=4?s=80" width="80px;" alt="tonyco82"/><br /><sub><b>tonyco82</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Atonyco82" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/yxlllc"><img src="https://avatars.githubusercontent.com/u/33565655?v=4?s=80" width="80px;" alt="yxlllc"/><br /><sub><b>yxlllc</b></sub></a><br /><a href="#ideas-yxlllc" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=yxlllc" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/outhipped"><img src="https://avatars.githubusercontent.com/u/116147475?v=4?s=80" width="80px;" alt="outhipped"/><br /><sub><b>outhipped</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aouthipped" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/escoolioinglesias"><img src="https://avatars.githubusercontent.com/u/73505402?v=4?s=80" width="80px;" alt="escoolioinglesias"/><br /><sub><b>escoolioinglesias</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aescoolioinglesias" title="Bug reports">🐛</a> <a href="#userTesting-escoolioinglesias" title="User Testing">📓</a> <a href="#video-escoolioinglesias" title="Videos">📹</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/Blacksingh"><img src="https://avatars.githubusercontent.com/u/130872856?v=4?s=80" width="80px;" alt="Blacksingh"/><br /><sub><b>Blacksingh</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3ABlacksingh" title="Bug reports">🐛</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="http://tybantarnusa.com"><img src="https://avatars.githubusercontent.com/u/9532857?v=4?s=80" width="80px;" alt="Mgs. M. Thoyib Antarnusa"/><br /><sub><b>Mgs. M. Thoyib Antarnusa</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Atybantarnusa" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/ZeroHackz"><img src="https://avatars.githubusercontent.com/u/15729496?v=4?s=80" width="80px;" alt="Exosfeer"/><br /><sub><b>Exosfeer</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3AZeroHackz" title="Bug reports">🐛</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=ZeroHackz" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/guranon"><img src="https://avatars.githubusercontent.com/u/130421189?v=4?s=80" width="80px;" alt="guranon"/><br /><sub><b>guranon</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/issues?q=author%3Aguranon" title="Bug reports">🐛</a> <a href="#ideas-guranon" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=guranon" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/alexanderkoumis"><img src="https://avatars.githubusercontent.com/u/5108856?v=4?s=80" width="80px;" alt="Alexander Koumis"/><br /><sub><b>Alexander Koumis</b></sub></a><br /><a href="https://github.com/voicepaw/so-vits-svc-fork/commits?author=alexanderkoumis" title="Code">💻</a></td>
</tr>
</tbody>
</table>
<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->
<!-- ALL-CONTRIBUTORS-LIST:END -->
<!-- prettier-ignore-end -->
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
voice_conversion/so-vits-svc-paul-v2/commitlint.config.js
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module.exports = {
extends: ["@commitlint/config-conventional"],
rules: {
"header-max-length": [0, "always", Infinity],
"body-max-line-length": [0, "always", Infinity],
"footer-max-line-length": [0, "always", Infinity],
},
};
voice_conversion/so-vits-svc-paul-v2/docs/Makefile
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
voice_conversion/so-vits-svc-paul-v2/docs/_static/.gitkeep
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voice_conversion/so-vits-svc-paul-v2/docs/changelog.md
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```{include} ../CHANGELOG.md
```
voice_conversion/so-vits-svc-paul-v2/docs/conf.py
+78
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# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
from pathlib import Path
from typing import Any
from sphinx.application import Sphinx
from sphinx.ext import apidoc
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
project = "SoftVC VITS Singing Voice Conversion Fork"
copyright = "2023, 34j"
author = "34j"
release = "0.0.0"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"myst_parser",
"sphinx.ext.napoleon",
"sphinx.ext.autodoc",
"sphinx.ext.viewcode",
]
napoleon_google_docstring = False
# The suffix of source filenames.
source_suffix = [".rst", ".md"]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns: list[str] = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "sphinx_rtd_theme"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# -- Automatically run sphinx-apidoc -----------------------------------------
def run_apidoc(_: Any) -> None:
docs_path = Path(__file__).parent
module_path = docs_path.parent / "src" / "so_vits_svc_fork"
apidoc.main(
[
"--force",
"--module-first",
"-o",
docs_path.as_posix(),
module_path.as_posix(),
]
)
def setup(app: Sphinx) -> None:
app.connect("builder-inited", run_apidoc)
voice_conversion/so-vits-svc-paul-v2/docs/contributing.md
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```{include} ../CONTRIBUTING.md
```
voice_conversion/so-vits-svc-paul-v2/docs/index.md
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# Welcome to SoftVC VITS Singing Voice Conversion Fork documentation!
```{toctree}
:caption: Installation & Usage
:maxdepth: 2
installation
usage
```
```{toctree}
:caption: Project Info
:maxdepth: 2
changelog
contributing
```
```{toctree}
:caption: API Reference
:maxdepth: 2
so_vits_svc_fork
```
```{include} ../README.md
```
voice_conversion/so-vits-svc-paul-v2/docs/installation.md
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# Installation
The package is published on [PyPI](https://pypi.org/project/so-vits-svc-fork/) and can be installed with `pip` (or any equivalent):
```bash
pip install so-vits-svc-fork
```
voice_conversion/so-vits-svc-paul-v2/docs/make.bat
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@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=.
set BUILDDIR=_build
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.https://www.sphinx-doc.org/
exit /b 1
)
if "%1" == "" goto help
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
voice_conversion/so-vits-svc-paul-v2/docs/usage.md
+9
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# Usage
To use this package, import it:
```python
import so_vits_svc_fork
```
TODO: Document usage
voice_conversion/so-vits-svc-paul-v2/easy-installation/install-cn.bat
+101
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@echo off
echo 本bat汉化基于英文版，对原版进行了一些本地工作和优化，如安装过程有问题，可以尝试安装原版
echo.
echo.
echo 检查 Python 版本 3.10...
echo.
py -3.10 --version >nul 2>&1
if %errorlevel%==0 (
echo Python 3.10 已经安装
echo.
) else (
echo Python 3.10 未安装，开始下载...
echo.
curl https://www.python.org/ftp/python/3.10.10/python-3.10.10-amd64.exe -o python-3.10.10-amd64.exe
echo 安装 Python 3.10...
echo.
python-3.10.10-amd64.exe /quiet InstallAllUsers=1 PrependPath=1
echo 清理安装器...
echo.
del python-3.10.10-amd64.exe
)
echo.
echo 检查 GPU...
echo.
nvidia-smi >nul 2>&1
if %errorlevel%==0 (
echo 找到可用GPU
echo.
) else (
echo 未找到可用found
echo.
)
nvidia-smi >nul 2>&1
if %errorlevel%==0 (
echo.
echo 检查CUDA...
echo.
if %errorlevel%==0 (
echo CUDA 已经安装
echo.
) else (
echo 未检测到CUDA，请从下面链接手动安装CUDA，安装后再重新运行本程序
echo https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows
echo.
echo 如果你已经确定安装了CUDA，可能是程序检测出错，可以按任意键强制继续执行，否则请关闭本程序，安装好CUDA后再重新运行
echo.
Pause
)
echo 检查 cuDNN...
if exist "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\cudnn64_8.dll" (
echo cuDNN 已经安装
echo.
) else (
echo 未检测到cuDNN，请从下面链接手动安装CUDA，安装后再重新运行本程序
echo https://developer.nvidia.com/cudnn (https://developer.nvidia.com/downloads/compute/cudnn/secure/8.8.1/local_installers/11.8/cudnn-windows-x86_64-8.8.1.3_cuda11-archive.zip/)
echo.
echo 如果你已经确定安装了cuDNN，可能是程序检测出错，可以按任意键强制继续执行，否则请关闭本程序，安装好CUDA后再重新运行
echo.
Pause
)
)
echo.
echo 正在创建虚拟环境（需要一点时间，请耐心等待）...
echo.
py -3.10 -m venv venv
echo.
echo 升级 pip 和 wheel...
echo.
venv\Scripts\python.exe -m pip install --upgrade pip wheel
echo.
nvidia-smi >nul 2>&1
if %errorlevel%==0 (
echo 安装 PyTorch （GPU版本）...
echo.
venv\Scripts\pip.exe install torch torchvision torchaudio --index-url https://mirror.sjtu.edu.cn/pytorch-wheels
echo 安装 PyTorch （CPU版本）...
echo.
venv\Scripts\pip.exe install torch torchaudio -i https://pypi.tuna.tsinghua.edu.cn/simple pyspider
)
echo.
echo 请检查以上包是否都成功安装，确定成功安装后，按任意键开始安装so-vits-svc-fork
echo.
Pause
echo 安装 so-vits-svc-fork...
echo.
venv\Scripts\pip.exe install so-vits-svc-fork
echo.
echo 启动 so-vits-svc-fork 图形化界面...
echo.
venv\Scripts\svcg.exe
Pause
voice_conversion/so-vits-svc-paul-v2/easy-installation/install.bat
+70
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@echo off
echo Checking for Python 3.10...
py -3.10 --version >nul 2>&1
if %errorlevel%==0 (
echo Python 3.10 is already installed.
) else (
echo Python 3.10 is not installed. Downloading installer...
curl https://www.python.org/ftp/python/3.10.10/python-3.10.10-amd64.exe -o python-3.10.10-amd64.exe
echo Installing Python 3.10...
python-3.10.10-amd64.exe /quiet InstallAllUsers=1 PrependPath=1
echo Cleaning up installer...
del python-3.10.10-amd64.exe
)
echo Checking GPU...
nvidia-smi >nul 2>&1
if %errorlevel%==0 (
echo found a GPU
) else (
echo no GPU found
)
nvidia-smi >nul 2>&1
if %errorlevel%==0 (
echo Checking CUDA...
nvcc --version
if %errorlevel%==0 (
echo CUDA is already installed.
) else (
echo CUDA is not installed.
echo Please install CUDA 11.7 from https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows
echo If you are sure it is already installed and added to path, press any key to force the installation to continue
Pause
)
echo Checking cuDNN...
if exist "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\cudnn64_8.dll" (
echo cuDNN is already installed.
) else (
echo cuDNN is not installed.
echo Please install cuDNN 11.8 from https://developer.nvidia.com/cudnn (https://developer.nvidia.com/downloads/compute/cudnn/secure/8.8.1/local_installers/11.8/cudnn-windows-x86_64-8.8.1.3_cuda11-archive.zip/)
echo If you are sure it is already installed and added to path, press any key to force the installation to continue
Pause
)
)
echo Creating virtual environment...
py -3.10 -m venv venv
echo Updating pip and wheel...
venv\Scripts\python.exe -m pip install --upgrade pip wheel
nvidia-smi >nul 2>&1
if %errorlevel%==0 (
echo Installing PyTorch with GPU support...
venv\Scripts\pip.exe install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
) else (
echo Installing PyTorch without GPU support...
venv\Scripts\pip.exe install torch torchaudio
)
echo Installing so-vits-svc-fork...
venv\Scripts\pip.exe install so-vits-svc-fork
echo Launching so-vits-svc-fork GUI...
venv\Scripts\svcg.exe
voice_conversion/so-vits-svc-paul-v2/notebooks/so-vits-svc-fork-4.0.ipynb
+315
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@@ -0,0 +1,315 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Before training\n",
"\n",
"This program saves the last 3 generations of models to Google Drive. Since 1 generation of models is >1GB, you should have at least 3GB of free space in Google Drive. If you do not have such free space, it is recommended to create another Google Account.\n",
"\n",
"Training requires >10GB VRAM. (T4 should be enough) Inference does not require such a lot of VRAM."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Check GPU\n",
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Mount Google Drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Install dependencies\n",
"#@markdown pip may fail to resolve dependencies and raise ERROR, but it can be ignored.\n",
"!python -m pip install -U pip wheel\n",
"%pip install -U ipython \n",
"\n",
"#@markdown Branch (for development)\n",
"BRANCH = \"none\" #@param {\"type\": \"string\"}\n",
"if BRANCH == \"none\":\n",
" %pip install -U so-vits-svc-fork\n",
"else:\n",
" %pip install -U git+https://github.com/34j/so-vits-svc-fork.git@{BRANCH}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Make dataset directory\n",
"!mkdir -p \"dataset_raw\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!rm -r \"dataset_raw\"\n",
"#!rm -r \"dataset/44k\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Copy your dataset\n",
"#@markdown **We assume that your dataset is in your Google Drive's `so-vits-svc-fork/dataset/(speaker_name)` directory.**\n",
"DATASET_NAME = \"kiritan\" #@param {type: \"string\"}\n",
"!cp -R /content/drive/MyDrive/so-vits-svc-fork/dataset/{DATASET_NAME}/ -t \"dataset_raw/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Download dataset (Tsukuyomi-chan JVS)\n",
"#@markdown You can download this dataset if you don't have your own dataset.\n",
"#@markdown Make sure you agree to the license when using this dataset.\n",
"#@markdown https://tyc.rei-yumesaki.net/material/corpus/#toc6\n",
"# !wget https://tyc.rei-yumesaki.net/files/sozai-tyc-corpus1.zip\n",
"# !unzip sozai-tyc-corpus1.zip\n",
"# !mv \"/content/つくよみちゃんコーパス Vol.1 声優統計コーパス（JVSコーパス準拠）/おまけ：WAV（+12dB増幅＆高音域削減）/WAV（+12dB増幅＆高音域削減）\" \"dataset_raw/tsukuyomi\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Automatic preprocessing\n",
"!svc pre-resample"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!svc pre-config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Copy configs file\n",
"!cp configs/44k/config.json drive/MyDrive/so-vits-svc-fork"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"F0_METHOD = \"dio\" #@param [\"crepe\", \"crepe-tiny\", \"parselmouth\", \"dio\", \"harvest\"]\n",
"!svc pre-hubert -fm {F0_METHOD}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Train\n",
"%load_ext tensorboard\n",
"%tensorboard --logdir drive/MyDrive/so-vits-svc-fork/logs/44k\n",
"!svc train --model-path drive/MyDrive/so-vits-svc-fork/logs/44k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training Cluster model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!svc train-cluster --output-path drive/MyDrive/so-vits-svc-fork/logs/44k/kmeans.pt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Get the author's voice as a source\n",
"import random\n",
"NAME = str(random.randint(1, 49))\n",
"TYPE = \"fsd50k\" #@param [\"\", \"digit\", \"dog\", \"fsd50k\"]\n",
"CUSTOM_FILEPATH = \"\" #@param {type: \"string\"}\n",
"if CUSTOM_FILEPATH != \"\":\n",
" NAME = CUSTOM_FILEPATH\n",
"else:\n",
" # it is extremely difficult to find a voice that can download from the internet directly\n",
" if TYPE == \"dog\":\n",
" !wget -N f\"https://huggingface.co/datasets/437aewuh/dog-dataset/resolve/main/dogs/dogs_{NAME:.0000}.wav\" -O {NAME}.wav\n",
" elif TYPE == \"digit\":\n",
" # george, jackson, lucas, nicolas, ...\n",
" !wget -N f\"https://github.com/Jakobovski/free-spoken-digit-dataset/raw/master/recordings/0_george_{NAME}.wav\" -O {NAME}.wav\n",
" elif TYPE == \"fsd50k\":\n",
" !wget -N f\"https://huggingface.co/datasets/Fhrozen/FSD50k/blob/main/clips/dev/{10000+int(NAME)}.wav\" -O {NAME}.wav\n",
" else:\n",
" !wget -N f\"https://zunko.jp/sozai/utau/voice_{\"kiritan\" if NAME < 25 else \"itako\"}{NAME % 5 + 1}.wav\" -O {NAME}.wav\n",
"from IPython.display import Audio, display\n",
"display(Audio(f\"{NAME}.wav\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Use trained model\n",
"#@markdown **Put your .wav file in `so-vits-svc-fork/audio` directory**\n",
"from IPython.display import Audio, display\n",
"!svc infer drive/MyDrive/so-vits-svc-fork/audio/{NAME}.wav -m drive/MyDrive/so-vits-svc-fork/logs/44k/ -c drive/MyDrive/so-vits-svc-fork/logs/44k/config.json\n",
"display(Audio(f\"drive/MyDrive/so-vits-svc-fork/audio/{NAME}.out.wav\", autoplay=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##@title Use trained model (with cluster)\n",
"!svc infer {NAME}.wav -s speaker -r 0.1 -m drive/MyDrive/so-vits-svc-fork/logs/44k/ -c drive/MyDrive/so-vits-svc-fork/logs/44k/config.json -k drive/MyDrive/so-vits-svc-fork/logs/44k/kmeans.pt\n",
"display(Audio(f\"{NAME}.out.wav\", autoplay=True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pretrained models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/tree/main\n",
"!wget -N \"https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/resolve/main/riri/G_riri_220.pth\"\n",
"!wget -N \"https://huggingface.co/TachibanaKimika/so-vits-svc-4.0-models/resolve/main/riri/config.json\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!svc infer {NAME}.wav -c config.json -m G_riri_220.pth\n",
"display(Audio(f\"{NAME}.out.wav\", autoplay=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title https://huggingface.co/therealvul/so-vits-svc-4.0/tree/main\n",
"!wget -N \"https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Pinkie%20(speaking%20sep)/G_166400.pth\"\n",
"!wget -N \"https://huggingface.co/therealvul/so-vits-svc-4.0/resolve/main/Pinkie%20(speaking%20sep)/config.json\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!svc infer {NAME}.wav --speaker \"Pinkie {neutral}\" -c config.json -m G_166400.pth\n",
"display(Audio(f\"{NAME}.out.wav\", autoplay=True))"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
voice_conversion/so-vits-svc-paul-v2/poetry.lock
Generated
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voice_conversion/so-vits-svc-paul-v2/pyproject.toml
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[tool.poetry]
name = "so-vits-svc-fork"
version = "3.12.1"
description = "A fork of so-vits-svc."
authors = ["34j <34j.95a2p@simplelogin.com>"]
license = "MIT"
#readme = "README.md"
repository = "https://github.com/34j/so-vits-svc-fork"
documentation = "https://so-vits-svc-fork.readthedocs.io"
classifiers = [
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"Natural Language :: English",
"Operating System :: OS Independent",
"Topic :: Software Development :: Libraries",
]
packages = [
{ include = "so_vits_svc_fork", from = "src" },
]
[tool.poetry.scripts]
so-vits-svc-fork = "so_vits_svc_fork.__main__:cli"
svc = "so_vits_svc_fork.__main__:cli"
svcf = "so_vits_svc_fork.__main__:cli"
svcg = "so_vits_svc_fork.gui:main"
svc-gui = "so_vits_svc_fork.gui:main"
svcf-gui = "so_vits_svc_fork.gui:main"
[tool.poetry.urls]
"Bug Tracker" = "https://github.com/34j/so-vits-svc-fork/issues"
"Changelog" = "https://github.com/34j/so-vits-svc-fork/blob/main/CHANGELOG.md"
[tool.poetry.dependencies]
python = ">=3.8,<3.11"
librosa = "0.9.1"
numpy = "1.23.5"
pydub = "0.25.1"
pyworld = "0.2.12"
scipy = "1.8.0"
tqdm = "4.65.0"
scikit-maad = "1.3.12"
onnx = "1.13.1"
onnxsim = "0.4.28"
onnxoptimizer = "0.3.10"
torch = "1.13.1"
torchaudio = "0.13.1"
tensorboard = "2.12.0"
rich = "13.3.5"
tqdm-joblib = "0.0.3"
tensorboardx = "2.6"
pyinputplus = "0.2.12"
cm-time = "0.1.2"
pysimplegui = "4.60.4"
pebble = "5.0.3"
torchcrepe = "0.0.18"
lightning = "2.0.2"
transformers = "^4.28.1"
matplotlib = "3.7.1"
[tool.poetry.group.dev.dependencies]
pre-commit = ">=3"
pytest = "^7.0"
pytest-cov = "^4.0.0"
[tool.poetry.group.docs]
optional = true
[tool.poetry.group.docs.dependencies]
myst-parser = ">=0.16"
sphinx = ">=4.0"
sphinx-rtd-theme = ">=1.0"
[tool.semantic_release]
branch = "main"
version_toml = "pyproject.toml:tool.poetry.version"
version_variable = "src/so_vits_svc_fork/__init__.py:__version__"
build_command = "pip install poetry && poetry build"
[tool.pytest.ini_options]
addopts = "-v -Wdefault --cov=so_vits_svc_fork --cov-report=term-missing:skip-covered"
pythonpath = ["src"]
[tool.coverage.run]
branch = true
[tool.coverage.report]
exclude_lines = [
"pragma: no cover",
"@overload",
"if TYPE_CHECKING",
"raise NotImplementedError",
'if __name__ == "__main__":',
]
[tool.isort]
profile = "black"
known_first_party = ["so_vits_svc_fork", "tests"]
[tool.autoflake]
remove_all_unused_imports = true
[tool.mypy]
check_untyped_defs = true
disallow_any_generics = true
disallow_incomplete_defs = true
disallow_untyped_defs = true
mypy_path = "src/"
no_implicit_optional = true
show_error_codes = true
warn_unreachable = true
warn_unused_ignores = true
exclude = [
'docs/.*',
'setup.py',
]
[[tool.mypy.overrides]]
module = "tests.*"
allow_untyped_defs = true
[[tool.mypy.overrides]]
module = "docs.*"
ignore_errors = true
[tool.bandit]
exclude_dirs = ["src"]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
voice_conversion/so-vits-svc-paul-v2/renovate.json
+3
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@@ -0,0 +1,3 @@
{
"extends": ["github>browniebroke/renovate-configs:python"]
}
voice_conversion/so-vits-svc-paul-v2/requirements.txt
+24
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@@ -0,0 +1,24 @@
librosa = "0.9.1"
numpy = "1.23.5"
pydub = "0.25.1"
pyworld = "0.2.12"
scipy = "1.8.0"
tqdm = "4.65.0"
scikit-maad = "1.3.12"
onnx = "1.13.1"
onnxsim = "0.4.28"
onnxoptimizer = "0.3.10"
torch = "1.13.1"
torchaudio = "0.13.1"
tensorboard = "2.12.0"
rich = "13.3.5"
tqdm-joblib = "0.0.3"
tensorboardx = "2.6"
pyinputplus = "0.2.12"
cm-time = "0.1.2"
pysimplegui = "4.60.4"
pebble = "5.0.3"
torchcrepe = "0.0.18"
lightning = "2.0.2"
transformers = "4.28.1"
matplotlib = "3.7.1"
voice_conversion/so-vits-svc-paul-v2/setup.py
+9
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@@ -0,0 +1,9 @@
#!/usr/bin/env python
# This is a shim to allow GitHub to detect the package, build is done with poetry
# Taken from https://github.com/Textualize/rich
import setuptools
if __name__ == "__main__":
setuptools.setup(name="so-vits-svc-fork")
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/__init__.py
+5
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@@ -0,0 +1,5 @@
__version__ = "3.12.1"
from .logger import init_logger
init_logger()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/__main__.py
+863
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@@ -0,0 +1,863 @@
from __future__ import annotations
import os
from logging import getLogger
from multiprocessing import freeze_support
from pathlib import Path
from typing import Literal
import click
import pyinputplus as pyip
import torch
from so_vits_svc_fork import __version__
from so_vits_svc_fork.utils import get_optimal_device
LOG = getLogger(__name__)
IS_TEST = "test" in Path(__file__).parent.stem
if IS_TEST:
LOG.debug("Test mode is on.")
class RichHelpFormatter(click.HelpFormatter):
def __init__(
self,
indent_increment: int = 2,
width: int | None = None,
max_width: int | None = None,
) -> None:
width = 100
super().__init__(indent_increment, width, max_width)
LOG.info(f"Version: {__version__}")
def patch_wrap_text():
orig_wrap_text = click.formatting.wrap_text
def wrap_text(
text,
width=78,
initial_indent="",
subsequent_indent="",
preserve_paragraphs=False,
):
return orig_wrap_text(
text.replace("\n", "\n\n"),
width=width,
initial_indent=initial_indent,
subsequent_indent=subsequent_indent,
preserve_paragraphs=True,
).replace("\n\n", "\n")
click.formatting.wrap_text = wrap_text
patch_wrap_text()
CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"], show_default=True)
click.Context.formatter_class = RichHelpFormatter
@click.group(context_settings=CONTEXT_SETTINGS)
def cli():
"""so-vits-svc allows any folder structure for training data.
However, the following folder structure is recommended.\n
When training: dataset_raw/{speaker_name}/**/{wav_name}.{any_format}\n
When inference: configs/44k/config.json, logs/44k/G_XXXX.pth\n
If the folder structure is followed, you DO NOT NEED TO SPECIFY model path, config path, etc.
(The latest model will be automatically loaded.)\n
To train a model, run pre-resample, pre-config, pre-hubert, train.\n
To infer a model, run infer.
"""
@cli.command()
@click.option(
"-c",
"--config-path",
type=click.Path(exists=True),
help="path to config",
default=Path("./configs/44k/config.json"),
)
@click.option(
"-m",
"--model-path",
type=click.Path(),
help="path to output dir",
default=Path("./logs/44k"),
)
@click.option(
"-t/-nt",
"--tensorboard/--no-tensorboard",
default=False,
type=bool,
help="launch tensorboard",
)
@click.option(
"-r",
"--reset-optimizer",
default=False,
type=bool,
help="reset optimizer",
is_flag=True,
)
def train(
config_path: Path,
model_path: Path,
tensorboard: bool = False,
reset_optimizer: bool = False,
):
"""Train model
If D_0.pth or G_0.pth not found, automatically download from hub."""
from .train import train
config_path = Path(config_path)
model_path = Path(model_path)
if tensorboard:
import webbrowser
from tensorboard import program
getLogger("tensorboard").setLevel(30)
tb = program.TensorBoard()
tb.configure(argv=[None, "--logdir", model_path.as_posix()])
url = tb.launch()
webbrowser.open(url)
train(
config_path=config_path, model_path=model_path, reset_optimizer=reset_optimizer
)
@cli.command()
@click.argument(
"input-path",
type=click.Path(exists=True),
)
@click.option(
"-o",
"--output-path",
type=click.Path(),
help="path to output dir",
)
@click.option("-s", "--speaker", type=str, default=None, help="speaker name")
@click.option(
"-m",
"--model-path",
type=click.Path(exists=True),
default=Path("./logs/44k/"),
help="path to model",
)
@click.option(
"-c",
"--config-path",
type=click.Path(exists=True),
default=Path("./configs/44k/config.json"),
help="path to config",
)
@click.option(
"-k",
"--cluster-model-path",
type=click.Path(exists=True),
default=None,
help="path to cluster model",
)
@click.option("-t", "--transpose", type=int, default=0, help="transpose")
@click.option(
"-db", "--db-thresh", type=int, default=-20, help="threshold (DB) (RELATIVE)"
)
@click.option(
"-fm",
"--f0-method",
type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
default="dio",
help="f0 prediction method",
)
@click.option(
"-a/-na",
"--auto-predict-f0/--no-auto-predict-f0",
type=bool,
default=False,
help="auto predict f0",
)
@click.option(
"-r", "--cluster-infer-ratio", type=float, default=0, help="cluster infer ratio"
)
@click.option("-n", "--noise-scale", type=float, default=0.4, help="noise scale")
@click.option("-p", "--pad-seconds", type=float, default=0.5, help="pad seconds")
@click.option(
"-d",
"--device",
type=str,
default=get_optimal_device(),
help="device",
)
@click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds")
@click.option(
"-ab/-nab",
"--absolute-thresh/--no-absolute-thresh",
type=bool,
default=False,
help="absolute thresh",
)
def infer(
# paths
input_path: Path,
output_path: Path,
model_path: Path,
config_path: Path,
# svc config
speaker: str,
cluster_model_path: Path | None = None,
transpose: int = 0,
auto_predict_f0: bool = False,
cluster_infer_ratio: float = 0,
noise_scale: float = 0.4,
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
# slice config
db_thresh: int = -40,
pad_seconds: float = 0.5,
chunk_seconds: float = 0.5,
absolute_thresh: bool = False,
device: str | torch.device = get_optimal_device(),
):
"""Inference"""
from so_vits_svc_fork.inference.main import infer
if not auto_predict_f0:
LOG.warning(
f"auto_predict_f0 = False, transpose = {transpose}. If you want to change the pitch, please set transpose."
"Generally transpose = 0 does not work because your voice pitch and target voice pitch are different."
