mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-02 16:23:58 +03:00
update
This commit is contained in:
w-okada
parent
39e0d0cfd6
commit
bc6e8a9c08
2
client/demo/dist/index.js
vendored
2
client/demo/dist/index.js
vendored
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2178
client/demo/package-lock.json
generated
2178
client/demo/package-lock.json
generated
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Load Diff
@ -28,41 +28,41 @@
|
||||
"author": "wataru.okada@flect.co.jp",
|
||||
"license": "ISC",
|
||||
"devDependencies": {
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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},
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"dependencies": {
|
||||
"@alexanderolsen/libsamplerate-js": "^2.1.0",
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"@dannadori/voice-changer-client-js": "^1.0.180",
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"@alexanderolsen/libsamplerate-js": "^2.1.1",
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"@dannadori/voice-changer-client-js": "^1.0.182",
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"@dannadori/voice-changer-js": "^1.0.2",
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"@dannadori/worker-manager": "^1.0.20",
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"@fortawesome/fontawesome-svg-core": "^6.5.1",
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@ -70,9 +70,9 @@
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"@fortawesome/free-regular-svg-icons": "^6.5.1",
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"@fortawesome/free-solid-svg-icons": "^6.5.1",
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"@fortawesome/react-fontawesome": "^0.2.0",
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"@tensorflow/tfjs": "^4.14.0",
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||||
"onnxruntime-web": "^1.16.3",
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"protobufjs": "^7.2.5",
|
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||||
"onnxruntime-web": "^1.17.1",
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"protobufjs": "^7.2.6",
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"react": "^18.2.0",
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"react-dom": "^18.2.0"
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}
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@ -125,6 +125,11 @@ export const FileUploaderScreen = (props: FileUploaderScreenProps) => {
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return x.kind == "llvcConfig";
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});
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return enough;
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} else if (setting.voiceChangerType == "EasyVC") {
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const enough = !!setting.files.find((x) => {
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return x.kind == "easyVCModel";