)
input_path = Path(input_path)
if output_path is None:
output_path = input_path.parent / f"{input_path.stem}.out{input_path.suffix}"
output_path = Path(output_path)
model_path = Path(model_path)
if model_path.is_dir():
model_path = list(
sorted(model_path.glob("G_*.pth"), key=lambda x: x.stat().st_mtime)
)[-1]
LOG.info(f"Since model_path is a directory, use {model_path}")
config_path = Path(config_path)
if cluster_model_path is not None:
cluster_model_path = Path(cluster_model_path)
infer(
# paths
input_path=input_path,
output_path=output_path,
model_path=model_path,
config_path=config_path,
# svc config
speaker=speaker,
cluster_model_path=cluster_model_path,
transpose=transpose,
auto_predict_f0=auto_predict_f0,
cluster_infer_ratio=cluster_infer_ratio,
noise_scale=noise_scale,
f0_method=f0_method,
# slice config
db_thresh=db_thresh,
pad_seconds=pad_seconds,
chunk_seconds=chunk_seconds,
absolute_thresh=absolute_thresh,
device=device,
)
@cli.command()
@click.option(
"-m",
"--model-path",
type=click.Path(exists=True),
default=Path("./logs/44k/"),
help="path to model",
)
@click.option(
"-c",
"--config-path",
type=click.Path(exists=True),
default=Path("./configs/44k/config.json"),
help="path to config",
)
@click.option(
"-k",
"--cluster-model-path",
type=click.Path(exists=True),
default=None,
help="path to cluster model",
)
@click.option("-t", "--transpose", type=int, default=12, help="transpose")
@click.option(
"-a/-na",
"--auto-predict-f0/--no-auto-predict-f0",
type=bool,
default=True,
help="auto predict f0 (not recommended for realtime since voice pitch will not be stable)",
)
@click.option(
"-r", "--cluster-infer-ratio", type=float, default=0, help="cluster infer ratio"
)
@click.option("-n", "--noise-scale", type=float, default=0.4, help="noise scale")
@click.option(
"-db", "--db-thresh", type=int, default=-30, help="threshold (DB) (ABSOLUTE)"
)
@click.option(
"-fm",
"--f0-method",
type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
default="dio",
help="f0 prediction method",
)
@click.option("-p", "--pad-seconds", type=float, default=0.02, help="pad seconds")
@click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds")
@click.option(
"-cr",
"--crossfade-seconds",
type=float,
default=0.01,
help="crossfade seconds",
)
@click.option(
"-ab",
"--additional-infer-before-seconds",
type=float,
default=0.2,
help="additional infer before seconds",
)
@click.option(
"-aa",
"--additional-infer-after-seconds",
type=float,
default=0.1,
help="additional infer after seconds",
)
@click.option("-b", "--block-seconds", type=float, default=0.5, help="block seconds")
@click.option(
"-d",
"--device",
type=str,
default=get_optimal_device(),
help="device",
)
@click.option("-s", "--speaker", type=str, default=None, help="speaker name")
@click.option("-v", "--version", type=int, default=2, help="version")
@click.option("-i", "--input-device", type=int, default=None, help="input device")
@click.option("-o", "--output-device", type=int, default=None, help="output device")
@click.option(
"-po",
"--passthrough-original",
type=bool,
default=False,
is_flag=True,
help="passthrough original (for latency check)",
)
def vc(
# paths
model_path: Path,
config_path: Path,
# svc config
speaker: str,
cluster_model_path: Path | None,
transpose: int,
auto_predict_f0: bool,
cluster_infer_ratio: float,
noise_scale: float,
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
# slice config
db_thresh: int,
pad_seconds: float,
chunk_seconds: float,
# realtime config
crossfade_seconds: float,
additional_infer_before_seconds: float,
additional_infer_after_seconds: float,
block_seconds: float,
version: int,
input_device: int | str | None,
output_device: int | str | None,
device: torch.device,
passthrough_original: bool = False,
) -> None:
"""Realtime inference from microphone"""
from so_vits_svc_fork.inference.main import realtime
if auto_predict_f0:
LOG.warning(
"auto_predict_f0 = True in realtime inference will cause unstable voice pitch, use with caution"
)
else:
LOG.warning(
f"auto_predict_f0 = False, transpose = {transpose}. If you want to change the pitch, please change the transpose value."
"Generally transpose = 0 does not work because your voice pitch and target voice pitch are different."
)
model_path = Path(model_path)
config_path = Path(config_path)
if cluster_model_path is not None:
cluster_model_path = Path(cluster_model_path)
if model_path.is_dir():
model_path = list(
sorted(model_path.glob("G_*.pth"), key=lambda x: x.stat().st_mtime)
)[-1]
LOG.info(f"Since model_path is a directory, use {model_path}")
realtime(
# paths
model_path=model_path,
config_path=config_path,
# svc config
speaker=speaker,
cluster_model_path=cluster_model_path,
transpose=transpose,
auto_predict_f0=auto_predict_f0,
cluster_infer_ratio=cluster_infer_ratio,
noise_scale=noise_scale,
f0_method=f0_method,
# slice config
db_thresh=db_thresh,
pad_seconds=pad_seconds,
chunk_seconds=chunk_seconds,
# realtime config
crossfade_seconds=crossfade_seconds,
additional_infer_before_seconds=additional_infer_before_seconds,
additional_infer_after_seconds=additional_infer_after_seconds,
block_seconds=block_seconds,
version=version,
input_device=input_device,
output_device=output_device,
device=device,
passthrough_original=passthrough_original,
)
@cli.command()
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
default=Path("./dataset_raw"),
help="path to source dir",
)
@click.option(
"-o",
"--output-dir",
type=click.Path(),
default=Path("./dataset/44k"),
help="path to output dir",
)
@click.option("-s", "--sampling-rate", type=int, default=44100, help="sampling rate")
@click.option(
"-n",
"--n-jobs",
type=int,
default=-1,
help="number of jobs (optimal value may depend on your RAM capacity and audio duration per file)",
)
@click.option("-d", "--top-db", type=float, default=30, help="top db")
@click.option("-f", "--frame-seconds", type=float, default=1, help="frame seconds")
@click.option(
"-ho", "-hop", "--hop-seconds", type=float, default=0.3, help="hop seconds"
)
def pre_resample(
input_dir: Path,
output_dir: Path,
sampling_rate: int,
n_jobs: int,
top_db: int,
frame_seconds: float,
hop_seconds: float,
) -> None:
"""Preprocessing part 1: resample"""
from so_vits_svc_fork.preprocessing.preprocess_resample import preprocess_resample
input_dir = Path(input_dir)
output_dir = Path(output_dir)
preprocess_resample(
input_dir=input_dir,
output_dir=output_dir,
sampling_rate=sampling_rate,
n_jobs=n_jobs,
top_db=top_db,
frame_seconds=frame_seconds,
hop_seconds=hop_seconds,
)
from so_vits_svc_fork.preprocessing.preprocess_flist_config import CONFIG_TEMPLATE_DIR
@cli.command()
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
default=Path("./dataset/44k"),
help="path to source dir",
)
@click.option(
"-f",
"--filelist-path",
type=click.Path(),
default=Path("./filelists/44k"),
help="path to filelist dir",
)
@click.option(
"-c",
"--config-path",
type=click.Path(),
default=Path("./configs/44k/config.json"),
help="path to config",
)
@click.option(
"-t",
"--config-type",
type=click.Choice([x.stem for x in CONFIG_TEMPLATE_DIR.rglob("*.json")]),
default="so-vits-svc-4.0v1-legacy",
help="config type",
)
def pre_config(
input_dir: Path,
filelist_path: Path,
config_path: Path,
config_type: str,
):
"""Preprocessing part 2: config"""
from so_vits_svc_fork.preprocessing.preprocess_flist_config import preprocess_config
input_dir = Path(input_dir)
filelist_path = Path(filelist_path)
config_path = Path(config_path)
preprocess_config(
input_dir=input_dir,
train_list_path=filelist_path / "train.txt",
val_list_path=filelist_path / "val.txt",
test_list_path=filelist_path / "test.txt",
config_path=config_path,
config_name=config_type,
)
@cli.command()
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
default=Path("./dataset/44k"),
help="path to source dir",
)
@click.option(
"-c",
"--config-path",
type=click.Path(exists=True),
help="path to config",
default=Path("./configs/44k/config.json"),
)
@click.option(
"-n",
"--n-jobs",
type=int,
default=None,
help="number of jobs (optimal value may depend on your VRAM capacity and audio duration per file)",
)
@click.option(
"-f/-nf",
"--force-rebuild/--no-force-rebuild",
type=bool,
default=True,
help="force rebuild existing preprocessed files",
)
@click.option(
"-fm",
"--f0-method",
type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
default="dio",
)
def pre_hubert(
input_dir: Path,
config_path: Path,
n_jobs: bool,
force_rebuild: bool,
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
) -> None:
"""Preprocessing part 3: hubert
If the HuBERT model is not found, it will be downloaded automatically."""
from so_vits_svc_fork.preprocessing.preprocess_hubert_f0 import preprocess_hubert_f0
input_dir = Path(input_dir)
config_path = Path(config_path)
preprocess_hubert_f0(
input_dir=input_dir,
config_path=config_path,
n_jobs=n_jobs,
force_rebuild=force_rebuild,
f0_method=f0_method,
)
@cli.command()
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
default=Path("./dataset_raw_raw/"),
help="path to source dir",
)
@click.option(
"-o",
"--output-dir",
type=click.Path(),
default=Path("./dataset_raw/"),
help="path to output dir",
)
@click.option(
"-n",
"--n-jobs",
type=int,
default=-1,
help="number of jobs (optimal value may depend on your VRAM capacity and audio duration per file)",
)
@click.option("-min", "--min-speakers", type=int, default=2, help="min speakers")
@click.option("-max", "--max-speakers", type=int, default=2, help="max speakers")
@click.option(
"-t", "--huggingface-token", type=str, default=None, help="huggingface token"
)
@click.option("-s", "--sr", type=int, default=44100, help="sampling rate")
def pre_sd(
input_dir: Path | str,
output_dir: Path | str,
min_speakers: int,
max_speakers: int,
huggingface_token: str | None,
n_jobs: int,
sr: int,
):
"""Speech diarization using pyannote.audio"""
if huggingface_token is None:
huggingface_token = os.environ.get("HUGGINGFACE_TOKEN", None)
if huggingface_token is None:
huggingface_token = click.prompt(
"Please enter your HuggingFace token", hide_input=True
)
if os.environ.get("HUGGINGFACE_TOKEN", None) is None:
LOG.info("You can also set the HUGGINGFACE_TOKEN environment variable.")
assert huggingface_token is not None
huggingface_token = huggingface_token.rstrip(" \n\r\t\0")
if len(huggingface_token) <= 1:
raise ValueError("HuggingFace token is empty: " + huggingface_token)
if max_speakers == 1:
LOG.warning("Consider using pre-split if max_speakers == 1")
from so_vits_svc_fork.preprocessing.preprocess_speaker_diarization import (
preprocess_speaker_diarization,
)
preprocess_speaker_diarization(
input_dir=input_dir,
output_dir=output_dir,
min_speakers=min_speakers,
max_speakers=max_speakers,
huggingface_token=huggingface_token,
n_jobs=n_jobs,
sr=sr,
)
@cli.command()
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
default=Path("./dataset_raw_raw/"),
help="path to source dir",
)
@click.option(
"-o",
"--output-dir",
type=click.Path(),
default=Path("./dataset_raw/"),
help="path to output dir",
)
@click.option(
"-n",
"--n-jobs",
type=int,
default=-1,
help="number of jobs (optimal value may depend on your RAM capacity and audio duration per file)",
)
@click.option("-d", "--top-db", type=float, default=30, help="top db")
@click.option("-f", "--frame-seconds", type=float, default=1, help="frame seconds")
@click.option(
"-ho", "-hop", "--hop-seconds", type=float, default=0.3, help="hop seconds"
)
@click.option("-s", "--sr", type=int, default=44100, help="sample rate")
def pre_split(
input_dir: Path | str,
output_dir: Path | str,
top_db: int,
frame_seconds: float,
hop_seconds: float,
n_jobs: int,
sr: int,
):
"""Split audio files into multiple files"""
from so_vits_svc_fork.preprocessing.preprocess_split import preprocess_split
preprocess_split(
input_dir=input_dir,
output_dir=output_dir,
top_db=top_db,
frame_seconds=frame_seconds,
hop_seconds=hop_seconds,
n_jobs=n_jobs,
sr=sr,
)
@cli.command()
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
required=True,
help="path to source dir",
)
@click.option(
"-o",
"--output-dir",
type=click.Path(),
default=None,
help="path to output dir",
)
@click.option(
"-c/-nc",
"--create-new/--no-create-new",
type=bool,
default=True,
help="create a new folder for the speaker if not exist",
)
def pre_classify(
input_dir: Path | str,
output_dir: Path | str | None,
create_new: bool,
) -> None:
"""Classify multiple audio files into multiple files"""
from so_vits_svc_fork.preprocessing.preprocess_classify import preprocess_classify
if output_dir is None:
output_dir = input_dir
preprocess_classify(
input_dir=input_dir,
output_dir=output_dir,
create_new=create_new,
)
@cli.command
def clean():
"""Clean up files, only useful if you are using the default file structure"""
import shutil
folders = ["dataset", "filelists", "logs"]
if pyip.inputYesNo(f"Are you sure you want to delete files in {folders}?") == "yes":
for folder in folders:
if Path(folder).exists():
shutil.rmtree(folder)
LOG.info("Cleaned up files")
else:
LOG.info("Aborted")
@cli.command
@click.option(
"-i",
"--input-path",
type=click.Path(exists=True),
help="model path",
default=Path("./logs/44k/"),
)
@click.option(
"-o",
"--output-path",
type=click.Path(),
help="onnx model path to save",
default=None,
)
@click.option(
"-c",
"--config-path",
type=click.Path(),
help="config path",
default=Path("./configs/44k/config.json"),
)
@click.option(
"-d",
"--device",
type=str,
default="cpu",
help="device to use",
)
def onnx(
input_path: Path, output_path: Path, config_path: Path, device: torch.device | str
) -> None:
"""Export model to onnx (currently not working)"""
raise NotImplementedError("ONNX export is not yet supported")
input_path = Path(input_path)
if input_path.is_dir():
input_path = list(input_path.glob("*.pth"))[0]
if output_path is None:
output_path = input_path.with_suffix(".onnx")
output_path = Path(output_path)
if output_path.is_dir():
output_path = output_path / (input_path.stem + ".onnx")
config_path = Path(config_path)
device_ = torch.device(device)
from so_vits_svc_fork.modules.onnx._export import onnx_export
onnx_export(
input_path=input_path,
output_path=output_path,
config_path=config_path,
device=device_,
)
@cli.command
@click.option(
"-i",
"--input-dir",
type=click.Path(exists=True),
help="dataset directory",
default=Path("./dataset/44k"),
)
@click.option(
"-o",
"--output-path",
type=click.Path(),
help="model path to save",
default=Path("./logs/44k/kmeans.pt"),
)
@click.option("-n", "--n-clusters", type=int, help="number of clusters", default=2000)
@click.option(
"-m/-nm", "--minibatch/--no-minibatch", default=True, help="use minibatch k-means"
)
def train_cluster(
input_dir: Path, output_path: Path, n_clusters: int, minibatch: bool
) -> None:
"""Train k-means clustering"""
from .cluster.train_cluster import main
main(
input_dir=input_dir,
output_path=output_path,
n_clusters=n_clusters,
verbose=True,
use_minibatch=minibatch,
)
if __name__ == "__main__":
freeze_support()
cli()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/cluster/__init__.py
+48
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@@ -0,0 +1,48 @@
from __future__ import annotations
from pathlib import Path
from typing import Any
import torch
from sklearn.cluster import KMeans
def get_cluster_model(ckpt_path: Path | str):
with Path(ckpt_path).open("rb") as f:
checkpoint = torch.load(
f, map_location="cpu"
) # Danger of arbitrary code execution
kmeans_dict = {}
for spk, ckpt in checkpoint.items():
km = KMeans(ckpt["n_features_in_"])
km.__dict__["n_features_in_"] = ckpt["n_features_in_"]
km.__dict__["_n_threads"] = ckpt["_n_threads"]
km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"]
kmeans_dict[spk] = km
return kmeans_dict
def check_speaker(model: Any, speaker: Any):
if speaker not in model:
raise ValueError(f"Speaker {speaker} not in {list(model.keys())}")
def get_cluster_result(model: Any, x: Any, speaker: Any):
"""
x: np.array [t, 256]
return cluster class result
"""
check_speaker(model, speaker)
return model[speaker].predict(x)
def get_cluster_center_result(model: Any, x: Any, speaker: Any):
"""x: np.array [t, 256]"""
check_speaker(model, speaker)
predict = model[speaker].predict(x)
return model[speaker].cluster_centers_[predict]
def get_center(model: Any, x: Any, speaker: Any):
check_speaker(model, speaker)
return model[speaker].cluster_centers_[x]
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/cluster/train_cluster.py
+91
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@@ -0,0 +1,91 @@
from __future__ import annotations
from logging import getLogger
from pathlib import Path
from typing import Any
import numpy as np
import torch
from cm_time import timer
from joblib import Parallel, delayed
from sklearn.cluster import KMeans, MiniBatchKMeans
from tqdm_joblib import tqdm_joblib
LOG = getLogger(__name__)
def train_cluster(
input_dir: Path | str,
n_clusters: int,
use_minibatch: bool = True,
verbose: bool = False,
) -> dict:
input_dir = Path(input_dir)
LOG.info(f"Loading features from {input_dir}")
features = []
for path in input_dir.rglob("*.data.pt"):
with path.open("rb") as f:
features.append(
torch.load(f, weights_only=True)["content"].squeeze(0).numpy().T
)
if not features:
raise ValueError(f"No features found in {input_dir}")
features = np.concatenate(features, axis=0).astype(np.float32)
if features.shape[0] < n_clusters:
raise ValueError(
"Too few HuBERT features to cluster. Consider using a smaller number of clusters."
)
LOG.info(
f"shape: {features.shape}, size: {features.nbytes/1024**2:.2f} MB, dtype: {features.dtype}"
)
with timer() as t:
if use_minibatch:
kmeans = MiniBatchKMeans(
n_clusters=n_clusters,
verbose=verbose,
batch_size=4096,
max_iter=80,
n_init="auto",
).fit(features)
else:
kmeans = KMeans(n_clusters=n_clusters, verbose=verbose, n_init="auto").fit(
features
)
LOG.info(f"Clustering took {t.elapsed:.2f} seconds")
x = {
"n_features_in_": kmeans.n_features_in_,
"_n_threads": kmeans._n_threads,
"cluster_centers_": kmeans.cluster_centers_,
}
return x
def main(
input_dir: Path | str,
output_path: Path | str,
n_clusters: int = 10000,
use_minibatch: bool = True,
verbose: bool = False,
) -> None:
input_dir = Path(input_dir)
output_path = Path(output_path)
def train_cluster_(input_path: Path, **kwargs: Any) -> tuple[str, dict]:
return input_path.stem, train_cluster(input_path, **kwargs)
with tqdm_joblib(desc="Training clusters", total=len(list(input_dir.iterdir()))):
parallel_result = Parallel(n_jobs=-1)(
delayed(train_cluster_)(
speaker_name,
n_clusters=n_clusters,
use_minibatch=use_minibatch,
verbose=verbose,
)
for speaker_name in input_dir.iterdir()
)
assert parallel_result is not None
checkpoint = dict(parallel_result)
output_path.parent.mkdir(exist_ok=True, parents=True)
with output_path.open("wb") as f:
torch.save(checkpoint, f)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/dataset.py
+87
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@@ -0,0 +1,87 @@
from __future__ import annotations
from pathlib import Path
from random import Random
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from .hparams import HParams
class TextAudioDataset(Dataset):
def __init__(self, hps: HParams, is_validation: bool = False):
self.datapaths = [
Path(x).parent / (Path(x).name + ".data.pt")
for x in Path(
hps.data.validation_files if is_validation else hps.data.training_files
)
.read_text("utf-8")
.splitlines()
]
self.hps = hps
self.random = Random(hps.train.seed)
self.random.shuffle(self.datapaths)
self.max_spec_len = 800
def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
with Path(self.datapaths[index]).open("rb") as f:
data = torch.load(f, weights_only=True, map_location="cpu")
# cut long data randomly
spec_len = data["mel_spec"].shape[1]
hop_len = self.hps.data.hop_length
if spec_len > self.max_spec_len:
start = self.random.randint(0, spec_len - self.max_spec_len)
end = start + self.max_spec_len - 10
for key in data.keys():
if key == "audio":
data[key] = data[key][:, start * hop_len : end * hop_len]
elif key == "spk":
continue
else:
data[key] = data[key][..., start:end]
torch.cuda.empty_cache()
return data
def __len__(self) -> int:
return len(self.datapaths)
def _pad_stack(array: Sequence[torch.Tensor]) -> torch.Tensor:
max_idx = torch.argmax(torch.tensor([x_.shape[-1] for x_ in array]))
max_x = array[max_idx]
x_padded = [
F.pad(x_, (0, max_x.shape[-1] - x_.shape[-1]), mode="constant", value=0)
for x_ in array
]
return torch.stack(x_padded)
class TextAudioCollate(nn.Module):
def forward(
self, batch: Sequence[dict[str, torch.Tensor]]
) -> tuple[torch.Tensor, ...]:
batch = [b for b in batch if b is not None]
batch = list(sorted(batch, key=lambda x: x["mel_spec"].shape[1], reverse=True))
lengths = torch.tensor([b["mel_spec"].shape[1] for b in batch]).long()
results = {}
for key in batch[0].keys():
if key not in ["spk"]:
results[key] = _pad_stack([b[key] for b in batch]).cpu()
else:
results[key] = torch.tensor([[b[key]] for b in batch]).cpu()
return (
results["content"],
results["f0"],
results["spec"],
results["mel_spec"],
results["audio"],
results["spk"],
lengths,
results["uv"],
)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/default_gui_presets.json
+87
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@@ -0,0 +1,87 @@
{
"Default VC (GPU, GTX 1060)": {
"silence_threshold": -35.0,
"transpose": 12.0,
"auto_predict_f0": false,
"f0_method": "dio",
"cluster_infer_ratio": 0.0,
"noise_scale": 0.4,
"pad_seconds": 0.1,
"chunk_seconds": 0.5,
"absolute_thresh": true,
"crossfade_seconds": 0.05,
"block_seconds": 0.35,
"additional_infer_before_seconds": 0.15,
"additional_infer_after_seconds": 0.1,
"realtime_algorithm": "1 (Divide constantly)",
"passthrough_original": false,
"use_gpu": true
},
"Default VC (CPU)": {
"silence_threshold": -35.0,
"transpose": 12.0,
"auto_predict_f0": false,
"f0_method": "dio",
"cluster_infer_ratio": 0.0,
"noise_scale": 0.4,
"pad_seconds": 0.1,
"chunk_seconds": 0.5,
"absolute_thresh": true,
"crossfade_seconds": 0.05,
"block_seconds": 1.5,
"additional_infer_before_seconds": 0.01,
"additional_infer_after_seconds": 0.01,
"realtime_algorithm": "1 (Divide constantly)",
"passthrough_original": false,
"use_gpu": false
},
"Default VC (Mobile CPU)": {
"silence_threshold": -35.0,
"transpose": 12.0,
"auto_predict_f0": false,
"f0_method": "dio",
"cluster_infer_ratio": 0.0,
"noise_scale": 0.4,
"pad_seconds": 0.1,
"chunk_seconds": 0.5,
"absolute_thresh": true,
"crossfade_seconds": 0.05,
"block_seconds": 2.5,
"additional_infer_before_seconds": 0.01,
"additional_infer_after_seconds": 0.01,
"realtime_algorithm": "1 (Divide constantly)",
"passthrough_original": false,
"use_gpu": false
},
"Default VC (Crooning)": {
"silence_threshold": -35.0,
"transpose": 12.0,
"auto_predict_f0": false,
"f0_method": "dio",
"cluster_infer_ratio": 0.0,
"noise_scale": 0.4,
"pad_seconds": 0.1,
"chunk_seconds": 0.5,
"absolute_thresh": true,
"crossfade_seconds": 0.04,
"block_seconds": 0.15,
"additional_infer_before_seconds": 0.05,
"additional_infer_after_seconds": 0.05,
"realtime_algorithm": "1 (Divide constantly)",
"passthrough_original": false,
"use_gpu": true
},
"Default File": {
"silence_threshold": -35.0,
"transpose": 0.0,
"auto_predict_f0": true,
"f0_method": "crepe",
"cluster_infer_ratio": 0.0,
"noise_scale": 0.4,
"pad_seconds": 0.1,
"chunk_seconds": 0.5,
"absolute_thresh": true,
"auto_play": true,
"passthrough_original": false
}
}
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/f0.py
+239
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from __future__ import annotations
from logging import getLogger
from typing import Any, Literal
import numpy as np
import torch
import torchcrepe
from cm_time import timer
from numpy import dtype, float32, ndarray
from torch import FloatTensor, Tensor
from so_vits_svc_fork.utils import get_optimal_device
LOG = getLogger(__name__)
def normalize_f0(
f0: FloatTensor, x_mask: FloatTensor, uv: FloatTensor, random_scale=True
) -> FloatTensor:
# calculate means based on x_mask
uv_sum = torch.sum(uv, dim=1, keepdim=True)
uv_sum[uv_sum == 0] = 9999
means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
if random_scale:
factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
else:
factor = torch.ones(f0.shape[0], 1).to(f0.device)
# normalize f0 based on means and factor
f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
if torch.isnan(f0_norm).any():
exit(0)
return f0_norm * x_mask
def interpolate_f0(
f0: ndarray[Any, dtype[float32]]
) -> tuple[ndarray[Any, dtype[float32]], ndarray[Any, dtype[float32]]]:
data = np.reshape(f0, (f0.size, 1))
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
vuv_vector[data > 0.0] = 1.0
vuv_vector[data <= 0.0] = 0.0
ip_data = data
frame_number = data.size
last_value = 0.0
for i in range(frame_number):
if data[i] <= 0.0:
j = i + 1
for j in range(i + 1, frame_number):
if data[j] > 0.0:
break
if j < frame_number - 1:
if last_value > 0.0:
step = (data[j] - data[i - 1]) / float(j - i)
for k in range(i, j):
ip_data[k] = data[i - 1] + step * (k - i + 1)
else:
for k in range(i, j):
ip_data[k] = data[j]
else:
for k in range(i, frame_number):
ip_data[k] = last_value
else:
ip_data[i] = data[i]
last_value = data[i]
return ip_data[:, 0], vuv_vector[:, 0]
def compute_f0_parselmouth(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
):
import parselmouth
x = wav_numpy
if p_len is None:
p_len = x.shape[0] // hop_length
else:
assert abs(p_len - x.shape[0] // hop_length) < 4, "pad length error"
time_step = hop_length / sampling_rate * 1000
f0_min = 50
f0_max = 1100
f0 = (
parselmouth.Sound(x, sampling_rate)
.to_pitch_ac(
time_step=time_step / 1000,
voicing_threshold=0.6,
pitch_floor=f0_min,
pitch_ceiling=f0_max,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
return f0
def _resize_f0(
x: ndarray[Any, dtype[float32]], target_len: int
) -> ndarray[Any, dtype[float32]]:
source = np.array(x)
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * target_len, len(source)) / target_len,
np.arange(0, len(source)),
source,
)
res = np.nan_to_num(target)
return res
def compute_f0_pyworld(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
type_: Literal["dio", "harvest"] = "dio",
):
import pyworld
if p_len is None:
p_len = wav_numpy.shape[0] // hop_length
if type_ == "dio":
f0, t = pyworld.dio(
wav_numpy.astype(np.double),
fs=sampling_rate,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=1000 * hop_length / sampling_rate,
)
elif type_ == "harvest":
f0, t = pyworld.harvest(
wav_numpy.astype(np.double),
fs=sampling_rate,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=1000 * hop_length / sampling_rate,
)
f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
for index, pitch in enumerate(f0):
f0[index] = round(pitch, 1)
return _resize_f0(f0, p_len)
def compute_f0_crepe(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
device: str | torch.device = get_optimal_device(),
model: Literal["full", "tiny"] = "full",
):
audio = torch.from_numpy(wav_numpy).to(device, copy=True)
audio = torch.unsqueeze(audio, dim=0)
if audio.ndim == 2 and audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True).detach()
# (T) -> (1, T)
audio = audio.detach()
pitch: Tensor = torchcrepe.predict(
audio,
sampling_rate,
hop_length,
f0_min,
f0_max,
model,
batch_size=hop_length * 2,
device=device,
pad=True,
)
f0 = pitch.squeeze(0).cpu().float().numpy()
p_len = p_len or wav_numpy.shape[0] // hop_length
f0 = _resize_f0(f0, p_len)
return f0
def compute_f0(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
**kwargs,
):
with timer() as t:
wav_numpy = wav_numpy.astype(np.float32)
wav_numpy /= np.quantile(np.abs(wav_numpy), 0.999)
if method in ["dio", "harvest"]:
f0 = compute_f0_pyworld(wav_numpy, p_len, sampling_rate, hop_length, method)
elif method == "crepe":
f0 = compute_f0_crepe(wav_numpy, p_len, sampling_rate, hop_length, **kwargs)
elif method == "crepe-tiny":
f0 = compute_f0_crepe(
wav_numpy, p_len, sampling_rate, hop_length, model="tiny", **kwargs
)
elif method == "parselmouth":
f0 = compute_f0_parselmouth(wav_numpy, p_len, sampling_rate, hop_length)
else:
raise ValueError(
"type must be dio, crepe, crepe-tiny, harvest or parselmouth"
)
rtf = t.elapsed / (len(wav_numpy) / sampling_rate)
LOG.info(f"F0 inference time: {t.elapsed:.3f}s, RTF: {rtf:.3f}")
return f0
def f0_to_coarse(f0: torch.Tensor | float):
is_torch = isinstance(f0, torch.Tensor)
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
f0_coarse.max(),
f0_coarse.min(),
)
return f0_coarse
f0_bin = 256
f0_max = 1100.0
f0_min = 50.0
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/fakeyou_infer.py
+612
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@@ -0,0 +1,612 @@
from __future__ import annotations
import sys
#sys.path.append(".")
#sys.path.append("/models/voice_conversion/so-vits-svc/src/so_vits_svc_fork")
import os
from logging import (
DEBUG,
INFO,
FileHandler,
StreamHandler,
basicConfig,
captureWarnings,
getLogger,
)
from pathlib import Path
from typing import Literal
import click
import pyinputplus as pyip
import torch
from rich.logging import RichHandler
print("Env vars:")
print(os.environ)
def print_gpu_info():
print('========================================')
print('Python interpreter', sys.executable)
print('PyTorch version', torch.__version__)
print('CUDA Available?', torch.cuda.is_available())
print('CUDA Device count', torch.cuda.device_count())
print('CUDA architectures library was compiled for', torch.cuda.get_arch_list())
#try:
# from tensorflow.python.client import device_lib
# print('local devices', str(device_lib.list_local_devices()).replace("\n", "\n "))
#except ImportError:
# print('no tensorflow - cannot list devices')
# pass
print('========================================', flush=True)
print_gpu_info()
def init_logger() -> None:
IN_COLAB = os.getenv("COLAB_RELEASE_TAG")
IS_TEST = "test" in Path(__file__).parent.stem
basicConfig(
level=DEBUG if IS_TEST else INFO,
format="%(asctime)s %(message)s",
datefmt="[%X]",
handlers=[
RichHandler() if not IN_COLAB else StreamHandler(),
FileHandler(f"{__name__.split('.')[0]}.log"),
],
)
captureWarnings(True)
if IS_TEST:
LOG.debug("Test mode is on.")
init_logger()
LOG = getLogger(__name__)
class RichHelpFormatter(click.HelpFormatter):
def __init__(
self,
indent_increment: int = 2,
width: int | None = None,
max_width: int | None = None,
) -> None:
width = 100
super().__init__(indent_increment, width, max_width)
def patch_wrap_text():
orig_wrap_text = click.formatting.wrap_text
def wrap_text(
text,
width=78,
initial_indent="",
subsequent_indent="",
preserve_paragraphs=False,
):
return orig_wrap_text(
text.replace("\n", "\n\n"),
width=width,
initial_indent=initial_indent,
subsequent_indent=subsequent_indent,
preserve_paragraphs=True,
).replace("\n\n", "\n")
click.formatting.wrap_text = wrap_text
patch_wrap_text()
CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"], show_default=True)
click.Context.formatter_class = RichHelpFormatter
@click.group(context_settings=CONTEXT_SETTINGS)
def cli():
"""so-vits-svc allows any folder structure for training data.
However, the following folder structure is recommended.\n
When training: dataset_raw/{speaker_name}/**/{wav_name}.{any_format}\n
When inference: configs/44k/config.json, logs/44k/G_XXXX.pth\n
If the folder structure is followed, you DO NOT NEED TO SPECIFY model path, config path, etc.
(The latest model will be automatically loaded.)\n
To train a model, run pre-resample, pre-config, pre-hubert, train.\n
To infer a model, run infer.
"""
init_logger()
#@cli.command()
#@click.option(
# "-c",
# "--config-path",
# type=click.Path(exists=True),
# help="path to config",
# default=Path("./configs/44k/config.json"),
#)
#@click.option(
# "-m",
# "--model-path",
# type=click.Path(),
# help="path to output dir",
# default=Path("./logs/44k"),
#)
#def train(config_path: Path, model_path: Path):
# """Train model
# If D_0.pth or G_0.pth not found, automatically download from hub."""
# from .train import train
#
# config_path = Path(config_path)
# model_path = Path(model_path)
# train(config_path=config_path, model_path=model_path)
@cli.command()
@click.argument(
"input-path",
type=click.Path(exists=True),
)
@click.option(
"-o",
"--output-path",
type=click.Path(),
help="path to output dir",
)
@click.option("-s", "--speaker", type=str, default=None, help="speaker name")
@click.option(
"-m",
"--model-path",
type=click.Path(exists=True),
default=Path("./logs/44k/"),
help="path to model",
)
@click.option(
"-c",
"--config-path",
type=click.Path(exists=True),
default=Path("./configs/44k/config.json"),
help="path to config",
)
@click.option(
"-k",
"--cluster-model-path",
type=click.Path(exists=True),
default=None,
help="path to cluster model",
)
@click.option("-t", "--transpose", type=int, default=0, help="transpose")
@click.option(
"-db", "--db-thresh", type=int, default=-20, help="threshold (DB) (RELATIVE)"
)
@click.option(
"-a", "--auto-predict-f0", type=bool, default=True, help="auto predict f0"
)
@click.option(
"-r", "--cluster-infer-ratio", type=float, default=0, help="cluster infer ratio"
)
@click.option("-n", "--noise-scale", type=float, default=0.4, help="noise scale")
@click.option("-p", "--pad-seconds", type=float, default=0.5, help="pad seconds")
@click.option(
"-d",
"--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="device",
)
@click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds")
@click.option(
"-ab", "--absolute-thresh", type=bool, default=False, help="absolute thresh"
)
def infer(
input_path: Path,
output_path: Path,
speaker: str,
model_path: Path,
config_path: Path,
cluster_model_path: Path | None = None,
transpose: int = 0,
db_thresh: int = -40,
auto_predict_f0: bool = False,
cluster_infer_ratio: float = 0,
noise_scale: float = 0.4,
pad_seconds: float = 0.5,
chunk_seconds: float = 0.5,
absolute_thresh: bool = False,
device: Literal["cpu", "cuda"] = "cuda" if torch.cuda.is_available() else "cpu",
):
"""Inference"""
from .inference_main import infer
#from inference_main import infer
if not auto_predict_f0:
LOG.warning(
f"auto_predict_f0 = False, transpose = {transpose}. If you want to change the pitch, please set transpose."
"Generally transpose = 0 does not work because your voice pitch and target voice pitch are different."
)
input_path = Path(input_path)
if output_path is None:
output_path = input_path.parent / f"{input_path.stem}.out{input_path.suffix}"
output_path = Path(output_path)
model_path = Path(model_path)
if model_path.is_dir():
model_path = list(sorted(model_path.glob("*.pth")))[-1]
LOG.info(f"Since model_path is a directory, use {model_path}")
config_path = Path(config_path)
if cluster_model_path is not None:
cluster_model_path = Path(cluster_model_path)
LOG.info("Calling infer()...")
infer(
input_path=input_path,
output_path=output_path,
speaker=speaker,
model_path=model_path,
config_path=config_path,
cluster_model_path=cluster_model_path,
transpose=transpose,
db_thresh=db_thresh,
auto_predict_f0=auto_predict_f0,
cluster_infer_ratio=cluster_infer_ratio,
noise_scale=noise_scale,
pad_seconds=pad_seconds,
chunk_seconds=chunk_seconds,
absolute_thresh=absolute_thresh,
device=device,
)
#@cli.command()
#@click.option(
# "-m",
# "--model-path",
# type=click.Path(exists=True),
# default=Path("./logs/44k/"),
# help="path to model",
#)
#@click.option(
# "-c",
# "--config-path",
# type=click.Path(exists=True),
# default=Path("./configs/44k/config.json"),
# help="path to config",
#)
#@click.option(
# "-k",
# "--cluster-model-path",
# type=click.Path(exists=True),
# default=None,
# help="path to cluster model",
#)
#@click.option("-t", "--transpose", type=int, default=12, help="transpose")
#@click.option(
# "-a",
# "--auto-predict-f0",
# type=bool,
# default=False,
# help="auto predict f0 (not recommended for realtime since voice pitch will not be stable)",
#)
#@click.option(
# "-r", "--cluster-infer-ratio", type=float, default=0, help="cluster infer ratio"
#)
#@click.option("-n", "--noise-scale", type=float, default=0.4, help="noise scale")
#@click.option(
# "-db", "--db-thresh", type=int, default=-30, help="threshold (DB) (ABSOLUTE)"
#)
#@click.option(
# "-fm",
# "--f0-method",
# type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
# default="crepe",
# help="f0 prediction method",
#)
#@click.option("-p", "--pad-seconds", type=float, default=0.02, help="pad seconds")
#@click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds")
#@click.option(
# "-cr",
# "--crossfade-seconds",
# type=float,
# default=0.01,
# help="crossfade seconds",
#)
#@click.option(
# "-ab",
# "--additional-infer-before-seconds",
# type=float,
# default=0.2,
# help="additional infer before seconds",
#)
#@click.option(
# "-aa",
# "--additional-infer-after-seconds",
# type=float,
# default=0.1,
# help="additional infer after seconds",
#)
#@click.option("-b", "--block-seconds", type=float, default=0.5, help="block seconds")
#@click.option(
# "-d",
# "--device",
# type=str,
# default="cuda" if torch.cuda.is_available() else "cpu",
# help="device",
#)
#@click.option("-s", "--speaker", type=str, default=None, help="speaker name")
#@click.option("-v", "--version", type=int, default=2, help="version")
#@click.option("-i", "--input-device", type=int, default=None, help="input device")
#@click.option("-o", "--output-device", type=int, default=None, help="output device")
#@click.option(
# "-po",
# "--passthrough-original",
# type=bool,
# default=False,
# is_flag=True,
# help="passthrough original (for latency check)",
#)
#def vc(
# # paths
# model_path: Path,
# config_path: Path,
# # svc config
# speaker: str,
# cluster_model_path: Path | None,
# transpose: int,
# auto_predict_f0: bool,
# cluster_infer_ratio: float,
# noise_scale: float,
# f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
# # slice config
# db_thresh: int,
# pad_seconds: float,
# chunk_seconds: float,
# # realtime config
# crossfade_seconds: float,
# additional_infer_before_seconds: float,
# additional_infer_after_seconds: float,
# block_seconds: float,
# version: int,
# input_device: int | str | None,
# output_device: int | str | None,
# device: Literal["cpu", "cuda"],
# passthrough_original: bool = False,
#) -> None:
# """Realtime inference from microphone"""
# from .inference_main import realtime
#
# if auto_predict_f0:
# LOG.warning(
# "auto_predict_f0 = True in realtime inference will cause unstable voice pitch, use with caution"
# )
# else:
# LOG.warning(
# f"auto_predict_f0 = False, transpose = {transpose}. If you want to change the pitch, please change the transpose value."
# "Generally transpose = 0 does not work because your voice pitch and target voice pitch are different."
# )
# model_path = Path(model_path)
# config_path = Path(config_path)
# if cluster_model_path is not None:
# cluster_model_path = Path(cluster_model_path)
# if model_path.is_dir():
# model_path = list(sorted(model_path.glob("*.pth")))[-1]
# LOG.info(f"Since model_path is a directory, use {model_path}")
#
# realtime(
# # paths
# model_path=model_path,
# config_path=config_path,
# # svc config
# speaker=speaker,
# cluster_model_path=cluster_model_path,
# transpose=transpose,
# auto_predict_f0=auto_predict_f0,
# cluster_infer_ratio=cluster_infer_ratio,
# noise_scale=noise_scale,
# f0_method=f0_method,
# # slice config
# db_thresh=db_thresh,
# pad_seconds=pad_seconds,
# chunk_seconds=chunk_seconds,
# # realtime config
# crossfade_seconds=crossfade_seconds,
# additional_infer_before_seconds=additional_infer_before_seconds,
# additional_infer_after_seconds=additional_infer_after_seconds,
# block_seconds=block_seconds,
# version=version,
# input_device=input_device,
# output_device=output_device,
# device=device,
# passthrough_original=passthrough_original,
# )
#@cli.command()
#@click.option(
# "-i",
# "--input-dir",
# type=click.Path(exists=True),
# default=Path("./dataset_raw"),
# help="path to source dir",
#)
#@click.option(
# "-o",
# "--output-dir",
# type=click.Path(),
# default=Path("./dataset/44k"),
# help="path to output dir",
#)
#@click.option("-s", "--sampling-rate", type=int, default=44100, help="sampling rate")
#def pre_resample(input_dir: Path, output_dir: Path, sampling_rate: int) -> None:
# """Preprocessing part 1: resample"""
# from .preprocess_resample import preprocess_resample
#
# input_dir = Path(input_dir)
# output_dir = Path(output_dir)
# preprocess_resample(
# input_dir=input_dir, output_dir=output_dir, sampling_rate=sampling_rate
# )
#@cli.command()
#@click.option(
# "-i",
# "--input-dir",
# type=click.Path(exists=True),
# default=Path("./dataset/44k"),
# help="path to source dir",
#)
#@click.option(
# "-f",
# "--filelist-path",
# type=click.Path(),
# default=Path("./filelists/44k"),
# help="path to filelist dir",
#)
#@click.option(
# "-c",
# "--config-path",
# type=click.Path(),
# default=Path("./configs/44k/config.json"),
# help="path to config",
#)
#def pre_config(
# input_dir: Path,
# filelist_path: Path,
# config_path: Path,
#):
# """Preprocessing part 2: config"""
# from .preprocess_flist_config import preprocess_config
#
# input_dir = Path(input_dir)
# filelist_path = Path(filelist_path)
# config_path = Path(config_path)
# preprocess_config(
# input_dir=input_dir,
# train_list_path=filelist_path / "train.txt",
# val_list_path=filelist_path / "val.txt",
# test_list_path=filelist_path / "test.txt",
# config_path=config_path,
# )
#@cli.command()
#@click.option(
# "-i",
# "--input-dir",
# type=click.Path(exists=True),
# default=Path("./dataset/44k"),
# help="path to source dir",
#)
#@click.option(
# "-c",
# "--config-path",
# type=click.Path(exists=True),
# help="path to config",
# default=Path("./configs/44k/config.json"),
#)
#@click.option(
# "-n",
# "--n_jobs",
# type=int,
# default=4,
# help="number of jobs (optimal value may depend on your VRAM capacity and audio duration per file)",
#)
#@click.option(
# "-f",
# "--force-rebuild",
# type=bool,
# default=True,
# help="force rebuild existing preprocessed files",
#)
#@click.option(
# "-fm",
# "--f0-method",
# type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
# default="dio",
#)
#def pre_hubert(
# input_dir: Path,
# config_path: Path,
# n_jobs: bool,
# force_rebuild: bool,
# f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
#) -> None:
# """Preprocessing part 3: hubert
# If the HuBERT model is not found, it will be downloaded automatically."""
# from .preprocess_hubert_f0 import preprocess_hubert_f0
#
# input_dir = Path(input_dir)
# config_path = Path(config_path)
# preprocess_hubert_f0(
# input_dir=input_dir,
# config_path=config_path,
# n_jobs=n_jobs,
# force_rebuild=force_rebuild,
# f0_method=f0_method,
# )
#@cli.command
#def clean():
# """Clean up files, only useful if you are using the default file structure"""
# import shutil
#
# folders = ["dataset", "filelists", "logs"]
# if pyip.inputYesNo(f"Are you sure you want to delete files in {folders}?") == "yes":
# for folder in folders:
# if Path(folder).exists():
# shutil.rmtree(folder)
# LOG.info("Cleaned up files")
# else:
# LOG.info("Aborted")
#@cli.command
#@click.option("-i", "--input-path", type=click.Path(exists=True), help="model path")
#@click.option("-o", "--output-path", type=click.Path(), help="onnx model path to save")
#@click.option("-c", "--config-path", type=click.Path(), help="config path")
#@click.option("-d", "--device", type=str, default="cpu", help="torch device")
#def onnx(input_path: Path, output_path: Path, config_path: Path, device: str) -> None:
# """Export model to onnx"""
# input_path = Path(input_path)
# output_path = Path(output_path)
# config_path = Path(config_path)
# device_ = torch.device(device)
# from .onnx_export import onnx_export
#
# onnx_export(
# input_path=input_path,
# output_path=output_path,
# config_path=config_path,
# device=device_,
# )
#@cli.command
#@click.option(
# "-i",
# "--input-dir",
# type=click.Path(exists=True),
# help="dataset directory",
# default=Path("./dataset/44k"),
#)
#@click.option(
# "-o",
# "--output-path",
# type=click.Path(),
# help="model path to save",
# default=Path("./models/44k/kmeans.pt"),
#)
#@click.option("-n", "--n-clusters", type=int, help="number of clusters", default=10000)
#def train_cluster(input_dir: Path, output_path: Path, n_clusters: int) -> None:
# """Train k-means clustering"""
# from .cluster.train_cluster import main
#
# main(input_dir=input_dir, output_path=output_path, n_clusters=n_clusters)
if __name__ == '__main__':
infer()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/gui.py
+746
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@@ -0,0 +1,746 @@
from __future__ import annotations
import json
import multiprocessing
from copy import copy
from logging import getLogger
from pathlib import Path
import PySimpleGUI as sg
import sounddevice as sd
import soundfile as sf
import torch
from pebble import ProcessFuture, ProcessPool
from . import __version__
from .utils import get_optimal_device
GUI_DEFAULT_PRESETS_PATH = Path(__file__).parent / "default_gui_presets.json"
GUI_PRESETS_PATH = Path("./user_gui_presets.json").absolute()
LOG = getLogger(__name__)
def play_audio(path: Path | str):
if isinstance(path, Path):
path = path.as_posix()
data, sr = sf.read(path)
sd.play(data, sr)
def load_presets() -> dict:
defaults = json.loads(GUI_DEFAULT_PRESETS_PATH.read_text("utf-8"))
users = (
json.loads(GUI_PRESETS_PATH.read_text("utf-8"))
if GUI_PRESETS_PATH.exists()
else {}
)
# prioriy: defaults > users
# order: defaults -> users
return {**defaults, **users, **defaults}
def add_preset(name: str, preset: dict) -> dict:
presets = load_presets()
presets[name] = preset
with GUI_PRESETS_PATH.open("w") as f:
json.dump(presets, f, indent=2)
return load_presets()
def delete_preset(name: str) -> dict:
presets = load_presets()
if name in presets:
del presets[name]
else:
LOG.warning(f"Cannot delete preset {name} because it does not exist.")
with GUI_PRESETS_PATH.open("w") as f:
json.dump(presets, f, indent=2)
return load_presets()
def get_output_path(input_path: Path) -> Path:
# Default output path
output_path = input_path.parent / f"{input_path.stem}.out{input_path.suffix}"
# Increment file number in path if output file already exists
file_num = 1
while output_path.exists():
output_path = (
input_path.parent / f"{input_path.stem}.out_{file_num}{input_path.suffix}"
)
file_num += 1
return output_path
def get_supported_file_types() -> tuple[tuple[str], ...]:
return tuple(
[
((extension, f".{extension.lower()}"))
for extension in sf.available_formats().keys()
]
)
def validate_output_file_type(output_path: Path) -> bool:
supported_file_types = sorted(
[f".{extension.lower()}" for extension in sf.available_formats().keys()]
)
if not output_path.suffix:
sg.popup_ok(
"Error: Output path missing file type extension, enter "
+ "one of the following manually:\n\n"
+ "\n".join(supported_file_types)
)
return False
if output_path.suffix.lower() not in supported_file_types:
sg.popup_ok(
f"Error: {output_path.suffix.lower()} is not a supported "
+ "extension; use one of the following:\n\n"
+ "\n".join(supported_file_types)
)
return False
return True
def get_devices(
update: bool = True,
) -> tuple[list[str], list[str], list[int], list[int]]:
if update:
sd._terminate()
sd._initialize()
devices = sd.query_devices()
hostapis = sd.query_hostapis()
for hostapi in hostapis:
for device_idx in hostapi["devices"]:
devices[device_idx]["hostapi_name"] = hostapi["name"]
input_devices = [
f"{d['name']} ({d['hostapi_name']})"
for d in devices
if d["max_input_channels"] > 0
]
output_devices = [
f"{d['name']} ({d['hostapi_name']})"
for d in devices
if d["max_output_channels"] > 0
]
input_devices_indices = [d["index"] for d in devices if d["max_input_channels"] > 0]
output_devices_indices = [
d["index"] for d in devices if d["max_output_channels"] > 0
]
return input_devices, output_devices, input_devices_indices, output_devices_indices
def after_inference(window: sg.Window, path: Path, auto_play: bool, output_path: Path):
try:
LOG.info(f"Finished inference for {path.stem}{path.suffix}")
window["infer"].update(disabled=False)
if auto_play:
play_audio(output_path)
except Exception as e:
LOG.exception(e)
def main():
LOG.info(f"version: {__version__}")
sg.theme("Dark")
model_candidates = list(sorted(Path("./logs/44k/").glob("G_*.pth")))
frame_contents = {
"Paths": [
[
sg.Text("Model path"),
sg.Push(),
sg.InputText(
key="model_path",
default_text=model_candidates[-1].absolute().as_posix()
if model_candidates
else "",
enable_events=True,
),
sg.FileBrowse(
initial_folder=Path("./logs/44k/").absolute
if Path("./logs/44k/").exists()
else Path(".").absolute().as_posix(),
key="model_path_browse",
file_types=(("PyTorch", "*.pth"),),
),
],
[
sg.Text("Config path"),
sg.Push(),
sg.InputText(
key="config_path",
default_text=Path("./configs/44k/config.json").absolute().as_posix()
if Path("./configs/44k/config.json").exists()
else "",
enable_events=True,
),
sg.FileBrowse(
initial_folder=Path("./configs/44k/").as_posix()
if Path("./configs/44k/").exists()
else Path(".").absolute().as_posix(),
key="config_path_browse",
file_types=(("JSON", "*.json"),),
),
],
[
sg.Text("Cluster model path (Optional)"),
sg.Push(),
sg.InputText(
key="cluster_model_path",
default_text=Path("./logs/44k/kmeans.pt").absolute().as_posix()
if Path("./logs/44k/kmeans.pt").exists()
else "",
enable_events=True,
),
sg.FileBrowse(
initial_folder="./logs/44k/"
if Path("./logs/44k/").exists()
else ".",
key="cluster_model_path_browse",
file_types=(("PyTorch", "*.pt"),),
),
],
],
"Common": [
[
sg.Text("Speaker"),
sg.Push(),
sg.Combo(values=[], key="speaker", size=(20, 1)),
],
[
sg.Text("Silence threshold"),
sg.Push(),
sg.Slider(
range=(-60.0, 0),
orientation="h",
key="silence_threshold",
resolution=0.1,
),
],
[
sg.Text(
"Pitch (12 = 1 octave)\n"
"ADJUST THIS based on your voice\n"
"when Auto predict F0 is turned off.",
size=(None, 4),
),
sg.Push(),
sg.Slider(
range=(-36, 36),
orientation="h",
key="transpose",
tick_interval=12,
),
],
[
sg.Checkbox(
key="auto_predict_f0",
text="Auto predict F0 (Pitch may become unstable when turned on in real-time inference.)",
)
],
[
sg.Text("F0 prediction method"),
sg.Push(),
sg.Combo(
["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
key="f0_method",
),
],
[
sg.Text("Cluster infer ratio"),
sg.Push(),
sg.Slider(
range=(0, 1.0),
orientation="h",
key="cluster_infer_ratio",
resolution=0.01,
),
],
[
sg.Text("Noise scale"),
sg.Push(),
sg.Slider(
range=(0.0, 1.0),
orientation="h",
key="noise_scale",
resolution=0.01,
),
],
[
sg.Text("Pad seconds"),
sg.Push(),
sg.Slider(
range=(0.0, 1.0),
orientation="h",
key="pad_seconds",
resolution=0.01,
),
],
[
sg.Text("Chunk seconds"),
sg.Push(),
sg.Slider(
range=(0.0, 3.0),
orientation="h",
key="chunk_seconds",
resolution=0.01,
),
],
[
sg.Checkbox(
key="absolute_thresh",
text="Absolute threshold (ignored (True) in realtime inference)",
)
],
],
"File": [
[
sg.Text("Input audio path"),
sg.Push(),
sg.InputText(key="input_path", enable_events=True),
sg.FileBrowse(initial_folder=".", key="input_path_browse"),
sg.Button("Play", key="play_input"),
],
[
sg.Text("Output audio path"),
sg.Push(),
sg.InputText(key="output_path"),
sg.FileSaveAs(
initial_folder=".",
key="output_path_browse",
file_types=get_supported_file_types(),
),
],
[sg.Checkbox(key="auto_play", text="Auto play", default=True)],
],
"Realtime": [
[
sg.Text("Crossfade seconds"),
sg.Push(),
sg.Slider(
range=(0, 0.6),
orientation="h",
key="crossfade_seconds",
resolution=0.001,
),
],
[
sg.Text(
"Block seconds", # \n(big -> more robust, slower, (the same) latency)"
tooltip="Big -> more robust, slower, (the same) latency",
),
sg.Push(),
sg.Slider(
range=(0, 3.0),
orientation="h",
key="block_seconds",
resolution=0.001,
),
],
[
sg.Text(
"Additional Infer seconds (before)", # \n(big -> more robust, slower)"
tooltip="Big -> more robust, slower, additional latency",
),
sg.Push(),
sg.Slider(
range=(0, 2.0),
orientation="h",
key="additional_infer_before_seconds",
resolution=0.001,
),
],
[
sg.Text(
"Additional Infer seconds (after)", # \n(big -> more robust, slower, additional latency)"
tooltip="Big -> more robust, slower, additional latency",
),
sg.Push(),
sg.Slider(
range=(0, 2.0),
orientation="h",
key="additional_infer_after_seconds",
resolution=0.001,
),
],
[
sg.Text("Realtime algorithm"),
sg.Push(),
sg.Combo(
["2 (Divide by speech)", "1 (Divide constantly)"],
default_value="1 (Divide constantly)",
key="realtime_algorithm",
),
],
[
sg.Text("Input device"),
sg.Push(),
sg.Combo(
key="input_device",
values=[],
size=(60, 1),
),
],
[
sg.Text("Output device"),
sg.Push(),
sg.Combo(
key="output_device",
values=[],
size=(60, 1),
),
],
[
sg.Checkbox(
"Passthrough original audio (for latency check)",
key="passthrough_original",
default=False,
),
sg.Push(),
sg.Button("Refresh devices", key="refresh_devices"),
],
[
sg.Frame(
"Notes",
[
[
sg.Text(
"In Realtime Inference:\n"
" - Setting F0 prediction method to 'crepe` may cause performance degradation.\n"
" - Auto Predict F0 must be turned off.\n"
"If the audio sounds mumbly and choppy:\n"
" Case: The inference has not been made in time (Increase Block seconds)\n"
" Case: Mic input is low (Decrease Silence threshold)\n"
)
]
],
),
],
],
"Presets": [
[
sg.Text("Presets"),
sg.Push(),
sg.Combo(
key="presets",
values=list(load_presets().keys()),
size=(20, 1),
enable_events=True,
),
sg.Button("Delete preset", key="delete_preset"),
],
[
sg.Text("Preset name"),
sg.Stretch(),
sg.InputText(key="preset_name", size=(20, 1)),
sg.Button("Add current settings as a preset", key="add_preset"),
],
],
}
# frames
frames = {}
for name, items in frame_contents.items():
frame = sg.Frame(name, items)
frame.expand_x = True
frames[name] = [frame]
column1 = sg.Column(
[
frames["Paths"],
frames["Common"],
],
vertical_alignment="top",
)
column2 = sg.Column(
[
frames["File"],
frames["Realtime"],
frames["Presets"],
[
sg.Checkbox(
key="use_gpu",
default=get_optimal_device() != torch.device("cpu"),
text="Use GPU"
+ (
" (not available; if your device has GPU, make sure you installed PyTorch with CUDA support)"
if get_optimal_device() == torch.device("cpu")
else ""
),
disabled=get_optimal_device() == torch.device("cpu"),
)
],
[
sg.Button("Infer", key="infer"),
sg.Button("(Re)Start Voice Changer", key="start_vc"),
sg.Button("Stop Voice Changer", key="stop_vc"),
sg.Push(),
# sg.Button("ONNX Export", key="onnx_export"),
],
]
)
# columns
layout = [[column1, column2]]
# layout = [[sg.Column(layout, vertical_alignment="top", scrollable=True, expand_x=True, expand_y=True)]]
window = sg.Window(
f"{__name__.split('.')[0]}", layout, grab_anywhere=True, finalize=True
) # , use_custom_titlebar=True)
# for n in ["input_device", "output_device"]:
# window[n].Widget.configure(justify="right")
event, values = window.read(timeout=0.01)
def update_speaker() -> None:
from . import utils
config_path = Path(values["config_path"])
if config_path.exists() and config_path.is_file():
hp = utils.get_hparams(values["config_path"])
LOG.debug(f"Loaded config from {values['config_path']}")
window["speaker"].update(
values=list(hp.__dict__["spk"].keys()), set_to_index=0
)
def update_devices() -> None:
(
input_devices,
output_devices,
input_device_indices,
output_device_indices,
) = get_devices()
input_device_indices_reversed = {
v: k for k, v in enumerate(input_device_indices)
}
output_device_indices_reversed = {
v: k for k, v in enumerate(output_device_indices)
}
window["input_device"].update(
values=input_devices, value=values["input_device"]
)
window["output_device"].update(
values=output_devices, value=values["output_device"]
)
input_default, output_default = sd.default.device
if values["input_device"] not in input_devices:
window["input_device"].update(
values=input_devices,
set_to_index=input_device_indices_reversed.get(input_default, 0),
)
if values["output_device"] not in output_devices:
window["output_device"].update(
values=output_devices,
set_to_index=output_device_indices_reversed.get(output_default, 0),
)
PRESET_KEYS = [
key
for key in values.keys()
if not any(exclude in key for exclude in ["preset", "browse"])
]
def apply_preset(name: str) -> None:
for key, value in load_presets()[name].items():
if key in PRESET_KEYS:
window[key].update(value)
values[key] = value
default_name = list(load_presets().keys())[0]
apply_preset(default_name)
window["presets"].update(default_name)
del default_name
update_speaker()
update_devices()
# with ProcessPool(max_workers=1) as pool:
# to support Linux
with ProcessPool(
max_workers=min(2, multiprocessing.cpu_count()),
context=multiprocessing.get_context("spawn"),
) as pool:
future: None | ProcessFuture = None
infer_futures: set[ProcessFuture] = set()
while True:
event, values = window.read(200)
if event == sg.WIN_CLOSED:
break
if not event == sg.EVENT_TIMEOUT:
LOG.info(f"Event {event}, values {values}")
if event.endswith("_path"):
for name in window.AllKeysDict:
if str(name).endswith("_browse"):
browser = window[name]
if isinstance(browser, sg.Button):
LOG.info(
f"Updating browser {browser} to {Path(values[event]).parent}"
)
browser.InitialFolder = Path(values[event]).parent
browser.update()
else:
LOG.warning(f"Browser {browser} is not a FileBrowse")
window["transpose"].update(
disabled=values["auto_predict_f0"],
visible=not values["auto_predict_f0"],
)
input_path = Path(values["input_path"])
output_path = Path(values["output_path"])
if event == "add_preset":
presets = add_preset(
values["preset_name"], {key: values[key] for key in PRESET_KEYS}
)
window["presets"].update(values=list(presets.keys()))
elif event == "delete_preset":
presets = delete_preset(values["presets"])
window["presets"].update(values=list(presets.keys()))
elif event == "presets":
apply_preset(values["presets"])
update_speaker()
elif event == "refresh_devices":
update_devices()
elif event == "config_path":
update_speaker()
elif event == "input_path":
# Don't change the output path if it's already set
if values["output_path"]:
continue
# Set a sensible default output path
window.Element("output_path").Update(str(get_output_path(input_path)))
elif event == "infer":
if not input_path.exists() or not input_path.is_file():
LOG.warning(f"Input path {input_path} does not exist.")
continue
if not validate_output_file_type(output_path):
continue
try:
from so_vits_svc_fork.inference.main import infer
LOG.info("Starting inference...")
window["infer"].update(disabled=True)
infer_future = pool.schedule(
infer,
kwargs=dict(
# paths
model_path=Path(values["model_path"]),
output_path=output_path,
input_path=input_path,
config_path=Path(values["config_path"]),
# svc config
speaker=values["speaker"],
cluster_model_path=Path(values["cluster_model_path"])
if values["cluster_model_path"]
else None,
transpose=values["transpose"],
auto_predict_f0=values["auto_predict_f0"],
cluster_infer_ratio=values["cluster_infer_ratio"],
noise_scale=values["noise_scale"],
f0_method=values["f0_method"],
# slice config
db_thresh=values["silence_threshold"],
pad_seconds=values["pad_seconds"],
chunk_seconds=values["chunk_seconds"],
absolute_thresh=values["absolute_thresh"],
device="cpu"
if not values["use_gpu"]
else get_optimal_device(),
),
)
infer_future.add_done_callback(
lambda _future: after_inference(
window, input_path, values["auto_play"], output_path
)
)
infer_futures.add(infer_future)
except Exception as e:
LOG.exception(e)
elif event == "play_input":
if Path(values["input_path"]).exists():
pool.schedule(play_audio, args=[Path(values["input_path"])])
elif event == "start_vc":
_, _, input_device_indices, output_device_indices = get_devices(
update=False
)
from so_vits_svc_fork.inference.main import realtime
if future:
LOG.info("Canceling previous task")
future.cancel()
future = pool.schedule(
realtime,
kwargs=dict(
# paths
model_path=Path(values["model_path"]),
config_path=Path(values["config_path"]),
speaker=values["speaker"],
# svc config
cluster_model_path=Path(values["cluster_model_path"])
if values["cluster_model_path"]
else None,
transpose=values["transpose"],
auto_predict_f0=values["auto_predict_f0"],
cluster_infer_ratio=values["cluster_infer_ratio"],
noise_scale=values["noise_scale"],
f0_method=values["f0_method"],
# slice config
db_thresh=values["silence_threshold"],
pad_seconds=values["pad_seconds"],
chunk_seconds=values["chunk_seconds"],
# realtime config
crossfade_seconds=values["crossfade_seconds"],
additional_infer_before_seconds=values[
"additional_infer_before_seconds"
],
additional_infer_after_seconds=values[
"additional_infer_after_seconds"
],
block_seconds=values["block_seconds"],
version=int(values["realtime_algorithm"][0]),
input_device=input_device_indices[
window["input_device"].widget.current()
],
output_device=output_device_indices[
window["output_device"].widget.current()
],
device=get_optimal_device() if values["use_gpu"] else "cpu",
passthrough_original=values["passthrough_original"],
),
)
elif event == "stop_vc":
if future:
future.cancel()
future = None
elif event == "onnx_export":
try:
raise NotImplementedError("ONNX export is not implemented yet.")
from so_vits_svc_fork.modules.onnx._export import onnx_export
onnx_export(
input_path=Path(values["model_path"]),
output_path=Path(values["model_path"]).with_suffix(".onnx"),
config_path=Path(values["config_path"]),
device="cpu",
)
except Exception as e:
LOG.exception(e)
if future is not None and future.done():
try:
future.result()
except Exception as e:
LOG.error("Error in realtime: ")
LOG.exception(e)
future = None
for future in copy(infer_futures):
if future.done():
try:
future.result()
except Exception as e:
LOG.error("Error in inference: ")
LOG.exception(e)
infer_futures.remove(future)
if future:
future.cancel()
window.close()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/hparams.py
+38
View File
@@ -0,0 +1,38 @@
from __future__ import annotations
from typing import Any
class HParams:
def __init__(self, **kwargs: Any) -> None:
for k, v in kwargs.items():
if type(v) == dict:
v = HParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def get(self, key: str, default: Any = None):
return self.__dict__.get(key, default)
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/inference/__init__.py
View File
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/inference/core.py
+686
View File
@@ -0,0 +1,686 @@
from __future__ import annotations
from copy import deepcopy
from logging import getLogger
from pathlib import Path
from typing import Any, Callable, Iterable, Literal
import attrs
import librosa
import numpy as np
import torch
from cm_time import timer
from numpy import dtype, float32, ndarray
import so_vits_svc_fork.f0
from so_vits_svc_fork import cluster, utils
from ..modules.synthesizers import SynthesizerTrn
from ..utils import get_optimal_device
LOG = getLogger(__name__)
def pad_array(array_, target_length: int):
current_length = array_.shape[0]
if current_length >= target_length:
return array_[
(current_length - target_length)
// 2 : (current_length - target_length)
// 2
+ target_length,
...,
]
else:
pad_width = target_length - current_length
pad_left = pad_width // 2
pad_right = pad_width - pad_left
padded_arr = np.pad(
array_, (pad_left, pad_right), "constant", constant_values=(0, 0)
)
return padded_arr
@attrs.frozen(kw_only=True)
class Chunk:
is_speech: bool
audio: ndarray[Any, dtype[float32]]
start: int
end: int
@property
def duration(self) -> float32:
# return self.end - self.start
return float32(self.audio.shape[0])
def __repr__(self) -> str:
return f"Chunk(Speech: {self.is_speech}, {self.duration})"
def split_silence(
audio: ndarray[Any, dtype[float32]],
top_db: int = 40,
ref: float | Callable[[ndarray[Any, dtype[float32]]], float] = 1,
frame_length: int = 2048,
hop_length: int = 512,
aggregate: Callable[[ndarray[Any, dtype[float32]]], float] = np.mean,
chunk_size_max: int = 44100 * 10
) -> Iterable[Chunk]:
non_silence_indices = librosa.effects.split(
audio,
top_db=top_db,
ref=ref,
frame_length=frame_length,
hop_length=hop_length,
#aggregate=aggregate,
)
last_end = 0
for start, end in non_silence_indices:
if start != last_end:
yield Chunk(
is_speech=False, audio=audio[last_end:start], start=last_end, end=start
)
if end-start > chunk_size_max:
chunk_count = (end-start) // chunk_size_max
for i in range(0, chunk_count):
yield Chunk(is_speech=True, audio=audio[start+(i*chunk_size_max):start+((i+1)*chunk_size_max)],
start=start+(i*chunk_size_max), end=start+((i+1)*chunk_size_max))
yield Chunk(is_speech=True, audio=audio[start+chunk_count*chunk_size_max:end],
start=start+chunk_count*chunk_size_max, end=end)
last_end=end
else:
yield Chunk(is_speech=True, audio=audio[start:end], start=start, end=end)
last_end = end
if last_end != len(audio):
yield Chunk(
is_speech=False, audio=audio[last_end:], start=last_end, end=len(audio)
)
class Svc:
def __init__(
self,
*,
net_g_path: Path | str,
config_path: Path | str,
device: torch.device | str | None = None,
cluster_model_path: Path | str | None = None,
half: bool = False,
):
self.net_g_path = net_g_path
if device is None:
self.device = (get_optimal_device(),)
else:
self.device = torch.device(device)
self.hps = utils.get_hparams(config_path)
self.target_sample = self.hps.data.sampling_rate
self.hop_size = self.hps.data.hop_length
self.spk2id = self.hps.spk
self.hubert_model = utils.get_hubert_model(
self.device, self.hps.data.get("contentvec_final_proj", True)
)
self.dtype = torch.float16 if half else torch.float32
self.contentvec_final_proj = self.hps.data.__dict__.get(
"contentvec_final_proj", True
)
self.load_model()
if cluster_model_path is not None and Path(cluster_model_path).exists():
self.cluster_model = cluster.get_cluster_model(cluster_model_path)
def load_model(self):
self.net_g = SynthesizerTrn(
self.hps.data.filter_length // 2 + 1,
self.hps.train.segment_size // self.hps.data.hop_length,
**self.hps.model,
)
_ = utils.load_checkpoint(self.net_g_path, self.net_g, None)
_ = self.net_g.eval().to(self.device, dtype=self.dtype)
def get_unit_f0(
self,
audio: ndarray[Any, dtype[float32]],
tran: int,
cluster_infer_ratio: float,
speaker: int | str,
f0_method: Literal[
"crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
] = "dio",
):
f0 = so_vits_svc_fork.f0.compute_f0(
audio,
sampling_rate=self.target_sample,
hop_length=self.hop_size,
method=f0_method,
)
f0, uv = so_vits_svc_fork.f0.interpolate_f0(f0)
f0 = torch.as_tensor(f0, dtype=self.dtype, device=self.device)
uv = torch.as_tensor(uv, dtype=self.dtype, device=self.device)
f0 = f0 * 2 ** (tran / 12)
f0 = f0.unsqueeze(0)
uv = uv.unsqueeze(0)
c = utils.get_content(
self.hubert_model,
audio,
self.device,
self.target_sample,
self.contentvec_final_proj,
).to(self.dtype)
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
if cluster_infer_ratio != 0:
cluster_c = cluster.get_cluster_center_result(
self.cluster_model, c.cpu().numpy().T, speaker
).T
cluster_c = torch.FloatTensor(cluster_c).to(self.device)
c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
c = c.unsqueeze(0)
return c, f0, uv
def infer(
self,
speaker: int | str,
transpose: int,
audio: ndarray[Any, dtype[float32]],
cluster_infer_ratio: float = 0,
auto_predict_f0: bool = False,
noise_scale: float = 0.4,
f0_method: Literal[
"crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
] = "dio",
) -> tuple[torch.Tensor, int]:
audio = audio.astype(np.float32)
# get speaker id
if isinstance(speaker, int):
if len(self.spk2id.__dict__) >= speaker:
speaker_id = speaker
else:
raise ValueError(
f"Speaker id {speaker} >= number of speakers {len(self.spk2id.__dict__)}"
)
else:
if speaker in self.spk2id.__dict__:
speaker_id = self.spk2id.__dict__[speaker]
else:
LOG.warning(f"Speaker {speaker} is not found. Use speaker 0 instead.")
speaker_id = 0
speaker_candidates = list(
filter(lambda x: x[1] == speaker_id, self.spk2id.__dict__.items())
)
if len(speaker_candidates) > 1:
raise ValueError(
f"Speaker_id {speaker_id} is not unique. Candidates: {speaker_candidates}"
)
elif len(speaker_candidates) == 0:
raise ValueError(f"Speaker_id {speaker_id} is not found.")
speaker = speaker_candidates[0][0]
sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
# get unit f0
c, f0, uv = self.get_unit_f0(
audio, transpose, cluster_infer_ratio, speaker, f0_method
)
# inference
with torch.no_grad():
with timer() as t:
audio = self.net_g.infer(
c,
f0=f0,
g=sid,
uv=uv,
predict_f0=auto_predict_f0,
noice_scale=noise_scale,
)[0, 0].data.float()
audio_duration = audio.shape[-1] / self.target_sample
LOG.info(
f"Inferece time: {t.elapsed:.2f}s, RTF: {t.elapsed / audio_duration:.2f}"
)
torch.cuda.empty_cache()
return audio, audio.shape[-1]
def infer_silence(
self,
audio: np.ndarray[Any, np.dtype[np.float32]],
*,
# svc config
speaker: int | str,
transpose: int = 0,
auto_predict_f0: bool = False,
cluster_infer_ratio: float = 0,
noise_scale: float = 0.4,
f0_method: Literal[
"crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
] = "dio",
# slice config
db_thresh: int = -40,
pad_seconds: float = 0.5,
chunk_seconds: float = 0.5,
absolute_thresh: bool = False,
# fade_seconds: float = 0.0,
) -> np.ndarray[Any, np.dtype[np.float32]]:
sr = self.target_sample
result_audio = np.array([], dtype=np.float32)
chunk_length_min = chunk_length_min = (
int(
min(
sr / so_vits_svc_fork.f0.f0_min * 20 + 1,
chunk_seconds * sr,
)
)
// 2
)
for chunk in split_silence(
audio,
top_db=-db_thresh,
frame_length=chunk_length_min * 2,
hop_length=chunk_length_min,
ref=1 if absolute_thresh else np.max,
):
LOG.info(f"Chunk: {chunk}")
if not chunk.is_speech:
audio_chunk_infer = np.zeros_like(chunk.audio)
else:
# pad
pad_len = int(sr * pad_seconds)
audio_chunk_pad = np.concatenate(
[
np.zeros([pad_len], dtype=np.float32),
chunk.audio,
np.zeros([pad_len], dtype=np.float32),
]
)
audio_chunk_pad_infer_tensor, _ = self.infer(
speaker,
transpose,
audio_chunk_pad,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noise_scale=noise_scale,
f0_method=f0_method,
)
audio_chunk_pad_infer = audio_chunk_pad_infer_tensor.cpu().numpy()
pad_len = int(self.target_sample * pad_seconds)
cut_len_2 = (len(audio_chunk_pad_infer) - len(chunk.audio)) // 2
audio_chunk_infer = audio_chunk_pad_infer[
cut_len_2 : cut_len_2 + len(chunk.audio)
]
# add fade
# fade_len = int(self.target_sample * fade_seconds)
# _audio[:fade_len] = _audio[:fade_len] * np.linspace(0, 1, fade_len)
# _audio[-fade_len:] = _audio[-fade_len:] * np.linspace(1, 0, fade_len)
# empty cache
torch.cuda.empty_cache()
result_audio = np.concatenate([result_audio, audio_chunk_infer])
result_audio = result_audio[: audio.shape[0]]
return result_audio
def sola_crossfade(
first: ndarray[Any, dtype[float32]],
second: ndarray[Any, dtype[float32]],
crossfade_len: int,
sola_search_len: int,
) -> ndarray[Any, dtype[float32]]:
cor_nom = np.convolve(
second[: sola_search_len + crossfade_len],
np.flip(first[-crossfade_len:]),
"valid",
)
cor_den = np.sqrt(
np.convolve(
second[: sola_search_len + crossfade_len] ** 2,
np.ones(crossfade_len),
"valid",
)
+ 1e-8
)
sola_shift = np.argmax(cor_nom / cor_den)
LOG.info(f"SOLA shift: {sola_shift}")
second = second[sola_shift : sola_shift + len(second) - sola_search_len]
return np.concatenate(
[
first[:-crossfade_len],
first[-crossfade_len:] * np.linspace(1, 0, crossfade_len)
+ second[:crossfade_len] * np.linspace(0, 1, crossfade_len),
second[crossfade_len:],
]
)
class Crossfader:
def __init__(
self,
*,
additional_infer_before_len: int,
additional_infer_after_len: int,
crossfade_len: int,
sola_search_len: int = 384,
) -> None:
if additional_infer_before_len < 0:
raise ValueError("additional_infer_len must be >= 0")
if crossfade_len < 0:
raise ValueError("crossfade_len must be >= 0")
if additional_infer_after_len < 0:
raise ValueError("additional_infer_len must be >= 0")
if additional_infer_before_len < 0:
raise ValueError("additional_infer_len must be >= 0")
self.additional_infer_before_len = additional_infer_before_len
self.additional_infer_after_len = additional_infer_after_len
self.crossfade_len = crossfade_len
self.sola_search_len = sola_search_len
self.last_input_left = np.zeros(
sola_search_len
+ crossfade_len
+ additional_infer_before_len
+ additional_infer_after_len,
dtype=np.float32,
)
self.last_infered_left = np.zeros(crossfade_len, dtype=np.float32)
def process(
self, input_audio: ndarray[Any, dtype[float32]], *args, **kwargs: Any
) -> ndarray[Any, dtype[float32]]:
"""
chunks : ■■■■■■□□□□□□
add last input:□■■■■■■
■□□□□□□
infer :□■■■■■■
■□□□□□□
crossfade :▲■■■■■
▲□□□□□
"""
# check input
if input_audio.ndim != 1:
raise ValueError("Input audio must be 1-dimensional.")
if (
input_audio.shape[0] + self.additional_infer_before_len
<= self.crossfade_len
):
raise ValueError(
f"Input audio length ({input_audio.shape[0]}) + additional_infer_len ({self.additional_infer_before_len}) must be greater than crossfade_len ({self.crossfade_len})."
)
input_audio = input_audio.astype(np.float32)
input_audio_len = len(input_audio)
# concat last input and infer
input_audio_concat = np.concatenate([self.last_input_left, input_audio])
del input_audio
pad_len = 0
if pad_len:
infer_audio_concat = self.infer(
np.pad(input_audio_concat, (pad_len, pad_len), mode="reflect"),
*args,
**kwargs,
)[pad_len:-pad_len]
else:
infer_audio_concat = self.infer(input_audio_concat, *args, **kwargs)
# debug SOLA (using copy synthesis with a random shift)
"""
rs = int(np.random.uniform(-200,200))
LOG.info(f"Debug random shift: {rs}")
infer_audio_concat = np.roll(input_audio_concat, rs)
"""
if len(infer_audio_concat) != len(input_audio_concat):
raise ValueError(
f"Inferred audio length ({len(infer_audio_concat)}) should be equal to input audio length ({len(input_audio_concat)})."
)
infer_audio_to_use = infer_audio_concat[
-(
self.sola_search_len
+ self.crossfade_len
+ input_audio_len
+ self.additional_infer_after_len
) : -self.additional_infer_after_len
]
assert (
len(infer_audio_to_use)
== input_audio_len + self.sola_search_len + self.crossfade_len
), f"{len(infer_audio_to_use)} != {input_audio_len + self.sola_search_len + self.cross_fade_len}"
_audio = sola_crossfade(
self.last_infered_left,
infer_audio_to_use,
self.crossfade_len,
self.sola_search_len,
)
result_audio = _audio[: -self.crossfade_len]
assert (
len(result_audio) == input_audio_len
), f"{len(result_audio)} != {input_audio_len}"
# update last input and inferred
self.last_input_left = input_audio_concat[
-(
self.sola_search_len
+ self.crossfade_len
+ self.additional_infer_before_len
+ self.additional_infer_after_len
) :
]
self.last_infered_left = _audio[-self.crossfade_len :]
return result_audio
def infer(
self, input_audio: ndarray[Any, dtype[float32]]
) -> ndarray[Any, dtype[float32]]:
return input_audio
class RealtimeVC(Crossfader):
def __init__(
self,
*,
svc_model: Svc,
crossfade_len: int = 3840,
additional_infer_before_len: int = 7680,
additional_infer_after_len: int = 7680,
split: bool = True,
) -> None:
self.svc_model = svc_model
self.split = split
super().__init__(
crossfade_len=crossfade_len,
additional_infer_before_len=additional_infer_before_len,
additional_infer_after_len=additional_infer_after_len,
)
def process(
self,
input_audio: ndarray[Any, dtype[float32]],
*args: Any,
**kwargs: Any,
) -> ndarray[Any, dtype[float32]]:
return super().process(input_audio, *args, **kwargs)
def infer(
self,
input_audio: np.ndarray[Any, np.dtype[np.float32]],
# svc config
speaker: int | str,
transpose: int,
cluster_infer_ratio: float = 0,
auto_predict_f0: bool = False,
noise_scale: float = 0.4,
f0_method: Literal[
"crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
] = "dio",
# slice config
db_thresh: int = -40,
pad_seconds: float = 0.5,
chunk_seconds: float = 0.5,
) -> ndarray[Any, dtype[float32]]:
# infer
if self.split:
return self.svc_model.infer_silence(
audio=input_audio,
speaker=speaker,
transpose=transpose,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noise_scale=noise_scale,
f0_method=f0_method,
db_thresh=db_thresh,
pad_seconds=pad_seconds,
chunk_seconds=chunk_seconds,
absolute_thresh=True,
)
else:
rms = np.sqrt(np.mean(input_audio**2))
min_rms = 10 ** (db_thresh / 20)
if rms < min_rms:
LOG.info(f"Skip silence: RMS={rms:.2f} < {min_rms:.2f}")
return np.zeros_like(input_audio)
else:
LOG.info(f"Start inference: RMS={rms:.2f} >= {min_rms:.2f}")
infered_audio_c, _ = self.svc_model.infer(
speaker=speaker,
transpose=transpose,
audio=input_audio,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noise_scale=noise_scale,
f0_method=f0_method,
)
return infered_audio_c.cpu().numpy()
class RealtimeVC2:
chunk_store: list[Chunk]
def __init__(self, svc_model: Svc) -> None:
self.input_audio_store = np.array([], dtype=np.float32)
self.chunk_store = []
self.svc_model = svc_model
def process(
self,
input_audio: np.ndarray[Any, np.dtype[np.float32]],
# svc config
speaker: int | str,
transpose: int,
cluster_infer_ratio: float = 0,
auto_predict_f0: bool = False,
noise_scale: float = 0.4,
f0_method: Literal[
"crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
] = "dio",
# slice config
db_thresh: int = -40,
chunk_seconds: float = 0.5,
) -> ndarray[Any, dtype[float32]]:
def infer(audio: ndarray[Any, dtype[float32]]) -> ndarray[Any, dtype[float32]]:
infered_audio_c, _ = self.svc_model.infer(
speaker=speaker,
transpose=transpose,
audio=audio,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noise_scale=noise_scale,
f0_method=f0_method,
)
return infered_audio_c.cpu().numpy()
self.input_audio_store = np.concatenate([self.input_audio_store, input_audio])
LOG.info(f"input_audio_store: {self.input_audio_store.shape}")
sr = self.svc_model.target_sample
chunk_length_min = (
int(min(sr / so_vits_svc_fork.f0.f0_min * 20 + 1, chunk_seconds * sr)) // 2
)
LOG.info(f"Chunk length min: {chunk_length_min}")
chunk_list = list(
split_silence(
self.input_audio_store,
-db_thresh,
frame_length=chunk_length_min * 2,
hop_length=chunk_length_min,
ref=1, # use absolute threshold
)
)
assert len(chunk_list) > 0
LOG.info(f"Chunk list: {chunk_list}")
# do not infer LAST incomplete is_speech chunk and save to store
if chunk_list[-1].is_speech:
self.input_audio_store = chunk_list.pop().audio
else:
self.input_audio_store = np.array([], dtype=np.float32)
# infer complete is_speech chunk and save to store
self.chunk_store.extend(
[
attrs.evolve(c, audio=infer(c.audio) if c.is_speech else c.audio)
for c in chunk_list
]
)
# calculate lengths and determine compress rate
total_speech_len = sum(
[c.duration if c.is_speech else 0 for c in self.chunk_store]
)
total_silence_len = sum(
[c.duration if not c.is_speech else 0 for c in self.chunk_store]
)
input_audio_len = input_audio.shape[0]
silence_compress_rate = total_silence_len / max(
0, input_audio_len - total_speech_len
)
LOG.info(
f"Total speech len: {total_speech_len}, silence len: {total_silence_len}, silence compress rate: {silence_compress_rate}"
)
# generate output audio
output_audio = np.array([], dtype=np.float32)
break_flag = False
LOG.info(f"Chunk store: {self.chunk_store}")
for chunk in deepcopy(self.chunk_store):
compress_rate = 1 if chunk.is_speech else silence_compress_rate
left_len = input_audio_len - output_audio.shape[0]
# calculate chunk duration
chunk_duration_output = int(min(chunk.duration / compress_rate, left_len))
chunk_duration_input = int(min(chunk.duration, left_len * compress_rate))
LOG.info(
f"Chunk duration output: {chunk_duration_output}, input: {chunk_duration_input}, left len: {left_len}"
)
# remove chunk from store
self.chunk_store.pop(0)
if chunk.duration > chunk_duration_input:
left_chunk = attrs.evolve(
chunk, audio=chunk.audio[chunk_duration_input:]
)
chunk = attrs.evolve(chunk, audio=chunk.audio[:chunk_duration_input])
self.chunk_store.insert(0, left_chunk)
break_flag = True
if chunk.is_speech:
# if is_speech, just concat
output_audio = np.concatenate([output_audio, chunk.audio])
else:
# if is_silence, concat with zeros and compress with silence_compress_rate
output_audio = np.concatenate(
[
output_audio,
np.zeros(
chunk_duration_output,
dtype=np.float32,
),
]
)
if break_flag:
break
LOG.info(f"Chunk store: {self.chunk_store}, output_audio: {output_audio.shape}")
# make same length (errors)
output_audio = output_audio[:input_audio_len]
output_audio = np.concatenate(
[
output_audio,
np.zeros(input_audio_len - output_audio.shape[0], dtype=np.float32),
]
)
return output_audio
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/inference/main.py
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from __future__ import annotations
from logging import getLogger
from pathlib import Path
from typing import Literal
import librosa
import numpy as np
import soundfile
import torch
from cm_time import timer
from so_vits_svc_fork.inference.core import RealtimeVC, RealtimeVC2, Svc
from so_vits_svc_fork.utils import get_optimal_device
LOG = getLogger(__name__)
def infer(
*,
# paths
input_path: Path | str,
output_path: Path | str,
model_path: Path | str,
config_path: Path | str,
# svc config
speaker: int | str,
cluster_model_path: Path | str | None = None,
transpose: int = 0,
auto_predict_f0: bool = False,
cluster_infer_ratio: float = 0,
noise_scale: float = 0.4,
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
# slice config
db_thresh: int = -40,
pad_seconds: float = 0.5,
chunk_seconds: float = 0.5,
absolute_thresh: bool = False,
device: str | torch.device = get_optimal_device(),
):
model_path = Path(model_path)
output_path = Path(output_path)
input_path = Path(input_path)
config_path = Path(config_path)
cluster_model_path = Path(cluster_model_path) if cluster_model_path else None
svc_model = Svc(
net_g_path=model_path.as_posix(),
config_path=config_path.as_posix(),
cluster_model_path=cluster_model_path.as_posix()
if cluster_model_path
else None,
device=device,
)
audio, _ = librosa.load(input_path, sr=svc_model.target_sample)
audio = svc_model.infer_silence(
audio.astype(np.float32),
speaker=speaker,
transpose=transpose,
auto_predict_f0=auto_predict_f0,
cluster_infer_ratio=cluster_infer_ratio,
noise_scale=noise_scale,
f0_method=f0_method,
db_thresh=db_thresh,
pad_seconds=pad_seconds,
chunk_seconds=chunk_seconds,
absolute_thresh=absolute_thresh,
)
soundfile.write(output_path, audio, svc_model.target_sample)
def realtime(
*,
# paths
model_path: Path | str,
config_path: Path | str,
# svc config
speaker: str,
cluster_model_path: Path | str | None = None,
transpose: int = 0,
auto_predict_f0: bool = False,
cluster_infer_ratio: float = 0,
noise_scale: float = 0.4,
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
# slice config
db_thresh: int = -40,
pad_seconds: float = 0.5,
chunk_seconds: float = 0.5,
# realtime config
crossfade_seconds: float = 0.05,
additional_infer_before_seconds: float = 0.2,
additional_infer_after_seconds: float = 0.1,
block_seconds: float = 0.5,
version: int = 2,
input_device: int | str | None = None,
output_device: int | str | None = None,
device: str | torch.device = get_optimal_device(),
passthrough_original: bool = False,
):
import sounddevice as sd
model_path = Path(model_path)
config_path = Path(config_path)
cluster_model_path = Path(cluster_model_path) if cluster_model_path else None
svc_model = Svc(
net_g_path=model_path.as_posix(),
config_path=config_path.as_posix(),
cluster_model_path=cluster_model_path.as_posix()
if cluster_model_path
else None,
device=device,
)
LOG.info("Creating realtime model...")
if version == 1:
model = RealtimeVC(
svc_model=svc_model,
crossfade_len=int(crossfade_seconds * svc_model.target_sample),
additional_infer_before_len=int(
additional_infer_before_seconds * svc_model.target_sample
),
additional_infer_after_len=int(
additional_infer_after_seconds * svc_model.target_sample
),
)
else:
model = RealtimeVC2(
svc_model=svc_model,
)
# LOG all device info
devices = sd.query_devices()
LOG.info(f"Device: {devices}")
if isinstance(input_device, str):
input_device_candidates = [
i for i, d in enumerate(devices) if d["name"] == input_device
]
if len(input_device_candidates) == 0:
LOG.warning(f"Input device {input_device} not found, using default")
input_device = None
else:
input_device = input_device_candidates[0]
if isinstance(output_device, str):
output_device_candidates = [
i for i, d in enumerate(devices) if d["name"] == output_device
]
if len(output_device_candidates) == 0:
LOG.warning(f"Output device {output_device} not found, using default")
output_device = None
else:
output_device = output_device_candidates[0]
if input_device is None or input_device >= len(devices):
input_device = sd.default.device[0]
if output_device is None or output_device >= len(devices):
output_device = sd.default.device[1]
LOG.info(
f"Input Device: {devices[input_device]['name']}, Output Device: {devices[output_device]['name']}"
)
# the model RTL is somewhat significantly high only in the first inference
# there could be no better way to warm up the model than to do a dummy inference
# (there are not differences in the behavior of the model between the first and the later inferences)
# so we do a dummy inference to warm up the model (1 second of audio)
LOG.info("Warming up the model...")
svc_model.infer(
speaker=speaker,
transpose=transpose,
auto_predict_f0=auto_predict_f0,
cluster_infer_ratio=cluster_infer_ratio,
noise_scale=noise_scale,
f0_method=f0_method,
audio=np.zeros(svc_model.target_sample, dtype=np.float32),
)
def callback(
indata: np.ndarray,
outdata: np.ndarray,
frames: int,
time: int,
status: sd.CallbackFlags,
) -> None:
LOG.debug(
f"Frames: {frames}, Status: {status}, Shape: {indata.shape}, Time: {time}"
)
kwargs = dict(
input_audio=indata.mean(axis=1).astype(np.float32),
# svc config
speaker=speaker,
transpose=transpose,
auto_predict_f0=auto_predict_f0,
cluster_infer_ratio=cluster_infer_ratio,
noise_scale=noise_scale,
f0_method=f0_method,
# slice config
db_thresh=db_thresh,
# pad_seconds=pad_seconds,
chunk_seconds=chunk_seconds,
)
if version == 1:
kwargs["pad_seconds"] = pad_seconds
with timer() as t:
inference = model.process(
**kwargs,
).reshape(-1, 1)
if passthrough_original:
outdata[:] = (indata + inference) / 2
else:
outdata[:] = inference
rtf = t.elapsed / block_seconds
LOG.info(f"Realtime inference time: {t.elapsed:.3f}s, RTF: {rtf:.3f}")
if rtf > 1:
LOG.warning("RTF is too high, consider increasing block_seconds")
with sd.Stream(
device=(input_device, output_device),
channels=1,
callback=callback,
samplerate=svc_model.target_sample,
blocksize=int(block_seconds * svc_model.target_sample),
latency="low",
) as stream:
LOG.info(f"Latency: {stream.latency}")
while True:
sd.sleep(1000)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/logger.py
+54
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import os
import sys
from logging import (
DEBUG,
INFO,
FileHandler,
StreamHandler,
basicConfig,
captureWarnings,
getLogger,
)
from pathlib import Path
from rich.logging import RichHandler
LOGGER_INIT = False
def init_logger() -> None:
global LOGGER_INIT
if LOGGER_INIT:
return
IS_TEST = "test" in Path.cwd().stem
package_name = sys.modules[__name__].__package__
basicConfig(
level=INFO,
format="%(asctime)s %(message)s",
datefmt="[%X]",
handlers=[
StreamHandler() if is_notebook() else RichHandler(),
FileHandler(f"{package_name}.log"),
],
)
if IS_TEST:
getLogger(package_name).setLevel(DEBUG)
captureWarnings(True)
LOGGER_INIT = True
def is_notebook():
try:
from IPython import get_ipython
if "IPKernelApp" not in get_ipython().config: # pragma: no cover
raise ImportError("console")
return False
if "VSCODE_PID" in os.environ: # pragma: no cover
raise ImportError("vscode")
return False
except Exception:
return False
else: # pragma: no cover
return True
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/__init__.py
View File
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/attentions.py
+488
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@@ -0,0 +1,488 @@
import math
import torch
from torch import nn
from torch.nn import functional as F
from so_vits_svc_fork.modules import commons
from so_vits_svc_fork.modules.modules import LayerNorm
class FFT(nn.Module):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads,
n_layers=1,
kernel_size=1,
p_dropout=0.0,
proximal_bias=False,
proximal_init=True,
**kwargs
):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.proximal_bias = proximal_bias
self.proximal_init = proximal_init
self.drop = nn.Dropout(p_dropout)
self.self_attn_layers = nn.ModuleList()
self.norm_layers_0 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
for i in range(self.n_layers):
self.self_attn_layers.append(
MultiHeadAttention(
hidden_channels,
hidden_channels,
n_heads,
p_dropout=p_dropout,
proximal_bias=proximal_bias,
proximal_init=proximal_init,
)
)
self.norm_layers_0.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(
hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
p_dropout=p_dropout,
causal=True,
)
)
self.norm_layers_1.append(LayerNorm(hidden_channels))
def forward(self, x, x_mask):
"""
x: decoder input
h: encoder output
"""
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
device=x.device, dtype=x.dtype
)
x = x * x_mask
for i in range(self.n_layers):
y = self.self_attn_layers[i](x, x, self_attn_mask)
y = self.drop(y)
x = self.norm_layers_0[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.drop(y)
x = self.norm_layers_1[i](x + y)
x = x * x_mask
return x
class Encoder(nn.Module):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size=1,
p_dropout=0.0,
window_size=4,
**kwargs
):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.window_size = window_size
self.drop = nn.Dropout(p_dropout)
self.attn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_2 = nn.ModuleList()
for i in range(self.n_layers):
self.attn_layers.append(
MultiHeadAttention(
hidden_channels,
hidden_channels,
n_heads,
p_dropout=p_dropout,
window_size=window_size,
)
)
self.norm_layers_1.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(
hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
p_dropout=p_dropout,
)
)
self.norm_layers_2.append(LayerNorm(hidden_channels))
def forward(self, x, x_mask):
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
x = x * x_mask
for i in range(self.n_layers):
y = self.attn_layers[i](x, x, attn_mask)
y = self.drop(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.drop(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
return x
class Decoder(nn.Module):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size=1,
p_dropout=0.0,
proximal_bias=False,
proximal_init=True,
**kwargs
):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.proximal_bias = proximal_bias
self.proximal_init = proximal_init
self.drop = nn.Dropout(p_dropout)
self.self_attn_layers = nn.ModuleList()
self.norm_layers_0 = nn.ModuleList()
self.encdec_attn_layers = nn.ModuleList()
self.norm_layers_1 = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
self.norm_layers_2 = nn.ModuleList()
for i in range(self.n_layers):
self.self_attn_layers.append(
MultiHeadAttention(
hidden_channels,
hidden_channels,
n_heads,
p_dropout=p_dropout,
proximal_bias=proximal_bias,
proximal_init=proximal_init,
)
)
self.norm_layers_0.append(LayerNorm(hidden_channels))
self.encdec_attn_layers.append(
MultiHeadAttention(
hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
)
)
self.norm_layers_1.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(
hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
p_dropout=p_dropout,
causal=True,
)
)
self.norm_layers_2.append(LayerNorm(hidden_channels))
def forward(self, x, x_mask, h, h_mask):
"""
x: decoder input
h: encoder output
"""
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
device=x.device, dtype=x.dtype
)
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
x = x * x_mask
for i in range(self.n_layers):
y = self.self_attn_layers[i](x, x, self_attn_mask)
y = self.drop(y)
x = self.norm_layers_0[i](x + y)
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
y = self.drop(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.drop(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
return x
class MultiHeadAttention(nn.Module):
def __init__(
self,
channels,
out_channels,
n_heads,
p_dropout=0.0,
window_size=None,
heads_share=True,
block_length=None,
proximal_bias=False,
proximal_init=False,
):
super().__init__()
assert channels % n_heads == 0
self.channels = channels
self.out_channels = out_channels
self.n_heads = n_heads
self.p_dropout = p_dropout
self.window_size = window_size
self.heads_share = heads_share
self.block_length = block_length
self.proximal_bias = proximal_bias
self.proximal_init = proximal_init
self.attn = None
self.k_channels = channels // n_heads
self.conv_q = nn.Conv1d(channels, channels, 1)
self.conv_k = nn.Conv1d(channels, channels, 1)
self.conv_v = nn.Conv1d(channels, channels, 1)
self.conv_o = nn.Conv1d(channels, out_channels, 1)
self.drop = nn.Dropout(p_dropout)
if window_size is not None:
n_heads_rel = 1 if heads_share else n_heads
rel_stddev = self.k_channels**-0.5
self.emb_rel_k = nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev
)
self.emb_rel_v = nn.Parameter(
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
* rel_stddev
)
nn.init.xavier_uniform_(self.conv_q.weight)
nn.init.xavier_uniform_(self.conv_k.weight)
nn.init.xavier_uniform_(self.conv_v.weight)
if proximal_init:
with torch.no_grad():
self.conv_k.weight.copy_(self.conv_q.weight)
self.conv_k.bias.copy_(self.conv_q.bias)
def forward(self, x, c, attn_mask=None):
q = self.conv_q(x)
k = self.conv_k(c)
v = self.conv_v(c)
x, self.attn = self.attention(q, k, v, mask=attn_mask)
x = self.conv_o(x)
return x
def attention(self, query, key, value, mask=None):
# reshape [b, d, t] -> [b, n_h, t, d_k]
b, d, t_s, t_t = (*key.size(), query.size(2))
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
if self.window_size is not None:
assert (
t_s == t_t
), "Relative attention is only available for self-attention."
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
rel_logits = self._matmul_with_relative_keys(
query / math.sqrt(self.k_channels), key_relative_embeddings
)
scores_local = self._relative_position_to_absolute_position(rel_logits)
scores = scores + scores_local
if self.proximal_bias:
assert t_s == t_t, "Proximal bias is only available for self-attention."
scores = scores + self._attention_bias_proximal(t_s).to(
device=scores.device, dtype=scores.dtype
)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e4)
if self.block_length is not None:
assert (
t_s == t_t
), "Local attention is only available for self-attention."
block_mask = (
torch.ones_like(scores)
.triu(-self.block_length)
.tril(self.block_length)
)
scores = scores.masked_fill(block_mask == 0, -1e4)
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
p_attn = self.drop(p_attn)
output = torch.matmul(p_attn, value)
if self.window_size is not None:
relative_weights = self._absolute_position_to_relative_position(p_attn)
value_relative_embeddings = self._get_relative_embeddings(
self.emb_rel_v, t_s
)
output = output + self._matmul_with_relative_values(
relative_weights, value_relative_embeddings
)
output = (
output.transpose(2, 3).contiguous().view(b, d, t_t)
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
return output, p_attn
def _matmul_with_relative_values(self, x, y):
"""
x: [b, h, l, m]
y: [h or 1, m, d]
ret: [b, h, l, d]
"""
ret = torch.matmul(x, y.unsqueeze(0))
return ret
def _matmul_with_relative_keys(self, x, y):
"""
x: [b, h, l, d]
y: [h or 1, m, d]
ret: [b, h, l, m]
"""
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
return ret
def _get_relative_embeddings(self, relative_embeddings, length):
2 * self.window_size + 1
# Pad first before slice to avoid using cond ops.
pad_length = max(length - (self.window_size + 1), 0)
slice_start_position = max((self.window_size + 1) - length, 0)
slice_end_position = slice_start_position + 2 * length - 1
if pad_length > 0:
padded_relative_embeddings = F.pad(
relative_embeddings,
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
)
else:
padded_relative_embeddings = relative_embeddings
used_relative_embeddings = padded_relative_embeddings[
:, slice_start_position:slice_end_position
]
return used_relative_embeddings
def _relative_position_to_absolute_position(self, x):
"""
x: [b, h, l, 2*l-1]
ret: [b, h, l, l]
"""
batch, heads, length, _ = x.size()
# Concat columns of pad to shift from relative to absolute indexing.
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
# Concat extra elements so to add up to shape (len+1, 2*len-1).
x_flat = x.view([batch, heads, length * 2 * length])
x_flat = F.pad(
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
)
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
:, :, :length, length - 1 :
]
return x_final
def _absolute_position_to_relative_position(self, x):
"""
x: [b, h, l, l]
ret: [b, h, l, 2*l-1]
"""
batch, heads, length, _ = x.size()
# pad along column
x = F.pad(
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
)
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
return x_final
def _attention_bias_proximal(self, length):
"""Bias for self-attention to encourage attention to close positions.
Args:
length: an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
r = torch.arange(length, dtype=torch.float32)
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
class FFN(nn.Module):
def __init__(
self,
in_channels,
out_channels,
filter_channels,
kernel_size,
p_dropout=0.0,
activation=None,
causal=False,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.activation = activation
self.causal = causal
if causal:
self.padding = self._causal_padding
else:
self.padding = self._same_padding
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
self.drop = nn.Dropout(p_dropout)
def forward(self, x, x_mask):
x = self.conv_1(self.padding(x * x_mask))
if self.activation == "gelu":
x = x * torch.sigmoid(1.702 * x)
else:
x = torch.relu(x)
x = self.drop(x)
x = self.conv_2(self.padding(x * x_mask))
return x * x_mask
def _causal_padding(self, x):
if self.kernel_size == 1:
return x
pad_l = self.kernel_size - 1
pad_r = 0
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
x = F.pad(x, commons.convert_pad_shape(padding))
return x
def _same_padding(self, x):
if self.kernel_size == 1:
return x
pad_l = (self.kernel_size - 1) // 2
pad_r = self.kernel_size // 2
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
x = F.pad(x, commons.convert_pad_shape(padding))
return x
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/commons.py
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from __future__ import annotations
import torch
import torch.nn.functional as F
from torch import Tensor
def slice_segments(x: Tensor, starts: Tensor, length: int) -> Tensor:
if length is None:
return x
length = min(length, x.size(-1))
x_slice = torch.zeros((x.size()[:-1] + (length,)), dtype=x.dtype, device=x.device)
ends = starts + length
for i, (start, end) in enumerate(zip(starts, ends)):
# LOG.debug(i, start, end, x.size(), x[i, ..., start:end].size(), x_slice.size())
# x_slice[i, ...] = x[i, ..., start:end] need to pad
# x_slice[i, ..., :end - start] = x[i, ..., start:end] this does not work
x_slice[i, ...] = F.pad(x[i, ..., start:end], (0, max(0, length - x.size(-1))))
return x_slice
def rand_slice_segments_with_pitch(
x: Tensor, f0: Tensor, x_lengths: Tensor | int | None, segment_size: int | None
):
if segment_size is None:
return x, f0, torch.arange(x.size(0), device=x.device)
if x_lengths is None:
x_lengths = x.size(-1) * torch.ones(
x.size(0), dtype=torch.long, device=x.device
)
# slice_starts = (torch.rand(z.size(0), device=z.device) * (z_lengths - segment_size)).long()
slice_starts = (
torch.rand(x.size(0), device=x.device)
* torch.max(
x_lengths - segment_size, torch.zeros_like(x_lengths, device=x.device)
)
).long()
z_slice = slice_segments(x, slice_starts, segment_size)
f0_slice = slice_segments(f0, slice_starts, segment_size)
return z_slice, f0_slice, slice_starts
def slice_2d_segments(x: Tensor, starts: Tensor, length: int) -> Tensor:
batch_size, num_features, seq_len = x.shape
ends = starts + length
idxs = (
torch.arange(seq_len, device=x.device)
.unsqueeze(0)
.unsqueeze(1)
.repeat(batch_size, num_features, 1)
)
mask = (idxs >= starts.unsqueeze(-1).unsqueeze(-1)) & (
idxs < ends.unsqueeze(-1).unsqueeze(-1)
)
return x[mask].reshape(batch_size, num_features, length)
def slice_1d_segments(x: Tensor, starts: Tensor, length: int) -> Tensor:
batch_size, seq_len = x.shape
ends = starts + length
idxs = torch.arange(seq_len, device=x.device).unsqueeze(0).repeat(batch_size, 1)
mask = (idxs >= starts.unsqueeze(-1)) & (idxs < ends.unsqueeze(-1))
return x[mask].reshape(batch_size, length)
def _slice_segments_v3(x: Tensor, starts: Tensor, length: int) -> Tensor:
shape = x.shape[:-1] + (length,)
ends = starts + length
idxs = torch.arange(x.shape[-1], device=x.device).unsqueeze(0).unsqueeze(0)
unsqueeze_dims = len(shape) - len(
x.shape
) # calculate number of dimensions to unsqueeze
starts = starts.reshape(starts.shape + (1,) * unsqueeze_dims)
ends = ends.reshape(ends.shape + (1,) * unsqueeze_dims)
mask = (idxs >= starts) & (idxs < ends)
return x[mask].reshape(shape)
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
def subsequent_mask(length):
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
return mask
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
def sequence_mask(length, max_length=None):
if max_length is None:
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return x.unsqueeze(0) < length.unsqueeze(1)
def clip_grad_value_(parameters, clip_value, norm_type=2):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
norm_type = float(norm_type)
if clip_value is not None:
clip_value = float(clip_value)
total_norm = 0
for p in parameters:
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item() ** norm_type
if clip_value is not None:
p.grad.data.clamp_(min=-clip_value, max=clip_value)
total_norm = total_norm ** (1.0 / norm_type)
return total_norm
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/__init__.py
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voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/f0.py
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import torch
from torch import nn
from so_vits_svc_fork.modules import attentions as attentions
class F0Decoder(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=0,
):
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.spk_channels = spk_channels
self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
self.decoder = attentions.FFT(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
def forward(self, x, norm_f0, x_mask, spk_emb=None):
x = torch.detach(x)
if spk_emb is not None:
spk_emb = torch.detach(spk_emb)
x = x + self.cond(spk_emb)
x += self.f0_prenet(norm_f0)
x = self.prenet(x) * x_mask
x = self.decoder(x * x_mask, x_mask)
x = self.proj(x) * x_mask
return x
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/hifigan/__init__.py
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from ._models import NSFHifiGANGenerator
__all__ = ["NSFHifiGANGenerator"]
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/hifigan/_models.py
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from logging import getLogger
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import remove_weight_norm, weight_norm
from ...modules import ResBlock1, ResBlock2
from ._utils import init_weights
LOG = getLogger(__name__)
LRELU_SLOPE = 0.1
def padDiff(x):
return F.pad(
F.pad(x, (0, 0, -1, 1), "constant", 0) - x, (0, 0, 0, -1), "constant", 0
)
class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
sine_amp: amplitude of sine-wavefrom (default 0.1)
noise_std: std of Gaussian noise (default 0.003)
voiced_thoreshold: F0 threshold for U/V classification (default 0)
flag_for_pulse: this SinGen is used inside PulseGen (default False)
Note: when flag_for_pulse is True, the first time step of a voiced
segment is always sin(np.pi) or cos(0)
"""
def __init__(
self,
samp_rate,
harmonic_num=0,
sine_amp=0.1,
noise_std=0.003,
voiced_threshold=0,
flag_for_pulse=False,
):
super().__init__()
self.sine_amp = sine_amp
self.noise_std = noise_std
self.harmonic_num = harmonic_num
self.dim = self.harmonic_num + 1
self.sampling_rate = samp_rate
self.voiced_threshold = voiced_threshold
self.flag_for_pulse = flag_for_pulse
def _f02uv(self, f0):
# generate uv signal
uv = (f0 > self.voiced_threshold).type(torch.float32)
return uv
def _f02sine(self, f0_values):
"""f0_values: (batchsize, length, dim)
where dim indicates fundamental tone and overtones
"""
# convert to F0 in rad. The integer part n can be ignored
# because 2 * np.pi * n doesn't affect phase
rad_values = (f0_values / self.sampling_rate) % 1
# initial phase noise (no noise for fundamental component)
rand_ini = torch.rand(
f0_values.shape[0], f0_values.shape[2], device=f0_values.device
)
rand_ini[:, 0] = 0
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
if not self.flag_for_pulse:
# for normal case
# To prevent torch.cumsum numerical overflow,
# it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
# Buffer tmp_over_one_idx indicates the time step to add -1.
# This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
tmp_over_one = torch.cumsum(rad_values, 1) % 1
tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
cumsum_shift = torch.zeros_like(rad_values)
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
sines = torch.sin(
torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
)
else:
# If necessary, make sure that the first time step of every
# voiced segments is sin(pi) or cos(0)
# This is used for pulse-train generation
# identify the last time step in unvoiced segments
uv = self._f02uv(f0_values)
uv_1 = torch.roll(uv, shifts=-1, dims=1)
uv_1[:, -1, :] = 1
u_loc = (uv < 1) * (uv_1 > 0)
# get the instantanouse phase
tmp_cumsum = torch.cumsum(rad_values, dim=1)
# different batch needs to be processed differently
for idx in range(f0_values.shape[0]):
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
# stores the accumulation of i.phase within
# each voiced segments
tmp_cumsum[idx, :, :] = 0
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
# rad_values - tmp_cumsum: remove the accumulation of i.phase
# within the previous voiced segment.
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
# get the sines
sines = torch.cos(i_phase * 2 * np.pi)
return sines
def forward(self, f0):
"""sine_tensor, uv = forward(f0)
input F0: tensor(batchsize=1, length, dim=1)
f0 for unvoiced steps should be 0
output sine_tensor: tensor(batchsize=1, length, dim)
output uv: tensor(batchsize=1, length, 1)
"""
with torch.no_grad():
# f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
# fundamental component
# fn = torch.multiply(
# f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)
# )
fn = torch.multiply(
f0, torch.arange(1, self.harmonic_num + 2).to(f0.device).to(f0.dtype)
)
# generate sine waveforms
sine_waves = self._f02sine(fn) * self.sine_amp
# generate uv signal
# uv = torch.ones(f0.shape)
# uv = uv * (f0 > self.voiced_threshold)
uv = self._f02uv(f0)
# noise: for unvoiced should be similar to sine_amp
# std = self.sine_amp/3 -> max value ~ self.sine_amp
# . for voiced regions is self.noise_std
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
noise = noise_amp * torch.randn_like(sine_waves)
# first: set the unvoiced part to 0 by uv
# then: additive noise
sine_waves = sine_waves * uv + noise
return sine_waves, uv, noise
class SourceModuleHnNSF(torch.nn.Module):
"""SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sine source signal (default: 0.1)
add_noise_std: std of additive Gaussian noise (default: 0.003)
note that amplitude of noise in unvoiced is decided
by sine_amp
voiced_threshold: threshold to set U/V given F0 (default: 0)
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
uv (batchsize, length, 1)
"""
def __init__(
self,
sampling_rate,
harmonic_num=0,
sine_amp=0.1,
add_noise_std=0.003,
voiced_threshod=0,
):
super().__init__()
self.sine_amp = sine_amp
self.noise_std = add_noise_std
# to produce sine waveforms
self.l_sin_gen = SineGen(
sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
)
# to merge source harmonics into a single excitation
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
self.l_tanh = torch.nn.Tanh()
def forward(self, x):
"""
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
"""
# source for harmonic branch
sine_wavs, uv, _ = self.l_sin_gen(x)
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
# source for noise branch, in the same shape as uv
noise = torch.randn_like(uv) * self.sine_amp / 3
return sine_merge, noise, uv
class NSFHifiGANGenerator(torch.nn.Module):
def __init__(self, h):
super().__init__()
self.h = h
self.num_kernels = len(h["resblock_kernel_sizes"])
self.num_upsamples = len(h["upsample_rates"])
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
self.m_source = SourceModuleHnNSF(
sampling_rate=h["sampling_rate"], harmonic_num=8
)
self.noise_convs = nn.ModuleList()
self.conv_pre = weight_norm(
Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3)
)
resblock = ResBlock1 if h["resblock"] == "1" else ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(
zip(h["upsample_rates"], h["upsample_kernel_sizes"])
):
c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
self.ups.append(
weight_norm(
ConvTranspose1d(
h["upsample_initial_channel"] // (2**i),
h["upsample_initial_channel"] // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
if i + 1 < len(h["upsample_rates"]): #
stride_f0 = np.prod(h["upsample_rates"][i + 1 :])
self.noise_convs.append(
Conv1d(
1,
c_cur,
kernel_size=stride_f0 * 2,
stride=stride_f0,
padding=stride_f0 // 2,
)
)
else:
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = h["upsample_initial_channel"] // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])
):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
self.cond = nn.Conv1d(h["gin_channels"], h["upsample_initial_channel"], 1)
def forward(self, x, f0, g=None):
# LOG.info(1,x.shape,f0.shape,f0[:, None].shape)
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
# LOG.info(2,f0.shape)
har_source, noi_source, uv = self.m_source(f0)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
x = x + self.cond(g)
# LOG.info(124,x.shape,har_source.shape)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
# LOG.info(3,x.shape)
x = self.ups[i](x)
x_source = self.noise_convs[i](har_source)
# LOG.info(4,x_source.shape,har_source.shape,x.shape)
x = x + x_source
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
LOG.info("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/hifigan/_utils.py
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from logging import getLogger
# matplotlib.use("Agg")
LOG = getLogger(__name__)
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/mb_istft/__init__.py
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from ._generators import (
Multiband_iSTFT_Generator,
Multistream_iSTFT_Generator,
iSTFT_Generator,
)
from ._loss import subband_stft_loss
from ._pqmf import PQMF
__all__ = [
"subband_stft_loss",
"PQMF",
"iSTFT_Generator",
"Multiband_iSTFT_Generator",
"Multistream_iSTFT_Generator",
]
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/mb_istft/_generators.py
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import math
import torch
from torch import nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from ....modules import modules
from ....modules.commons import get_padding, init_weights
from ._pqmf import PQMF
from ._stft import TorchSTFT
class iSTFT_Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gen_istft_n_fft,
gen_istft_hop_size,
gin_channels=0,
):
super().__init__()
# self.h = h
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = weight_norm(
Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.post_n_fft = self.gen_istft_n_fft
self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
self.stft = TorchSTFT(
filter_length=self.gen_istft_n_fft,
hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft,
)
def forward(self, x, g=None):
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.conv_post(x)
spec = torch.exp(x[:, : self.post_n_fft // 2 + 1, :])
phase = math.pi * torch.sin(x[:, self.post_n_fft // 2 + 1 :, :])
out = self.stft.inverse(spec, phase).to(x.device)
return out, None
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post)
class Multiband_iSTFT_Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gen_istft_n_fft,
gen_istft_hop_size,
subbands,
gin_channels=0,
):
super().__init__()
# self.h = h
self.subbands = subbands
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = weight_norm(
Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.post_n_fft = gen_istft_n_fft
self.ups.apply(init_weights)
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
self.reshape_pixelshuffle = []
self.subband_conv_post = weight_norm(
Conv1d(ch, self.subbands * (self.post_n_fft + 2), 7, 1, padding=3)
)
self.subband_conv_post.apply(init_weights)
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
def forward(self, x, g=None):
stft = TorchSTFT(
filter_length=self.gen_istft_n_fft,
hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft,
).to(x.device)
pqmf = PQMF(x.device, subbands=self.subbands).to(x.device, dtype=x.dtype)
x = self.conv_pre(x) # [B, ch, length]
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.subband_conv_post(x)
x = torch.reshape(
x, (x.shape[0], self.subbands, x.shape[1] // self.subbands, x.shape[-1])
)
spec = torch.exp(x[:, :, : self.post_n_fft // 2 + 1, :])
phase = math.pi * torch.sin(x[:, :, self.post_n_fft // 2 + 1 :, :])
y_mb_hat = stft.inverse(
torch.reshape(
spec,
(
spec.shape[0] * self.subbands,
self.gen_istft_n_fft // 2 + 1,
spec.shape[-1],
),
),
torch.reshape(
phase,
(
phase.shape[0] * self.subbands,
self.gen_istft_n_fft // 2 + 1,
phase.shape[-1],
),
),
)
y_mb_hat = torch.reshape(
y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1])
)
y_mb_hat = y_mb_hat.squeeze(-2)
y_g_hat = pqmf.synthesis(y_mb_hat)
return y_g_hat, y_mb_hat
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
class Multistream_iSTFT_Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gen_istft_n_fft,
gen_istft_hop_size,
subbands,
gin_channels=0,
):
super().__init__()
# self.h = h
self.subbands = subbands
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = weight_norm(
Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.post_n_fft = gen_istft_n_fft
self.ups.apply(init_weights)
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
self.reshape_pixelshuffle = []
self.subband_conv_post = weight_norm(
Conv1d(ch, self.subbands * (self.post_n_fft + 2), 7, 1, padding=3)
)
self.subband_conv_post.apply(init_weights)
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
updown_filter = torch.zeros(
(self.subbands, self.subbands, self.subbands)
).float()
for k in range(self.subbands):
updown_filter[k, k, 0] = 1.0
self.register_buffer("updown_filter", updown_filter)
self.multistream_conv_post = weight_norm(
Conv1d(
self.subbands, 1, kernel_size=63, bias=False, padding=get_padding(63, 1)
)
)
self.multistream_conv_post.apply(init_weights)
def forward(self, x, g=None):
stft = TorchSTFT(
filter_length=self.gen_istft_n_fft,
hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft,
).to(x.device)
# pqmf = PQMF(x.device)
x = self.conv_pre(x) # [B, ch, length]
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.subband_conv_post(x)
x = torch.reshape(
x, (x.shape[0], self.subbands, x.shape[1] // self.subbands, x.shape[-1])
)
spec = torch.exp(x[:, :, : self.post_n_fft // 2 + 1, :])
phase = math.pi * torch.sin(x[:, :, self.post_n_fft // 2 + 1 :, :])
y_mb_hat = stft.inverse(
torch.reshape(
spec,
(
spec.shape[0] * self.subbands,
self.gen_istft_n_fft // 2 + 1,
spec.shape[-1],
),
),
torch.reshape(
phase,
(
phase.shape[0] * self.subbands,
self.gen_istft_n_fft // 2 + 1,
phase.shape[-1],
),
),
)
y_mb_hat = torch.reshape(
y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1])
)
y_mb_hat = y_mb_hat.squeeze(-2)
y_mb_hat = F.conv_transpose1d(
y_mb_hat,
self.updown_filter.to(x.device) * self.subbands,
stride=self.subbands,
)
y_g_hat = self.multistream_conv_post(y_mb_hat)
return y_g_hat, y_mb_hat
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/mb_istft/_loss.py
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from ._stft_loss import MultiResolutionSTFTLoss
def subband_stft_loss(h, y_mb, y_hat_mb):
sub_stft_loss = MultiResolutionSTFTLoss(
h.train.fft_sizes, h.train.hop_sizes, h.train.win_lengths
)
y_mb = y_mb.view(-1, y_mb.size(2))
y_hat_mb = y_hat_mb.view(-1, y_hat_mb.size(2))
sub_sc_loss, sub_mag_loss = sub_stft_loss(y_hat_mb[:, : y_mb.size(-1)], y_mb)
return sub_sc_loss + sub_mag_loss
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py
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# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Pseudo QMF modules."""
import numpy as np
import torch
import torch.nn.functional as F
from scipy.signal import kaiser
def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
"""Design prototype filter for PQMF.
This method is based on `A Kaiser window approach for the design of prototype
filters of cosine modulated filterbanks`_.
Args:
taps (int): The number of filter taps.
cutoff_ratio (float): Cut-off frequency ratio.
beta (float): Beta coefficient for kaiser window.
Returns:
ndarray: Impluse response of prototype filter (taps + 1,).
.. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
https://ieeexplore.ieee.org/abstract/document/681427
"""
# check the arguments are valid
assert taps % 2 == 0, "The number of taps mush be even number."
assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
# make initial filter
omega_c = np.pi * cutoff_ratio
with np.errstate(invalid="ignore"):
h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) / (
np.pi * (np.arange(taps + 1) - 0.5 * taps)
)
h_i[taps // 2] = np.cos(0) * cutoff_ratio # fix nan due to indeterminate form
# apply kaiser window
w = kaiser(taps + 1, beta)
h = h_i * w
return h
class PQMF(torch.nn.Module):
"""PQMF module.
This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
.. _`Near-perfect-reconstruction pseudo-QMF banks`:
https://ieeexplore.ieee.org/document/258122
"""
def __init__(self, device, subbands=8, taps=62, cutoff_ratio=0.15, beta=9.0):
"""Initialize PQMF module.
Args:
subbands (int): The number of subbands.
taps (int): The number of filter taps.
cutoff_ratio (float): Cut-off frequency ratio.
beta (float): Beta coefficient for kaiser window.
"""
super().__init__()
# define filter coefficient
h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
h_analysis = np.zeros((subbands, len(h_proto)))
h_synthesis = np.zeros((subbands, len(h_proto)))
for k in range(subbands):
h_analysis[k] = (
2
* h_proto
* np.cos(
(2 * k + 1)
* (np.pi / (2 * subbands))
* (np.arange(taps + 1) - ((taps - 1) / 2))
+ (-1) ** k * np.pi / 4
)
)
h_synthesis[k] = (
2
* h_proto
* np.cos(
(2 * k + 1)
* (np.pi / (2 * subbands))
* (np.arange(taps + 1) - ((taps - 1) / 2))
- (-1) ** k * np.pi / 4
)
)
# convert to tensor
analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device)
synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device)
# register coefficients as buffer
self.register_buffer("analysis_filter", analysis_filter)
self.register_buffer("synthesis_filter", synthesis_filter)
# filter for downsampling & upsampling
updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device)
for k in range(subbands):
updown_filter[k, k, 0] = 1.0
self.register_buffer("updown_filter", updown_filter)
self.subbands = subbands
# keep padding info
self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
def analysis(self, x):
"""Analysis with PQMF.
Args:
x (Tensor): Input tensor (B, 1, T).
Returns:
Tensor: Output tensor (B, subbands, T // subbands).
"""
x = F.conv1d(self.pad_fn(x), self.analysis_filter)
return F.conv1d(x, self.updown_filter, stride=self.subbands)
def synthesis(self, x):
"""Synthesis with PQMF.
Args:
x (Tensor): Input tensor (B, subbands, T // subbands).
Returns:
Tensor: Output tensor (B, 1, T).
"""
# NOTE(kan-bayashi): Power will be dreased so here multiply by # subbands.
# Not sure this is the correct way, it is better to check again.
# TODO(kan-bayashi): Understand the reconstruction procedure
x = F.conv_transpose1d(
x, self.updown_filter * self.subbands, stride=self.subbands
)
return F.conv1d(self.pad_fn(x), self.synthesis_filter)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/mb_istft/_stft.py
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"""
BSD 3-Clause License
Copyright (c) 2017, Prem Seetharaman
All rights reserved.
* Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import librosa.util as librosa_util
import numpy as np
import torch
import torch.nn.functional as F
from librosa.util import pad_center, tiny
from scipy.signal import get_window
from torch.autograd import Variable
def window_sumsquare(
window,
n_frames,
hop_length=200,
win_length=800,
n_fft=800,
dtype=np.float32,
norm=None,
):
"""
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
Window specification, as in `get_window`
n_frames : int > 0
The number of analysis frames
hop_length : int > 0
The number of samples to advance between frames
win_length : [optional]
The length of the window function. By default, this matches `n_fft`.
n_fft : int > 0
The length of each analysis frame.
dtype : np.dtype
The data type of the output
Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
The sum-squared envelope of the window function
"""
if win_length is None:
win_length = n_fft
n = n_fft + hop_length * (n_frames - 1)
x = np.zeros(n, dtype=dtype)
# Compute the squared window at the desired length
win_sq = get_window(window, win_length, fftbins=True)
win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2
win_sq = librosa_util.pad_center(win_sq, n_fft)
# Fill the envelope
for i in range(n_frames):
sample = i * hop_length
x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
return x
class STFT(torch.nn.Module):
"""adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
def __init__(
self, filter_length=800, hop_length=200, win_length=800, window="hann"
):
super().__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length
self.window = window
self.forward_transform = None
scale = self.filter_length / self.hop_length
fourier_basis = np.fft.fft(np.eye(self.filter_length))
cutoff = int(self.filter_length / 2 + 1)
fourier_basis = np.vstack(
[np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
)
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
inverse_basis = torch.FloatTensor(
np.linalg.pinv(scale * fourier_basis).T[:, None, :]
)
if window is not None:
assert filter_length >= win_length
# get window and zero center pad it to filter_length
fft_window = get_window(window, win_length, fftbins=True)
fft_window = pad_center(fft_window, filter_length)
fft_window = torch.from_numpy(fft_window).float()
# window the bases
forward_basis *= fft_window
inverse_basis *= fft_window
self.register_buffer("forward_basis", forward_basis.float())
self.register_buffer("inverse_basis", inverse_basis.float())
def transform(self, input_data):
num_batches = input_data.size(0)
num_samples = input_data.size(1)
self.num_samples = num_samples
# similar to librosa, reflect-pad the input
input_data = input_data.view(num_batches, 1, num_samples)
input_data = F.pad(
input_data.unsqueeze(1),
(int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
mode="reflect",
)
input_data = input_data.squeeze(1)
forward_transform = F.conv1d(
input_data,
Variable(self.forward_basis, requires_grad=False),
stride=self.hop_length,
padding=0,
)
cutoff = int((self.filter_length / 2) + 1)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part**2 + imag_part**2)
phase = torch.autograd.Variable(torch.atan2(imag_part.data, real_part.data))
return magnitude, phase
def inverse(self, magnitude, phase):
recombine_magnitude_phase = torch.cat(
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
)
inverse_transform = F.conv_transpose1d(
recombine_magnitude_phase,
Variable(self.inverse_basis, requires_grad=False),
stride=self.hop_length,
padding=0,
)
if self.window is not None:
window_sum = window_sumsquare(
self.window,
magnitude.size(-1),
hop_length=self.hop_length,
win_length=self.win_length,
n_fft=self.filter_length,
dtype=np.float32,
)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0]
)
window_sum = torch.autograd.Variable(
torch.from_numpy(window_sum), requires_grad=False
)
window_sum = window_sum.to(inverse_transform.device())
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
approx_nonzero_indices
]
# scale by hop ratio
inverse_transform *= float(self.filter_length) / self.hop_length
inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :]
inverse_transform = inverse_transform[:, :, : -int(self.filter_length / 2) :]
return inverse_transform
def forward(self, input_data):
self.magnitude, self.phase = self.transform(input_data)
reconstruction = self.inverse(self.magnitude, self.phase)
return reconstruction
class TorchSTFT(torch.nn.Module):
def __init__(
self, filter_length=800, hop_length=200, win_length=800, window="hann"
):
super().__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length
self.window = torch.from_numpy(
get_window(window, win_length, fftbins=True).astype(np.float32)
)
def transform(self, input_data):
forward_transform = torch.stft(
input_data,
self.filter_length,
self.hop_length,
self.win_length,
window=self.window,
return_complex=True,
)
return torch.abs(forward_transform), torch.angle(forward_transform)
def inverse(self, magnitude, phase):
inverse_transform = torch.istft(
magnitude * torch.exp(phase * 1j),
self.filter_length,
self.hop_length,
self.win_length,
window=self.window.to(magnitude.device),
)
return inverse_transform.unsqueeze(
-2
) # unsqueeze to stay consistent with conv_transpose1d implementation
def forward(self, input_data):
self.magnitude, self.phase = self.transform(input_data)
reconstruction = self.inverse(self.magnitude, self.phase)
return reconstruction
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/decoders/mb_istft/_stft_loss.py
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# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""STFT-based Loss modules."""
import torch
import torch.nn.functional as F
def stft(x, fft_size, hop_size, win_length, window):
"""Perform STFT and convert to magnitude spectrogram.
Args:
x (Tensor): Input signal tensor (B, T).
fft_size (int): FFT size.
hop_size (int): Hop size.
win_length (int): Window length.
window (str): Window function type.
Returns:
Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
"""
x_stft = torch.stft(
x, fft_size, hop_size, win_length, window.to(x.device), return_complex=False
)
real = x_stft[..., 0]
imag = x_stft[..., 1]
# NOTE(kan-bayashi): clamp is needed to avoid nan or inf
return torch.sqrt(torch.clamp(real**2 + imag**2, min=1e-7)).transpose(2, 1)
class SpectralConvergengeLoss(torch.nn.Module):
"""Spectral convergence loss module."""
def __init__(self):
"""Initialize spectral convergence loss module."""
super().__init__()
def forward(self, x_mag, y_mag):
"""Calculate forward propagation.
Args:
x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
Returns:
Tensor: Spectral convergence loss value.
"""
return torch.norm(y_mag - x_mag) / torch.norm(
y_mag
) # MB-iSTFT-VITS changed here due to codespell
class LogSTFTMagnitudeLoss(torch.nn.Module):
"""Log STFT magnitude loss module."""
def __init__(self):
"""Initialize los STFT magnitude loss module."""
super().__init__()
def forward(self, x_mag, y_mag):
"""Calculate forward propagation.
Args:
x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
Returns:
Tensor: Log STFT magnitude loss value.
"""
return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
class STFTLoss(torch.nn.Module):
"""STFT loss module."""
def __init__(
self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"
):
"""Initialize STFT loss module."""
super().__init__()
self.fft_size = fft_size
self.shift_size = shift_size
self.win_length = win_length
self.window = getattr(torch, window)(win_length)
self.spectral_convergenge_loss = SpectralConvergengeLoss()
self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
def forward(self, x, y):
"""Calculate forward propagation.
Args:
x (Tensor): Predicted signal (B, T).
y (Tensor): Groundtruth signal (B, T).
Returns:
Tensor: Spectral convergence loss value.
Tensor: Log STFT magnitude loss value.
"""
x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
return sc_loss, mag_loss
class MultiResolutionSTFTLoss(torch.nn.Module):
"""Multi resolution STFT loss module."""
def __init__(
self,
fft_sizes=[1024, 2048, 512],
hop_sizes=[120, 240, 50],
win_lengths=[600, 1200, 240],
window="hann_window",
):
"""Initialize Multi resolution STFT loss module.
Args:
fft_sizes (list): List of FFT sizes.
hop_sizes (list): List of hop sizes.
win_lengths (list): List of window lengths.
window (str): Window function type.
"""
super().__init__()
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
self.stft_losses = torch.nn.ModuleList()
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
self.stft_losses += [STFTLoss(fs, ss, wl, window)]
def forward(self, x, y):
"""Calculate forward propagation.
Args:
x (Tensor): Predicted signal (B, T).
y (Tensor): Groundtruth signal (B, T).
Returns:
Tensor: Multi resolution spectral convergence loss value.
Tensor: Multi resolution log STFT magnitude loss value.
"""
sc_loss = 0.0
mag_loss = 0.0
for f in self.stft_losses:
sc_l, mag_l = f(x, y)
sc_loss += sc_l
mag_loss += mag_l
sc_loss /= len(self.stft_losses)
mag_loss /= len(self.stft_losses)
return sc_loss, mag_loss
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/descriminators.py
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import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
from so_vits_svc_fork.modules import modules as modules
from so_vits_svc_fork.modules.commons import get_padding
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super().__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(
Conv2d(
1,
32,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
32,
128,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
128,
512,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
512,
1024,
(kernel_size, 1),
(stride, 1),
padding=(get_padding(kernel_size, 1), 0),
)
),
norm_f(
Conv2d(
1024,
1024,
(kernel_size, 1),
1,
padding=(get_padding(kernel_size, 1), 0),
)
),
]
)
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super().__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super().__init__()
periods = [2, 3, 5, 7, 11]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
]
self.discriminators = nn.ModuleList(discs)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class MultiScaleDiscriminator(torch.nn.Module):
def __init__(self):
super().__init__()
self.discriminators = nn.ModuleList(
[
DiscriminatorS(use_spectral_norm=True),
DiscriminatorS(),
DiscriminatorS(),
]
)
self.meanpools = nn.ModuleList(
[AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]
)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
if i != 0:
y = self.meanpools[i - 1](y)
y_hat = self.meanpools[i - 1](y_hat)
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/encoders.py
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import torch
from torch import nn
from so_vits_svc_fork.modules import attentions as attentions
from so_vits_svc_fork.modules import commons as commons
from so_vits_svc_fork.modules import modules as modules
class SpeakerEncoder(torch.nn.Module):
def __init__(
self,
mel_n_channels=80,
model_num_layers=3,
model_hidden_size=256,
model_embedding_size=256,
):
super().__init__()
self.lstm = nn.LSTM(
mel_n_channels, model_hidden_size, model_num_layers, batch_first=True
)
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
self.relu = nn.ReLU()
def forward(self, mels):
self.lstm.flatten_parameters()
_, (hidden, _) = self.lstm(mels)
embeds_raw = self.relu(self.linear(hidden[-1]))
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
mel_slices = []
for i in range(0, total_frames - partial_frames, partial_hop):
mel_range = torch.arange(i, i + partial_frames)
mel_slices.append(mel_range)
return mel_slices
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
mel_len = mel.size(1)
last_mel = mel[:, -partial_frames:]
if mel_len > partial_frames:
mel_slices = self.compute_partial_slices(
mel_len, partial_frames, partial_hop
)
mels = list(mel[:, s] for s in mel_slices)
mels.append(last_mel)
mels = torch.stack(tuple(mels), 0).squeeze(1)
with torch.no_grad():
partial_embeds = self(mels)
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
# embed = embed / torch.linalg.norm(embed, 2)
else:
with torch.no_grad():
embed = self(last_mel)
return embed
class Encoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = gin_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = modules.WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
# print(x.shape,x_lengths.shape)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
class TextEncoder(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None,
):
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.gin_channels = gin_channels
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
self.f0_emb = nn.Embedding(256, hidden_channels)
self.enc_ = attentions.Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
def forward(self, x, x_mask, f0=None, noice_scale=1):
x = x + self.f0_emb(f0).transpose(1, 2)
x = self.enc_(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
return z, m, logs, x_mask
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/flows.py
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from torch import nn
from so_vits_svc_fork.modules import modules as modules
class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/losses.py
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import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg**2)
loss += r_loss + g_loss
r_losses.append(r_loss.item())
g_losses.append(g_loss.item())
return loss, r_losses, g_losses
def generator_loss(disc_outputs):
loss = 0
gen_losses = []
for dg in disc_outputs:
dg = dg.float()
l = torch.mean((1 - dg) ** 2)
gen_losses.append(l)
loss += l
return loss, gen_losses
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
"""
z_p, logs_q: [b, h, t_t]
m_p, logs_p: [b, h, t_t]
"""
z_p = z_p.float()
logs_q = logs_q.float()
m_p = m_p.float()
logs_p = logs_p.float()
z_mask = z_mask.float()
# print(logs_p)
kl = logs_p - logs_q - 0.5
kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
kl = torch.sum(kl * z_mask)
l = kl / torch.sum(z_mask)
return l
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/mel_processing.py
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"""from logging import getLogger
import torch
import torch.utils.data
import torchaudio
LOG = getLogger(__name__)
from ..hparams import HParams
def spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
return torchaudio.transforms.Spectrogram(
n_fft=hps.data.filter_length,
win_length=hps.data.win_length,
hop_length=hps.data.hop_length,
power=1.0,
window_fn=torch.hann_window,
normalized=False,
).to(audio.device)(audio)
def spec_to_mel_torch(spec: torch.Tensor, hps: HParams) -> torch.Tensor:
return torchaudio.transforms.MelScale(
n_mels=hps.data.n_mel_channels,
sample_rate=hps.data.sampling_rate,
f_min=hps.data.mel_fmin,
f_max=hps.data.mel_fmax,
).to(spec.device)(spec)
def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
return torchaudio.transforms.MelSpectrogram(
sample_rate=hps.data.sampling_rate,
n_fft=hps.data.filter_length,
n_mels=hps.data.n_mel_channels,
win_length=hps.data.win_length,
hop_length=hps.data.hop_length,
f_min=hps.data.mel_fmin,
f_max=hps.data.mel_fmax,
power=1.0,
window_fn=torch.hann_window,
normalized=False,
).to(audio.device)(audio)"""
from logging import getLogger
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
LOG = getLogger(__name__)
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, hps, center=False):
if torch.min(y) < -1.0:
LOG.info("min value is ", torch.min(y))
if torch.max(y) > 1.0:
LOG.info("max value is ", torch.max(y))
n_fft = hps.data.filter_length
hop_size = hps.data.hop_length
win_size = hps.data.win_length
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def spec_to_mel_torch(spec, hps):
sampling_rate = hps.data.sampling_rate
n_fft = hps.data.filter_length
num_mels = hps.data.n_mel_channels
fmin = hps.data.mel_fmin
fmax = hps.data.mel_fmax
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=spec.dtype, device=spec.device
)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
def mel_spectrogram_torch(y, hps, center=False):
sampling_rate = hps.data.sampling_rate
n_fft = hps.data.filter_length
num_mels = hps.data.n_mel_channels
fmin = hps.data.mel_fmin
fmax = hps.data.mel_fmax
hop_size = hps.data.hop_length
win_size = hps.data.win_length
if torch.min(y) < -1.0:
LOG.info(f"min value is {torch.min(y)}")
if torch.max(y) > 1.0:
LOG.info(f"max value is {torch.max(y)}")
global mel_basis, hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=y.dtype, device=y.device
)
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/modules.py
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import torch
from torch import nn
from torch.nn import Conv1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from so_vits_svc_fork.modules import commons
from so_vits_svc_fork.modules.commons import get_padding, init_weights
LRELU_SLOPE = 0.1
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
class ConvReluNorm(nn.Module):
def __init__(
self,
in_channels,
hidden_channels,
out_channels,
kernel_size,
n_layers,
p_dropout,
):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_dropout = p_dropout
assert n_layers > 1, "Number of layers should be larger than 0."
self.conv_layers = nn.ModuleList()
self.norm_layers = nn.ModuleList()
self.conv_layers.append(
nn.Conv1d(
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
)
)
self.norm_layers.append(LayerNorm(hidden_channels))
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
for _ in range(n_layers - 1):
self.conv_layers.append(
nn.Conv1d(
hidden_channels,
hidden_channels,
kernel_size,
padding=kernel_size // 2,
)
)
self.norm_layers.append(LayerNorm(hidden_channels))
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
self.proj.weight.data.zero_()
self.proj.bias.data.zero_()
def forward(self, x, x_mask):
x_org = x
for i in range(self.n_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x)
x = self.relu_drop(x)
x = x_org + self.proj(x)
return x * x_mask
class DDSConv(nn.Module):
"""
Dialted and Depth-Separable Convolution
"""
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
super().__init__()
self.channels = channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_dropout = p_dropout
self.drop = nn.Dropout(p_dropout)
self.convs_sep = nn.ModuleList()
self.convs_1x1 = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(n_layers):
dilation = kernel_size**i
padding = (kernel_size * dilation - dilation) // 2
self.convs_sep.append(
nn.Conv1d(
channels,
channels,
kernel_size,
groups=channels,
dilation=dilation,
padding=padding,
)
)
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
self.norms_1.append(LayerNorm(channels))
self.norms_2.append(LayerNorm(channels))
def forward(self, x, x_mask, g=None):
if g is not None:
x = x + g
for i in range(self.n_layers):
y = self.convs_sep[i](x * x_mask)
y = self.norms_1[i](y)
y = F.gelu(y)
y = self.convs_1x1[i](y)
y = self.norms_2[i](y)
y = F.gelu(y)
y = self.drop(y)
x = x + y
return x * x_mask
class WN(torch.nn.Module):
def __init__(
self,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
p_dropout=0,
):
super().__init__()
assert kernel_size % 2 == 1
self.hidden_channels = hidden_channels
self.kernel_size = (kernel_size,)
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = gin_channels
self.p_dropout = p_dropout
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.drop = nn.Dropout(p_dropout)
if gin_channels != 0:
cond_layer = torch.nn.Conv1d(
gin_channels, 2 * hidden_channels * n_layers, 1
)
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
for i in range(n_layers):
dilation = dilation_rate**i
padding = int((kernel_size * dilation - dilation) / 2)
in_layer = torch.nn.Conv1d(
hidden_channels,
2 * hidden_channels,
kernel_size,
dilation=dilation,
padding=padding,
)
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
self.in_layers.append(in_layer)
# last one is not necessary
if i < n_layers - 1:
res_skip_channels = 2 * hidden_channels
else:
res_skip_channels = hidden_channels
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
self.res_skip_layers.append(res_skip_layer)
def forward(self, x, x_mask, g=None, **kwargs):
output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None:
g = self.cond_layer(g)
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
if g is not None:
cond_offset = i * 2 * self.hidden_channels
g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
else:
g_l = torch.zeros_like(x_in)
acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
acts = self.drop(acts)
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
res_acts = res_skip_acts[:, : self.hidden_channels, :]
x = (x + res_acts) * x_mask
output = output + res_skip_acts[:, self.hidden_channels :, :]
else:
output = output + res_skip_acts
return output * x_mask
def remove_weight_norm(self):
if self.gin_channels != 0:
torch.nn.utils.remove_weight_norm(self.cond_layer)
for l in self.in_layers:
torch.nn.utils.remove_weight_norm(l)
for l in self.res_skip_layers:
torch.nn.utils.remove_weight_norm(l)
class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super().__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x, x_mask=None):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c2(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super().__init__()
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
]
)
self.convs.apply(init_weights)
def forward(self, x, x_mask=None):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
class Log(nn.Module):
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
class Flip(nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x
class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels, 1))
self.logs = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
class ResidualCouplingLayer(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by 2"
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.half_channels = channels // 2
self.mean_only = mean_only
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
self.enc = WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=p_dropout,
gin_channels=gin_channels,
)
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/modules/synthesizers.py
+233
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import warnings
from logging import getLogger
from typing import Any, Literal, Sequence
import torch
from torch import nn
import so_vits_svc_fork.f0
from so_vits_svc_fork.f0 import f0_to_coarse
from so_vits_svc_fork.modules import commons as commons
from so_vits_svc_fork.modules.decoders.f0 import F0Decoder
from so_vits_svc_fork.modules.decoders.hifigan import NSFHifiGANGenerator
from so_vits_svc_fork.modules.decoders.mb_istft import (
Multiband_iSTFT_Generator,
Multistream_iSTFT_Generator,
iSTFT_Generator,
)
from so_vits_svc_fork.modules.encoders import Encoder, TextEncoder
from so_vits_svc_fork.modules.flows import ResidualCouplingBlock
LOG = getLogger(__name__)
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(
self,
spec_channels: int,
segment_size: int,
inter_channels: int,
hidden_channels: int,
filter_channels: int,
n_heads: int,
n_layers: int,
kernel_size: int,
p_dropout: int,
resblock: str,
resblock_kernel_sizes: Sequence[int],
resblock_dilation_sizes: Sequence[Sequence[int]],
upsample_rates: Sequence[int],
upsample_initial_channel: int,
upsample_kernel_sizes: Sequence[int],
gin_channels: int,
ssl_dim: int,
n_speakers: int,
sampling_rate: int = 44100,
type_: Literal["hifi-gan", "istft", "ms-istft", "mb-istft"] = "hifi-gan",
gen_istft_n_fft: int = 16,
gen_istft_hop_size: int = 4,
subbands: int = 4,
**kwargs: Any,
):
super().__init__()
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.gin_channels = gin_channels
self.ssl_dim = ssl_dim
self.n_speakers = n_speakers
self.sampling_rate = sampling_rate
self.type_ = type_
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
self.subbands = subbands
if kwargs:
warnings.warn(f"Unused arguments: {kwargs}")
self.emb_g = nn.Embedding(n_speakers, gin_channels)
if ssl_dim is None:
self.pre = nn.LazyConv1d(hidden_channels, kernel_size=5, padding=2)
else:
self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
self.enc_p = TextEncoder(
inter_channels,
hidden_channels,
filter_channels=filter_channels,
n_heads=n_heads,
n_layers=n_layers,
kernel_size=kernel_size,
p_dropout=p_dropout,
)
LOG.info(f"Decoder type: {type_}")
if type_ == "hifi-gan":
hps = {
"sampling_rate": sampling_rate,
"inter_channels": inter_channels,
"resblock": resblock,
"resblock_kernel_sizes": resblock_kernel_sizes,
"resblock_dilation_sizes": resblock_dilation_sizes,
"upsample_rates": upsample_rates,
"upsample_initial_channel": upsample_initial_channel,
"upsample_kernel_sizes": upsample_kernel_sizes,
"gin_channels": gin_channels,
}
self.dec = NSFHifiGANGenerator(h=hps)
self.mb = False
else:
hps = {
"initial_channel": inter_channels,
"resblock": resblock,
"resblock_kernel_sizes": resblock_kernel_sizes,
"resblock_dilation_sizes": resblock_dilation_sizes,
"upsample_rates": upsample_rates,
"upsample_initial_channel": upsample_initial_channel,
"upsample_kernel_sizes": upsample_kernel_sizes,
"gin_channels": gin_channels,
"gen_istft_n_fft": gen_istft_n_fft,
"gen_istft_hop_size": gen_istft_hop_size,
"subbands": subbands,
}
# gen_istft_n_fft, gen_istft_hop_size, subbands
if type_ == "istft":
del hps["subbands"]
self.dec = iSTFT_Generator(**hps)
elif type_ == "ms-istft":
self.dec = Multistream_iSTFT_Generator(**hps)
elif type_ == "mb-istft":
self.dec = Multiband_iSTFT_Generator(**hps)
else:
raise ValueError(f"Unknown type: {type_}")
self.mb = True
self.enc_q = Encoder(
spec_channels,
inter_channels,
hidden_channels,
5,
1,
16,
gin_channels=gin_channels,
)
self.flow = ResidualCouplingBlock(
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
)
self.f0_decoder = F0Decoder(
1,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=gin_channels,
)
self.emb_uv = nn.Embedding(2, hidden_channels)
def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
g = self.emb_g(g).transpose(1, 2)
# ssl prenet
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(
c.dtype
)
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
# f0 predict
lf0 = 2595.0 * torch.log10(1.0 + f0.unsqueeze(1) / 700.0) / 500
norm_lf0 = so_vits_svc_fork.f0.normalize_f0(lf0, x_mask, uv)
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
# encoder
z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
# flow
z_p = self.flow(z, spec_mask, g=g)
z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(
z, f0, spec_lengths, self.segment_size
)
# MB-iSTFT-VITS
if self.mb:
o, o_mb = self.dec(z_slice, g=g)
# HiFi-GAN
else:
o = self.dec(z_slice, g=g, f0=pitch_slice)
o_mb = None
return (
o,
o_mb,
ids_slice,
spec_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
pred_lf0,
norm_lf0,
lf0,
)
def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
g = self.emb_g(g).transpose(1, 2)
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(
c.dtype
)
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
if predict_f0:
lf0 = 2595.0 * torch.log10(1.0 + f0.unsqueeze(1) / 700.0) / 500
norm_lf0 = so_vits_svc_fork.f0.normalize_f0(
lf0, x_mask, uv, random_scale=False
)
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
z_p, m_p, logs_p, c_mask = self.enc_p(
x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale
)
z = self.flow(z_p, c_mask, g=g, reverse=True)
# MB-iSTFT-VITS
if self.mb:
o, o_mb = self.dec(z * c_mask, g=g)
else:
o = self.dec(z * c_mask, g=g, f0=f0)
return o
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/__init__.py
View File
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/config_templates/quickvc.json
+74
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{
"train": {
"log_interval": 100,
"eval_interval": 200,
"seed": 1234,
"epochs": 10000,
"learning_rate": 0.0001,
"betas": [0.8, 0.99],
"eps": 1e-9,
"batch_size": 16,
"fp16_run": false,
"bf16_run": false,
"lr_decay": 0.999875,
"segment_size": 10240,
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"c_kl": 1.0,
"use_sr": true,
"max_speclen": 512,
"port": "8001",
"keep_ckpts": 3,
"fft_sizes": [768, 1366, 342],
"hop_sizes": [60, 120, 20],
"win_lengths": [300, 600, 120],
"window": "hann_window",
"num_workers": 4,
"log_version": 0,
"ckpt_name_by_step": false,
"accumulate_grad_batches": 1
},
"data": {
"training_files": "filelists/44k/train.txt",
"validation_files": "filelists/44k/val.txt",
"max_wav_value": 32768.0,
"sampling_rate": 44100,
"filter_length": 2048,
"hop_length": 512,
"win_length": 2048,
"n_mel_channels": 80,
"mel_fmin": 0.0,
"mel_fmax": 22050,
"contentvec_final_proj": false
},
"model": {
"inter_channels": 192,
"hidden_channels": 192,
"filter_channels": 768,
"n_heads": 2,
"n_layers": 6,
"kernel_size": 3,
"p_dropout": 0.1,
"resblock": "1",
"resblock_kernel_sizes": [3, 7, 11],
"resblock_dilation_sizes": [
[1, 3, 5],
[1, 3, 5],
[1, 3, 5]
],
"upsample_rates": [8, 4],
"upsample_initial_channel": 512,
"upsample_kernel_sizes": [32, 16],
"n_layers_q": 3,
"use_spectral_norm": false,
"gin_channels": 256,
"ssl_dim": 768,
"n_speakers": 200,
"type_": "ms-istft",
"gen_istft_n_fft": 16,
"gen_istft_hop_size": 4,
"subbands": 4
},
"spk": {}
}
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/config_templates/so-vits-svc-4.0v1-legacy.json
+65
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{
"train": {
"log_interval": 200,
"eval_interval": 800,
"seed": 1234,
"epochs": 10000,
"learning_rate": 0.0001,
"betas": [0.8, 0.99],
"eps": 1e-9,
"batch_size": 16,
"fp16_run": false,
"bf16_run": false,
"lr_decay": 0.999875,
"segment_size": 10240,
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"c_kl": 1.0,
"use_sr": true,
"max_speclen": 512,
"port": "8001",
"keep_ckpts": 3,
"num_workers": 4,
"log_version": 0,
"ckpt_name_by_step": false,
"accumulate_grad_batches": 1
},
"data": {
"training_files": "filelists/44k/train.txt",
"validation_files": "filelists/44k/val.txt",
"max_wav_value": 32768.0,
"sampling_rate": 44100,
"filter_length": 2048,
"hop_length": 512,
"win_length": 2048,
"n_mel_channels": 80,
"mel_fmin": 0.0,
"mel_fmax": 22050
},
"model": {
"inter_channels": 192,
"hidden_channels": 192,
"filter_channels": 768,
"n_heads": 2,
"n_layers": 6,
"kernel_size": 3,
"p_dropout": 0.1,
"resblock": "1",
"resblock_kernel_sizes": [3, 7, 11],
"resblock_dilation_sizes": [
[1, 3, 5],
[1, 3, 5],
[1, 3, 5]
],
"upsample_rates": [8, 8, 2, 2, 2],
"upsample_initial_channel": 512,
"upsample_kernel_sizes": [16, 16, 4, 4, 4],
"n_layers_q": 3,
"use_spectral_norm": false,
"gin_channels": 256,
"ssl_dim": 256,
"n_speakers": 200
},
"spk": {}
}
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/config_templates/so-vits-svc-4.0v1.json
+67
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{
"train": {
"log_interval": 100,
"eval_interval": 200,
"seed": 1234,
"epochs": 10000,
"learning_rate": 0.0001,
"betas": [0.8, 0.99],
"eps": 1e-9,
"batch_size": 16,
"fp16_run": false,
"bf16_run": false,
"lr_decay": 0.999875,
"segment_size": 10240,
"init_lr_ratio": 1,
"warmup_epochs": 0,
"c_mel": 45,
"c_kl": 1.0,
"use_sr": true,
"max_speclen": 512,
"port": "8001",
"keep_ckpts": 3,
"num_workers": 4,
"log_version": 0,
"ckpt_name_by_step": false,
"accumulate_grad_batches": 1
},
"data": {
"training_files": "filelists/44k/train.txt",
"validation_files": "filelists/44k/val.txt",
"max_wav_value": 32768.0,
"sampling_rate": 44100,
"filter_length": 2048,
"hop_length": 512,
"win_length": 2048,
"n_mel_channels": 80,
"mel_fmin": 0.0,
"mel_fmax": 22050,
"contentvec_final_proj": false
},
"model": {
"inter_channels": 192,
"hidden_channels": 192,
"filter_channels": 768,
"n_heads": 2,
"n_layers": 6,
"kernel_size": 3,
"p_dropout": 0.1,
"resblock": "1",
"resblock_kernel_sizes": [3, 7, 11],
"resblock_dilation_sizes": [
[1, 3, 5],
[1, 3, 5],
[1, 3, 5]
],
"upsample_rates": [8, 8, 2, 2, 2],
"upsample_initial_channel": 512,
"upsample_kernel_sizes": [16, 16, 4, 4, 4],
"n_layers_q": 3,
"use_spectral_norm": false,
"gin_channels": 256,
"ssl_dim": 768,
"n_speakers": 200,
"type_": "hifi-gan"
},
"spk": {}
}
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_classify.py
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from __future__ import annotations
from logging import getLogger
from pathlib import Path
import keyboard
import librosa
import sounddevice as sd
import soundfile as sf
from rich.console import Console
from tqdm.rich import tqdm
LOG = getLogger(__name__)
def preprocess_classify(
input_dir: Path | str, output_dir: Path | str, create_new: bool = True
) -> None:
# paths
input_dir_ = Path(input_dir)
output_dir_ = Path(output_dir)
speed = 1
if not input_dir_.is_dir():
raise ValueError(f"{input_dir} is not a directory.")
output_dir_.mkdir(exist_ok=True)
console = Console()
# get audio paths and folders
audio_paths = list(input_dir_.glob("*.*"))
last_folders = [x for x in output_dir_.glob("*") if x.is_dir()]
console.print("Press ↑ or ↓ to change speed. Press any other key to classify.")
console.print(f"Folders: {[x.name for x in last_folders]}")
pbar_description = ""
pbar = tqdm(audio_paths)
for audio_path in pbar:
# read file
audio, sr = sf.read(audio_path)
# update description
duration = librosa.get_duration(y=audio, sr=sr)
pbar_description = f"{duration:.1f} {pbar_description}"
pbar.set_description(pbar_description)
while True:
# start playing
sd.play(librosa.effects.time_stretch(audio, rate=speed), sr, loop=True)
# wait for key press
key = str(keyboard.read_key())
if key == "down":
speed /= 1.1
console.print(f"Speed: {speed:.2f}")
elif key == "up":
speed *= 1.1
console.print(f"Speed: {speed:.2f}")
else:
break
# stop playing
sd.stop()
# print if folder changed
folders = [x for x in output_dir_.glob("*") if x.is_dir()]
if folders != last_folders:
console.print(f"Folders updated: {[x.name for x in folders]}")
last_folders = folders
# get folder
folder_candidates = [x for x in folders if x.name.startswith(key)]
if len(folder_candidates) == 0:
if create_new:
folder = output_dir_ / key
else:
console.print(f"No folder starts with {key}.")
continue
else:
if len(folder_candidates) > 1:
LOG.warning(
f"Multiple folders ({[x.name for x in folder_candidates]}) start with {key}. "
f"Using first one ({folder_candidates[0].name})."
)
folder = folder_candidates[0]
folder.mkdir(exist_ok=True)
# move file
new_path = folder / audio_path.name
audio_path.rename(new_path)
# update description
pbar_description = f"Last: {audio_path.name} -> {folder.name}"
# yield result
# yield audio_path, key, folder, new_path
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_flist_config.py
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from __future__ import annotations
import json
import os
from copy import deepcopy
from logging import getLogger
from pathlib import Path
from random import shuffle
from librosa import get_duration
from tqdm import tqdm
LOG = getLogger(__name__)
CONFIG_TEMPLATE_DIR = Path(__file__).parent / "config_templates"
def preprocess_config(
input_dir: Path | str,
train_list_path: Path | str,
val_list_path: Path | str,
test_list_path: Path | str,
config_path: Path | str,
config_name: str,
):
input_dir = Path(input_dir)
train_list_path = Path(train_list_path)
val_list_path = Path(val_list_path)
test_list_path = Path(test_list_path)
config_path = Path(config_path)
train = []
val = []
test = []
spk_dict = {}
spk_id = 0
for speaker in os.listdir(input_dir):
spk_dict[speaker] = spk_id
spk_id += 1
paths = []
for path in tqdm(list((input_dir / speaker).rglob("*.wav"))):
if get_duration(filename=path) < 0.3:
LOG.warning(f"skip {path} because it is too short.")
continue
paths.append(path)
shuffle(paths)
if len(paths) <= 4:
raise ValueError(
f"too few files in {input_dir / speaker} (expected at least 5)."
)
train += paths[2:-2]
val += paths[:2]
test += paths[-2:]
LOG.info(f"Writing {train_list_path}")
train_list_path.parent.mkdir(parents=True, exist_ok=True)
train_list_path.write_text(
"\n".join([x.as_posix() for x in train]), encoding="utf-8"
)
LOG.info(f"Writing {val_list_path}")
val_list_path.parent.mkdir(parents=True, exist_ok=True)
val_list_path.write_text("\n".join([x.as_posix() for x in val]), encoding="utf-8")
LOG.info(f"Writing {test_list_path}")
test_list_path.parent.mkdir(parents=True, exist_ok=True)
test_list_path.write_text("\n".join([x.as_posix() for x in test]), encoding="utf-8")
config = deepcopy(
json.loads(
(
CONFIG_TEMPLATE_DIR
/ (
config_name
if config_name.endswith(".json")
else config_name + ".json"
)
).read_text(encoding="utf-8")
)
)
config["spk"] = spk_dict
config["data"]["training_files"] = train_list_path.as_posix()
config["data"]["validation_files"] = val_list_path.as_posix()
LOG.info(f"Writing {config_path}")
config_path.parent.mkdir(parents=True, exist_ok=True)
with config_path.open("w", encoding="utf-8") as f:
json.dump(config, f, indent=2)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_hubert_f0.py
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from __future__ import annotations
from logging import getLogger
from pathlib import Path
from random import shuffle
from typing import Iterable, Literal
import librosa
import numpy as np
import torch
import torchaudio
from joblib import Parallel, cpu_count, delayed
from tqdm import tqdm
from transformers import HubertModel
import so_vits_svc_fork.f0
from so_vits_svc_fork import utils
from ..hparams import HParams
from ..modules.mel_processing import spec_to_mel_torch, spectrogram_torch
from ..utils import get_optimal_device, get_total_gpu_memory
from .preprocess_utils import check_hubert_min_duration
LOG = getLogger(__name__)
HUBERT_MEMORY = 2900
HUBERT_MEMORY_CREPE = 3900
def _process_one(
*,
filepath: Path,
content_model: HubertModel,
device: torch.device | str = get_optimal_device(),
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
force_rebuild: bool = False,
hps: HParams,
):
audio, sr = librosa.load(filepath, sr=hps.data.sampling_rate, mono=True)
if not check_hubert_min_duration(audio, sr):
LOG.info(f"Skip {filepath} because it is too short.")
return
data_path = filepath.parent / (filepath.name + ".data.pt")
if data_path.exists() and not force_rebuild:
return
# Compute f0
f0 = so_vits_svc_fork.f0.compute_f0(
audio, sampling_rate=sr, hop_length=hps.data.hop_length, method=f0_method
)
f0, uv = so_vits_svc_fork.f0.interpolate_f0(f0)
f0 = torch.from_numpy(f0).float()
uv = torch.from_numpy(uv).float()
# Compute HuBERT content
audio = torch.from_numpy(audio).float().to(device)
c = utils.get_content(
content_model,
audio,
device,
sr=sr,
legacy_final_proj=hps.data.get("contentvec_final_proj", True),
)
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
torch.cuda.empty_cache()
# Compute spectrogram
audio, sr = torchaudio.load(filepath)
spec = spectrogram_torch(audio, hps).squeeze(0)
mel_spec = spec_to_mel_torch(spec, hps)
torch.cuda.empty_cache()
# fix lengths
lmin = min(spec.shape[1], mel_spec.shape[1], f0.shape[0], uv.shape[0], c.shape[1])
spec, mel_spec, f0, uv, c = (
spec[:, :lmin],
mel_spec[:, :lmin],
f0[:lmin],
uv[:lmin],
c[:, :lmin],
)
# get speaker id
spk_name = filepath.parent.name
spk = hps.spk.__dict__[spk_name]
spk = torch.tensor(spk).long()
assert (
spec.shape[1] == mel_spec.shape[1] == f0.shape[0] == uv.shape[0] == c.shape[1]
), (spec.shape, mel_spec.shape, f0.shape, uv.shape, c.shape)
data = {
"spec": spec,
"mel_spec": mel_spec,
"f0": f0,
"uv": uv,
"content": c,
"audio": audio,
"spk": spk,
}
data = {k: v.cpu() for k, v in data.items()}
with data_path.open("wb") as f:
torch.save(data, f)
def _process_batch(filepaths: Iterable[Path], pbar_position: int, **kwargs):
hps = kwargs["hps"]
content_model = utils.get_hubert_model(
get_optimal_device(), hps.data.get("contentvec_final_proj", True)
)
for filepath in tqdm(filepaths, position=pbar_position):
_process_one(
content_model=content_model,
filepath=filepath,
**kwargs,
)
def preprocess_hubert_f0(
input_dir: Path | str,
config_path: Path | str,
n_jobs: int | None = None,
f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
force_rebuild: bool = False,
):
input_dir = Path(input_dir)
config_path = Path(config_path)
hps = utils.get_hparams(config_path)
if n_jobs is None:
# add cpu_count() to avoid SIGKILL
memory = get_total_gpu_memory("total")
n_jobs = min(
max(
memory
// (HUBERT_MEMORY_CREPE if f0_method == "crepe" else HUBERT_MEMORY)
if memory is not None
else 1,
1,
),
cpu_count(),
)
LOG.info(f"n_jobs automatically set to {n_jobs}, memory: {memory} MiB")
filepaths = list(input_dir.rglob("*.wav"))
n_jobs = min(len(filepaths) // 16 + 1, n_jobs)
shuffle(filepaths)
filepath_chunks = np.array_split(filepaths, n_jobs)
Parallel(n_jobs=n_jobs)(
delayed(_process_batch)(
filepaths=chunk,
pbar_position=pbar_position,
f0_method=f0_method,
force_rebuild=force_rebuild,
hps=hps,
)
for (pbar_position, chunk) in enumerate(filepath_chunks)
)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_resample.py
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from __future__ import annotations
import warnings
from logging import getLogger
from pathlib import Path
from typing import Iterable
import librosa
import soundfile
from joblib import Parallel, delayed
from tqdm_joblib import tqdm_joblib
from .preprocess_utils import check_hubert_min_duration
LOG = getLogger(__name__)
# input_dir and output_dir exists.
# write code to convert input dir audio files to output dir audio files,
# without changing folder structure. Use joblib to parallelize.
# Converting audio files includes:
# - resampling to specified sampling rate
# - trim silence
# - adjust volume in a smart way
# - save as 16-bit wav file
def _get_unique_filename(path: Path, existing_paths: Iterable[Path]) -> Path:
"""Return a unique path by appending a number to the original path."""
if path not in existing_paths:
return path
i = 1
while True:
new_path = path.parent / f"{path.stem}_{i}{path.suffix}"
if new_path not in existing_paths:
return new_path
i += 1
def is_relative_to(path: Path, *other):
"""Return True if the path is relative to another path or False.
Python 3.9+ has Path.is_relative_to() method, but we need to support Python 3.8.
"""
try:
path.relative_to(*other)
return True
except ValueError:
return False
def _preprocess_one(
input_path: Path,
output_path: Path,
sr: int,
*,
top_db: int,
frame_seconds: float,
hop_seconds: float,
) -> None:
"""Preprocess one audio file."""
try:
audio, sr = librosa.load(input_path, sr=sr, mono=True)
# Audioread is the last backend it will attempt, so this is the exception thrown on failure
except Exception as e:
# Failure due to attempting to load a file that is not audio, so return early
LOG.warning(f"Failed to load {input_path} due to {e}")
return
if not check_hubert_min_duration(audio, sr):
LOG.info(f"Skip {input_path} because it is too short.")
return
# Adjust volume
audio /= max(audio.max(), -audio.min())
# Trim silence
audio, _ = librosa.effects.trim(
audio,
top_db=top_db,
frame_length=int(frame_seconds * sr),
hop_length=int(hop_seconds * sr),
)
if not check_hubert_min_duration(audio, sr):
LOG.info(f"Skip {input_path} because it is too short.")
return
soundfile.write(output_path, audio, samplerate=sr, subtype="PCM_16")
def preprocess_resample(
input_dir: Path | str,
output_dir: Path | str,
sampling_rate: int,
n_jobs: int = -1,
*,
top_db: int = 30,
frame_seconds: float = 0.1,
hop_seconds: float = 0.05,
) -> None:
input_dir = Path(input_dir)
output_dir = Path(output_dir)
"""Preprocess audio files in input_dir and save them to output_dir."""
in_paths = []
out_paths = []
for in_path in input_dir.rglob("*.*"):
in_path_relative = in_path.relative_to(input_dir)
if not in_path.is_absolute() and is_relative_to(
in_path, Path("dataset_raw") / "44k"
):
new_in_path_relative = in_path_relative.relative_to("44k")
warnings.warn(
f"Recommended folder structure has changed since v1.0.0. "
"Please move your dataset directly under dataset_raw folder. "
f"Recoginzed {in_path_relative} as {new_in_path_relative}"
)
in_path_relative = new_in_path_relative
if len(in_path_relative.parts) < 2:
continue
speaker_name = in_path_relative.parts[0]
file_name = in_path_relative.with_suffix(".wav").name
out_path = output_dir / speaker_name / file_name
out_path = _get_unique_filename(out_path, out_paths)
out_path.parent.mkdir(parents=True, exist_ok=True)
in_paths.append(in_path)
out_paths.append(out_path)
in_and_out_paths = list(zip(in_paths, out_paths))
with tqdm_joblib(desc="Preprocessing", total=len(in_and_out_paths)):
Parallel(n_jobs=n_jobs)(
delayed(_preprocess_one)(
*args,
sr=sampling_rate,
top_db=top_db,
frame_seconds=frame_seconds,
hop_seconds=hop_seconds,
)
for args in in_and_out_paths
)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_speaker_diarization.py
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from __future__ import annotations
from collections import defaultdict
from logging import getLogger
from pathlib import Path
import librosa
import soundfile as sf
import torch
from joblib import Parallel, delayed
from pyannote.audio import Pipeline
from tqdm import tqdm
from tqdm_joblib import tqdm_joblib
LOG = getLogger(__name__)
def _process_one(
input_path: Path,
output_dir: Path,
sr: int,
*,
min_speakers: int = 1,
max_speakers: int = 1,
huggingface_token: str | None = None,
) -> None:
try:
audio, sr = librosa.load(input_path, sr=sr, mono=True)
except Exception as e:
LOG.warning(f"Failed to read {input_path}: {e}")
return
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization", use_auth_token=huggingface_token
)
if pipeline is None:
raise ValueError("Failed to load pipeline")
LOG.info(f"Processing {input_path}. This may take a while...")
diarization = pipeline(
input_path, min_speakers=min_speakers, max_speakers=max_speakers
)
LOG.info(f"Found {len(diarization)} tracks, writing to {output_dir}")
speaker_count = defaultdict(int)
output_dir.mkdir(parents=True, exist_ok=True)
for segment, track, speaker in tqdm(
list(diarization.itertracks(yield_label=True)), desc=f"Writing {input_path}"
):
if segment.end - segment.start < 1:
continue
speaker_count[speaker] += 1
audio_cut = audio[int(segment.start * sr) : int(segment.end * sr)]
sf.write(
(output_dir / f"{speaker}_{speaker_count[speaker]}.wav"),
audio_cut,
sr,
)
LOG.info(f"Speaker count: {speaker_count}")
def preprocess_speaker_diarization(
input_dir: Path | str,
output_dir: Path | str,
sr: int,
*,
min_speakers: int = 1,
max_speakers: int = 1,
huggingface_token: str | None = None,
n_jobs: int = -1,
) -> None:
if huggingface_token is not None and not huggingface_token.startswith("hf_"):
LOG.warning("Huggingface token probably should start with hf_")
if not torch.cuda.is_available():
LOG.warning("CUDA is not available. This will be extremely slow.")
input_dir = Path(input_dir)
output_dir = Path(output_dir)
input_dir.mkdir(parents=True, exist_ok=True)
output_dir.mkdir(parents=True, exist_ok=True)
input_paths = list(input_dir.rglob("*.*"))
with tqdm_joblib(desc="Preprocessing speaker diarization", total=len(input_paths)):
Parallel(n_jobs=n_jobs)(
delayed(_process_one)(
input_path,
output_dir / input_path.relative_to(input_dir).parent / input_path.stem,
sr,
max_speakers=max_speakers,
min_speakers=min_speakers,
huggingface_token=huggingface_token,
)
for input_path in input_paths
)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_split.py
+70
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from __future__ import annotations
from logging import getLogger
from pathlib import Path
import librosa
import soundfile as sf
from joblib import Parallel, delayed
from tqdm import tqdm
from tqdm_joblib import tqdm_joblib
LOG = getLogger(__name__)
def _process_one(
input_path: Path,
output_dir: Path,
sr: int,
*,
top_db: int = 30,
frame_seconds: float = 0.5,
hop_seconds: float = 0.1,
):
try:
audio, sr = librosa.load(input_path, sr=sr, mono=True)
except Exception as e:
LOG.warning(f"Failed to read {input_path}: {e}")
return
intervals = librosa.effects.split(
audio,
top_db=top_db,
frame_length=int(sr * frame_seconds),
hop_length=int(sr * hop_seconds),
)
output_dir.mkdir(parents=True, exist_ok=True)
for start, end in tqdm(intervals, desc=f"Writing {input_path}"):
audio_cut = audio[start:end]
sf.write(
(output_dir / f"{input_path.stem}_{start / sr:.3f}_{end / sr:.3f}.wav"),
audio_cut,
sr,
)
def preprocess_split(
input_dir: Path | str,
output_dir: Path | str,
sr: int,
*,
top_db: int = 30,
frame_seconds: float = 0.5,
hop_seconds: float = 0.1,
n_jobs: int = -1,
):
input_dir = Path(input_dir)
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
input_paths = list(input_dir.rglob("*.*"))
with tqdm_joblib(desc="Splitting", total=len(input_paths)):
Parallel(n_jobs=n_jobs)(
delayed(_process_one)(
input_path,
output_dir / input_path.relative_to(input_dir).parent,
sr,
top_db=top_db,
frame_seconds=frame_seconds,
hop_seconds=hop_seconds,
)
for input_path in input_paths
)
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/preprocessing/preprocess_utils.py
+5
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from numpy import ndarray
def check_hubert_min_duration(audio: ndarray, sr: int) -> bool:
return len(audio) / sr >= 0.3
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/py.typed
View File
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/train.py
+560
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from __future__ import annotations
import os
import warnings
from logging import getLogger
from multiprocessing import cpu_count
from pathlib import Path
from typing import Any
import lightning.pytorch as pl
import torch
from lightning.pytorch.accelerators import MPSAccelerator, TPUAccelerator
from lightning.pytorch.loggers import TensorBoardLogger
from lightning.pytorch.strategies.ddp import DDPStrategy
from lightning.pytorch.tuner import Tuner
from torch.cuda.amp import autocast
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard.writer import SummaryWriter
import so_vits_svc_fork.f0
import so_vits_svc_fork.modules.commons as commons
import so_vits_svc_fork.utils
from . import utils
from .dataset import TextAudioCollate, TextAudioDataset
from .logger import is_notebook
from .modules.descriminators import MultiPeriodDiscriminator
from .modules.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
from .modules.mel_processing import mel_spectrogram_torch
from .modules.synthesizers import SynthesizerTrn
LOG = getLogger(__name__)
torch.set_float32_matmul_precision("high")
class VCDataModule(pl.LightningDataModule):
batch_size: int
def __init__(self, hparams: Any):
super().__init__()
self.__hparams = hparams
self.batch_size = hparams.train.batch_size
if not isinstance(self.batch_size, int):
self.batch_size = 1
self.collate_fn = TextAudioCollate()
# these should be called in setup(), but we need to calculate check_val_every_n_epoch
self.train_dataset = TextAudioDataset(self.__hparams, is_validation=False)
self.val_dataset = TextAudioDataset(self.__hparams, is_validation=True)
def train_dataloader(self):
return DataLoader(
self.train_dataset,
num_workers=min(cpu_count(), self.__hparams.train.get("num_workers", 8)),
batch_size=self.batch_size,
collate_fn=self.collate_fn,
persistent_workers=True,
)
def val_dataloader(self):
return DataLoader(
self.val_dataset,
batch_size=1,
collate_fn=self.collate_fn,
)
def train(
config_path: Path | str, model_path: Path | str, reset_optimizer: bool = False
):
config_path = Path(config_path)
model_path = Path(model_path)
hparams = utils.get_backup_hparams(config_path, model_path)
utils.ensure_pretrained_model(model_path, hparams.model.get("type_", "hifi-gan"))
datamodule = VCDataModule(hparams)
strategy = (
(
"ddp_find_unused_parameters_true"
if os.name != "nt"
else DDPStrategy(find_unused_parameters=True, process_group_backend="gloo")
)
if torch.cuda.device_count() > 1
else "auto"
)
LOG.info(f"Using strategy: {strategy}")
trainer = pl.Trainer(
logger=TensorBoardLogger(
model_path, "lightning_logs", hparams.train.get("log_version", 0)
),
# profiler="simple",
val_check_interval=hparams.train.eval_interval,
max_epochs=hparams.train.epochs,
check_val_every_n_epoch=None,
precision="16-mixed"
if hparams.train.fp16_run
else "bf16-mixed"
if hparams.train.get("bf16_run", False)
else 32,
strategy=strategy,
callbacks=[pl.callbacks.RichProgressBar()] if not is_notebook() else None,
benchmark=True,
enable_checkpointing=False,
)
tuner = Tuner(trainer)
model = VitsLightning(reset_optimizer=reset_optimizer, **hparams)
# automatic batch size scaling
batch_size = hparams.train.batch_size
batch_split = str(batch_size).split("-")
batch_size = batch_split[0]
init_val = 2 if len(batch_split) <= 1 else int(batch_split[1])
max_trials = 25 if len(batch_split) <= 2 else int(batch_split[2])
if batch_size == "auto":
batch_size = "binsearch"
if batch_size in ["power", "binsearch"]:
model.tuning = True
tuner.scale_batch_size(
model,
mode=batch_size,
datamodule=datamodule,
steps_per_trial=1,
init_val=init_val,
max_trials=max_trials,
)
model.tuning = False
else:
batch_size = int(batch_size)
# automatic learning rate scaling is not supported for multiple optimizers
"""if hparams.train.learning_rate == "auto":
lr_finder = tuner.lr_find(model)
LOG.info(lr_finder.results)
fig = lr_finder.plot(suggest=True)
fig.savefig(model_path / "lr_finder.png")"""
trainer.fit(model, datamodule=datamodule)
class VitsLightning(pl.LightningModule):
def __init__(self, reset_optimizer: bool = False, **hparams: Any):
super().__init__()
self._temp_epoch = 0 # Add this line to initialize the _temp_epoch attribute
self.save_hyperparameters("reset_optimizer")
self.save_hyperparameters(*[k for k in hparams.keys()])
torch.manual_seed(self.hparams.train.seed)
self.net_g = SynthesizerTrn(
self.hparams.data.filter_length // 2 + 1,
self.hparams.train.segment_size // self.hparams.data.hop_length,
**self.hparams.model,
)
self.net_d = MultiPeriodDiscriminator(self.hparams.model.use_spectral_norm)
self.automatic_optimization = False
self.learning_rate = self.hparams.train.learning_rate
self.optim_g = torch.optim.AdamW(
self.net_g.parameters(),
self.learning_rate,
betas=self.hparams.train.betas,
eps=self.hparams.train.eps,
)
self.optim_d = torch.optim.AdamW(
self.net_d.parameters(),
self.learning_rate,
betas=self.hparams.train.betas,
eps=self.hparams.train.eps,
)
self.scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
self.optim_g, gamma=self.hparams.train.lr_decay
)
self.scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
self.optim_d, gamma=self.hparams.train.lr_decay
)
self.optimizers_count = 2
self.load(reset_optimizer)
self.tuning = False
def on_train_start(self) -> None:
if not self.tuning:
self.set_current_epoch(self._temp_epoch)
total_batch_idx = self._temp_epoch * len(self.trainer.train_dataloader)
self.set_total_batch_idx(total_batch_idx)
global_step = total_batch_idx * self.optimizers_count
self.set_global_step(global_step)
# check if using tpu or mps
if isinstance(self.trainer.accelerator, (TPUAccelerator, MPSAccelerator)):
# patch torch.stft to use cpu
LOG.warning("Using TPU/MPS. Patching torch.stft to use cpu.")
def stft(
input: torch.Tensor,
n_fft: int,
hop_length: int | None = None,
win_length: int | None = None,
window: torch.Tensor | None = None,
center: bool = True,
pad_mode: str = "reflect",
normalized: bool = False,
onesided: bool | None = None,
return_complex: bool | None = None,
) -> torch.Tensor:
device = input.device
input = input.cpu()
if window is not None:
window = window.cpu()
return torch.functional.stft(
input,
n_fft,
hop_length,
win_length,
window,
center,
pad_mode,
normalized,
onesided,
return_complex,
).to(device)
torch.stft = stft
elif "bf" in self.trainer.precision:
LOG.warning("Using bf. Patching torch.stft to use fp32.")
def stft(
input: torch.Tensor,
n_fft: int,
hop_length: int | None = None,
win_length: int | None = None,
window: torch.Tensor | None = None,
center: bool = True,
pad_mode: str = "reflect",
normalized: bool = False,
onesided: bool | None = None,
return_complex: bool | None = None,
) -> torch.Tensor:
dtype = input.dtype
input = input.float()
if window is not None:
window = window.float()
return torch.functional.stft(
input,
n_fft,
hop_length,
win_length,
window,
center,
pad_mode,
normalized,
onesided,
return_complex,
).to(dtype)
torch.stft = stft
def on_train_end(self) -> None:
self.save_checkpoints(adjust=0)
def save_checkpoints(self, adjust=1):
if self.tuning or self.trainer.sanity_checking:
return
# only save checkpoints if we are on the main device
if (
hasattr(self.device, "index")
and self.device.index != None
and self.device.index != 0
):
return
# `on_train_end` will be the actual epoch, not a -1, so we have to call it with `adjust = 0`
current_epoch = self.current_epoch + adjust
total_batch_idx = self.total_batch_idx - 1 + adjust
utils.save_checkpoint(
self.net_g,
self.optim_g,
self.learning_rate,
current_epoch,
Path(self.hparams.model_dir)
/ f"G_{total_batch_idx if self.hparams.train.get('ckpt_name_by_step', False) else current_epoch}.pth",
)
utils.save_checkpoint(
self.net_d,
self.optim_d,
self.learning_rate,
current_epoch,
Path(self.hparams.model_dir)
/ f"D_{total_batch_idx if self.hparams.train.get('ckpt_name_by_step', False) else current_epoch}.pth",
)
keep_ckpts = self.hparams.train.get("keep_ckpts", 0)
if keep_ckpts > 0:
utils.clean_checkpoints(
path_to_models=self.hparams.model_dir,
n_ckpts_to_keep=keep_ckpts,
sort_by_time=True,
)
def set_current_epoch(self, epoch: int):
LOG.info(f"Setting current epoch to {epoch}")
self.trainer.fit_loop.epoch_progress.current.completed = epoch
self.trainer.fit_loop.epoch_progress.current.processed = epoch
assert self.current_epoch == epoch, f"{self.current_epoch} != {epoch}"
def set_global_step(self, global_step: int):
LOG.info(f"Setting global step to {global_step}")
self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed = (
global_step
)
self.trainer.fit_loop.epoch_loop.automatic_optimization.optim_progress.optimizer.step.total.completed = (
global_step
)
assert self.global_step == global_step, f"{self.global_step} != {global_step}"
def set_total_batch_idx(self, total_batch_idx: int):
LOG.info(f"Setting total batch idx to {total_batch_idx}")
self.trainer.fit_loop.epoch_loop.batch_progress.total.ready = (
total_batch_idx + 1
)
self.trainer.fit_loop.epoch_loop.batch_progress.total.completed = (
total_batch_idx
)
assert (
self.total_batch_idx == total_batch_idx + 1
), f"{self.total_batch_idx} != {total_batch_idx + 1}"
@property
def total_batch_idx(self) -> int:
return self.trainer.fit_loop.epoch_loop.total_batch_idx + 1
def load(self, reset_optimizer: bool = False):
latest_g_path = utils.latest_checkpoint_path(self.hparams.model_dir, "G_*.pth")
latest_d_path = utils.latest_checkpoint_path(self.hparams.model_dir, "D_*.pth")
if latest_g_path is not None and latest_d_path is not None:
try:
_, _, _, epoch = utils.load_checkpoint(
latest_g_path,
self.net_g,
self.optim_g,
reset_optimizer,
)
_, _, _, epoch = utils.load_checkpoint(
latest_d_path,
self.net_d,
self.optim_d,
reset_optimizer,
)
self._temp_epoch = epoch
self.scheduler_g.last_epoch = epoch - 1
self.scheduler_d.last_epoch = epoch - 1
except Exception as e:
raise RuntimeError("Failed to load checkpoint") from e
else:
LOG.warning("No checkpoint found. Start from scratch.")
def configure_optimizers(self):
return [self.optim_g, self.optim_d], [self.scheduler_g, self.scheduler_d]
def log_image_dict(
self, image_dict: dict[str, Any], dataformats: str = "HWC"
) -> None:
if not isinstance(self.logger, TensorBoardLogger):
warnings.warn("Image logging is only supported with TensorBoardLogger.")
return
writer: SummaryWriter = self.logger.experiment
for k, v in image_dict.items():
try:
writer.add_image(k, v, self.total_batch_idx, dataformats=dataformats)
except Exception as e:
warnings.warn(f"Failed to log image {k}: {e}")
def log_audio_dict(self, audio_dict: dict[str, Any]) -> None:
if not isinstance(self.logger, TensorBoardLogger):
warnings.warn("Audio logging is only supported with TensorBoardLogger.")
return
writer: SummaryWriter = self.logger.experiment
for k, v in audio_dict.items():
writer.add_audio(
k,
v.float(),
self.total_batch_idx,
sample_rate=self.hparams.data.sampling_rate,
)
def log_dict_(self, log_dict: dict[str, Any], **kwargs) -> None:
if not isinstance(self.logger, TensorBoardLogger):
warnings.warn("Logging is only supported with TensorBoardLogger.")
return
writer: SummaryWriter = self.logger.experiment
for k, v in log_dict.items():
writer.add_scalar(k, v, self.total_batch_idx)
kwargs["logger"] = False
self.log_dict(log_dict, **kwargs)
def log_(self, key: str, value: Any, **kwargs) -> None:
self.log_dict_({key: value}, **kwargs)
def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
self.net_g.train()
self.net_d.train()
# get optims
optim_g, optim_d = self.optimizers()
# Generator
# train
self.toggle_optimizer(optim_g)
c, f0, spec, mel, y, g, lengths, uv = batch
(
y_hat,
y_hat_mb,
ids_slice,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
pred_lf0,
norm_lf0,
lf0,
) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
y_mel = commons.slice_segments(
mel,
ids_slice,
self.hparams.train.segment_size // self.hparams.data.hop_length,
)
y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), self.hparams)
y_mel = y_mel[..., : y_hat_mel.shape[-1]]
y = commons.slice_segments(
y,
ids_slice * self.hparams.data.hop_length,
self.hparams.train.segment_size,
)
y = y[..., : y_hat.shape[-1]]
# generator loss
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = self.net_d(y, y_hat)
with autocast(enabled=False):
loss_mel = F.l1_loss(y_mel, y_hat_mel) * self.hparams.train.c_mel
loss_kl = (
kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * self.hparams.train.c_kl
)
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_lf0 = F.mse_loss(pred_lf0, lf0)
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
# MB-iSTFT-VITS
loss_subband = torch.tensor(0.0)
if self.hparams.model.get("type_") == "mb-istft":
from .modules.decoders.mb_istft import PQMF, subband_stft_loss
y_mb = PQMF(y.device, self.hparams.model.subbands).analysis(y)
loss_subband = subband_stft_loss(self.hparams, y_mb, y_hat_mb)
loss_gen_all += loss_subband
# log loss
self.log_("lr", self.optim_g.param_groups[0]["lr"])
self.log_dict_(
{
"loss/g/total": loss_gen_all,
"loss/g/fm": loss_fm,
"loss/g/mel": loss_mel,
"loss/g/kl": loss_kl,
"loss/g/lf0": loss_lf0,
},
prog_bar=True,
)
if self.hparams.model.get("type_") == "mb-istft":
self.log_("loss/g/subband", loss_subband)
if self.total_batch_idx % self.hparams.train.log_interval == 0:
self.log_image_dict(
{
"slice/mel_org": utils.plot_spectrogram_to_numpy(
y_mel[0].data.cpu().float().numpy()
),
"slice/mel_gen": utils.plot_spectrogram_to_numpy(
y_hat_mel[0].data.cpu().float().numpy()
),
"all/mel": utils.plot_spectrogram_to_numpy(
mel[0].data.cpu().float().numpy()
),
"all/lf0": so_vits_svc_fork.utils.plot_data_to_numpy(
lf0[0, 0, :].cpu().float().numpy(),
pred_lf0[0, 0, :].detach().cpu().float().numpy(),
),
"all/norm_lf0": so_vits_svc_fork.utils.plot_data_to_numpy(
lf0[0, 0, :].cpu().float().numpy(),
norm_lf0[0, 0, :].detach().cpu().float().numpy(),
),
}
)
accumulate_grad_batches = self.hparams.train.get("accumulate_grad_batches", 1)
should_update = (
batch_idx + 1
) % accumulate_grad_batches == 0 or self.trainer.is_last_batch
# optimizer
self.manual_backward(loss_gen_all / accumulate_grad_batches)
if should_update:
self.log_(
"grad_norm_g", commons.clip_grad_value_(self.net_g.parameters(), None)
)
optim_g.step()
optim_g.zero_grad()
self.untoggle_optimizer(optim_g)
# Discriminator
# train
self.toggle_optimizer(optim_d)
y_d_hat_r, y_d_hat_g, _, _ = self.net_d(y, y_hat.detach())
# discriminator loss
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
loss_disc_all = loss_disc
# log loss
self.log_("loss/d/total", loss_disc_all, prog_bar=True)
# optimizer
self.manual_backward(loss_disc_all / accumulate_grad_batches)
if should_update:
self.log_(
"grad_norm_d", commons.clip_grad_value_(self.net_d.parameters(), None)
)
optim_d.step()
optim_d.zero_grad()
self.untoggle_optimizer(optim_d)
# end of epoch
if self.trainer.is_last_batch:
self.scheduler_g.step()
self.scheduler_d.step()
def validation_step(self, batch, batch_idx):
# avoid logging with wrong global step
if self.global_step == 0:
return
with torch.no_grad():
self.net_g.eval()
c, f0, _, mel, y, g, _, uv = batch
y_hat = self.net_g.infer(c, f0, uv, g=g)
y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1).float(), self.hparams)
self.log_audio_dict(
{f"gen/audio_{batch_idx}": y_hat[0], f"gt/audio_{batch_idx}": y[0]}
)
self.log_image_dict(
{
"gen/mel": utils.plot_spectrogram_to_numpy(
y_hat_mel[0].cpu().float().numpy()
),
"gt/mel": utils.plot_spectrogram_to_numpy(
mel[0].cpu().float().numpy()
),
}
)
def on_validation_end(self) -> None:
self.save_checkpoints()
voice_conversion/so-vits-svc-paul-v2/src/so_vits_svc_fork/utils.py
+430
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@@ -0,0 +1,430 @@
from __future__ import annotations
import json
import re
import subprocess
import warnings
from itertools import groupby
from logging import getLogger
from pathlib import Path
from typing import Any, Literal, Sequence
import matplotlib
import matplotlib.pylab as plt
import numpy as np
import requests
import torch
import torch.backends.mps
import torch.nn as nn
import torchaudio
from cm_time import timer
from numpy import ndarray
from tqdm import tqdm
from transformers import HubertModel
from so_vits_svc_fork.hparams import HParams
LOG = getLogger(__name__)
HUBERT_SAMPLING_RATE = 16000
def get_optimal_device(index: int = 0) -> torch.device:
if torch.cuda.is_available():
return torch.device(f"cuda:{index % torch.cuda.device_count()}")
elif torch.backends.mps.is_available():
return torch.device("mps")
else:
try:
import torch_xla.core.xla_model as xm # noqa
if xm.xrt_world_size() > 0:
return torch.device("xla")
# return xm.xla_device()
except ImportError:
pass
return torch.device("cpu")
def download_file(
url: str,
filepath: Path | str,
chunk_size: int = 64 * 1024,
tqdm_cls: type = tqdm,
skip_if_exists: bool = False,
overwrite: bool = False,
**tqdm_kwargs: Any,
):
if skip_if_exists is True and overwrite is True:
raise ValueError("skip_if_exists and overwrite cannot be both True")
filepath = Path(filepath)
filepath.parent.mkdir(parents=True, exist_ok=True)
temppath = filepath.parent / f"{filepath.name}.download"
if filepath.exists():
if skip_if_exists:
return
elif not overwrite:
filepath.unlink()
else:
raise FileExistsError(f"{filepath} already exists")
temppath.unlink(missing_ok=True)
resp = requests.get(url, stream=True)
total = int(resp.headers.get("content-length", 0))
kwargs = dict(
total=total,
unit="iB",
unit_scale=True,
unit_divisor=1024,
desc=f"Downloading {filepath.name}",
)
kwargs.update(tqdm_kwargs)
with temppath.open("wb") as f, tqdm_cls(**kwargs) as pbar:
for data in resp.iter_content(chunk_size=chunk_size):
size = f.write(data)
pbar.update(size)
temppath.rename(filepath)
PRETRAINED_MODEL_URLS = {
"hifi-gan": [
[
"https://huggingface.co/therealvul/so-vits-svc-4.0-init/resolve/main/D_0.pth",
"https://huggingface.co/therealvul/so-vits-svc-4.0-init/resolve/main/G_0.pth",
],
[
"https://huggingface.co/Himawari00/so-vits-svc4.0-pretrain-models/resolve/main/D_0.pth",
"https://huggingface.co/Himawari00/so-vits-svc4.0-pretrain-models/resolve/main/G_0.pth",
],
],
"contentvec": [
[
"https://huggingface.co/therealvul/so-vits-svc-4.0-init/resolve/main/checkpoint_best_legacy_500.pt"
],
[
"https://huggingface.co/Himawari00/so-vits-svc4.0-pretrain-models/resolve/main/checkpoint_best_legacy_500.pt"
],
[
"http://obs.cstcloud.cn/share/obs/sankagenkeshi/checkpoint_best_legacy_500.pt"
],
],
}
from joblib import Parallel, delayed
def ensure_pretrained_model(
folder_path: Path | str, type_: str, **tqdm_kwargs: Any
) -> tuple[Path, ...] | None:
folder_path = Path(folder_path)
models_candidates = PRETRAINED_MODEL_URLS.get(type_, None)
if models_candidates is None:
LOG.warning(f"Unknown pretrained model type: {type_}")
return
for model_urls in models_candidates:
paths = [folder_path / model_url.split("/")[-1] for model_url in model_urls]
try:
Parallel(n_jobs=len(paths))(
[
delayed(download_file)(
url, path, position=i, skip_if_exists=True, **tqdm_kwargs
)
for i, (url, path) in enumerate(zip(model_urls, paths))
]
)
return tuple(paths)
except Exception as e:
LOG.exception(e)
return
class HubertModelWithFinalProj(HubertModel):
def __init__(self, config):
super().__init__(config)
# The final projection layer is only used for backward compatibility.
# Following https://github.com/auspicious3000/contentvec/issues/6
# Remove this layer is necessary to achieve the desired outcome.
self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size)
def get_hubert_model(
device: str | torch.device, final_proj: bool = True
) -> HubertModel:
if final_proj:
return HubertModelWithFinalProj.from_pretrained(
"lengyue233/content-vec-best"
).to(device)
else:
return HubertModel.from_pretrained("lengyue233/content-vec-best").to(device)
def get_content(
cmodel: HubertModel,
audio: torch.Tensor | ndarray[Any, Any],
device: torch.device | str,
sr: int,
legacy_final_proj: bool = False,
) -> torch.Tensor:
audio = torch.as_tensor(audio)
if sr != HUBERT_SAMPLING_RATE:
audio = (
torchaudio.transforms.Resample(sr, HUBERT_SAMPLING_RATE)
.to(audio.device)(audio)
.to(device)
)
if audio.ndim == 1:
audio = audio.unsqueeze(0)
with torch.no_grad(), timer() as t:
if legacy_final_proj:
warnings.warn("legacy_final_proj is deprecated")
if not hasattr(cmodel, "final_proj"):
raise ValueError("HubertModel does not have final_proj")
c = cmodel(audio, output_hidden_states=True)["hidden_states"][9]
c = cmodel.final_proj(c)
else:
c = cmodel(audio)["last_hidden_state"]
c = c.transpose(1, 2)
wav_len = audio.shape[-1] / HUBERT_SAMPLING_RATE
LOG.info(
f"HuBERT inference time : {t.elapsed:.3f}s, RTF: {t.elapsed / wav_len:.3f}"
)
return c
def _substitute_if_same_shape(to_: dict[str, Any], from_: dict[str, Any]) -> None:
not_in_to = list(filter(lambda x: x not in to_, from_.keys()))
not_in_from = list(filter(lambda x: x not in from_, to_.keys()))
if not_in_to:
warnings.warn(f"Keys not found in model state dict:" f"{not_in_to}")
if not_in_from:
warnings.warn(f"Keys not found in checkpoint state dict:" f"{not_in_from}")
shape_missmatch = []
for k, v in from_.items():
if k not in to_:
pass
elif hasattr(v, "shape"):
if not hasattr(to_[k], "shape"):
raise ValueError(f"Key {k} is not a tensor")
if to_[k].shape == v.shape:
to_[k] = v
else:
shape_missmatch.append((k, to_[k].shape, v.shape))
elif isinstance(v, dict):
assert isinstance(to_[k], dict)
_substitute_if_same_shape(to_[k], v)
else:
to_[k] = v
if shape_missmatch:
warnings.warn(
f"Shape mismatch: {[f'{k}: {v1} -> {v2}' for k, v1, v2 in shape_missmatch]}"
)
def safe_load(model: torch.nn.Module, state_dict: dict[str, Any]) -> None:
model_state_dict = model.state_dict()
_substitute_if_same_shape(model_state_dict, state_dict)
model.load_state_dict(model_state_dict)
def load_checkpoint(
checkpoint_path: Path | str,
model: torch.nn.Module,
optimizer: torch.optim.Optimizer | None = None,
skip_optimizer: bool = False,
) -> tuple[torch.nn.Module, torch.optim.Optimizer | None, float, int]:
if not Path(checkpoint_path).is_file():
raise FileNotFoundError(f"File {checkpoint_path} not found")
with Path(checkpoint_path).open("rb") as f:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", category=UserWarning, message="TypedStorage is deprecated"
)
# NB(paul): iirc changing this to false prevented an error at some point
# but it also worsens security
checkpoint_dict = torch.load(f, map_location="cpu", weights_only=False)
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
# safe load module
if hasattr(model, "module"):
safe_load(model.module, checkpoint_dict["model"])
else:
safe_load(model, checkpoint_dict["model"])
# safe load optim
if (
optimizer is not None
and not skip_optimizer
and checkpoint_dict["optimizer"] is not None
):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
safe_load(optimizer, checkpoint_dict["optimizer"])
LOG.info(f"Loaded checkpoint '{checkpoint_path}' (epoch {iteration})")
return model, optimizer, learning_rate, iteration
def save_checkpoint(
model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
learning_rate: float,
iteration: int,
checkpoint_path: Path | str,
) -> None:
LOG.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
with Path(checkpoint_path).open("wb") as f:
torch.save(
{
"model": state_dict,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
f,
)
def clean_checkpoints(
path_to_models: Path | str, n_ckpts_to_keep: int = 2, sort_by_time: bool = True
) -> None:
"""Freeing up space by deleting saved ckpts
Arguments:
path_to_models -- Path to the model directory
n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
sort_by_time -- True -> chronologically delete ckpts
False -> lexicographically delete ckpts
"""
LOG.info("Cleaning old checkpoints...")
path_to_models = Path(path_to_models)
# Define sort key functions
name_key = lambda p: int(re.match(r"[GD]_(\d+)", p.stem).group(1))
time_key = lambda p: p.stat().st_mtime
path_key = lambda p: (p.stem[0], time_key(p) if sort_by_time else name_key(p))
models = list(
filter(
lambda p: (
p.is_file()
and re.match(r"[GD]_\d+", p.stem)
and not p.stem.endswith("_0")
),
path_to_models.glob("*.pth"),
)
)
models_sorted = sorted(models, key=path_key)
models_sorted_grouped = groupby(models_sorted, lambda p: p.stem[0])
for group_name, group_items in models_sorted_grouped:
to_delete_list = list(group_items)[:-n_ckpts_to_keep]
for to_delete in to_delete_list:
if to_delete.exists():
LOG.info(f"Removing {to_delete}")
to_delete.unlink()
def latest_checkpoint_path(dir_path: Path | str, regex: str = "G_*.pth") -> Path | None:
dir_path = Path(dir_path)
name_key = lambda p: int(re.match(r"._(\d+)\.pth", p.name).group(1))
paths = list(sorted(dir_path.glob(regex), key=name_key))
if len(paths) == 0:
return None
return paths[-1]
def plot_spectrogram_to_numpy(spectrogram: ndarray) -> ndarray:
matplotlib.use("Agg")
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def get_backup_hparams(
config_path: Path, model_path: Path, init: bool = True
) -> HParams:
model_path.mkdir(parents=True, exist_ok=True)
config_save_path = model_path / "config.json"
if init:
with config_path.open() as f:
data = f.read()
with config_save_path.open("w") as f:
f.write(data)
else:
with config_save_path.open() as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_path.as_posix()
return hparams
def get_hparams(config_path: Path | str) -> HParams:
config = json.loads(Path(config_path).read_text("utf-8"))
hparams = HParams(**config)
return hparams
def repeat_expand_2d(content: torch.Tensor, target_len: int) -> torch.Tensor:
# content : [h, t]
src_len = content.shape[-1]
if target_len < src_len:
return content[:, :target_len]
else:
return torch.nn.functional.interpolate(
content.unsqueeze(0), size=target_len, mode="nearest"
).squeeze(0)
def plot_data_to_numpy(x: ndarray, y: ndarray) -> ndarray:
matplotlib.use("Agg")
fig, ax = plt.subplots(figsize=(10, 2))
plt.plot(x)
plt.plot(y)
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def get_gpu_memory(type_: Literal["total", "free", "used"]) -> Sequence[int] | None:
command = f"nvidia-smi --query-gpu=memory.{type_} --format=csv"
try:
memory_free_info = (
subprocess.check_output(command.split())
.decode("ascii")
.split("\n")[:-1][1:]
)
memory_free_values = [int(x.split()[0]) for i, x in enumerate(memory_free_info)]
return memory_free_values
except Exception:
return
def get_total_gpu_memory(type_: Literal["total", "free", "used"]) -> int | None:
memories = get_gpu_memory(type_)
if memories is None:
return
return sum(memories)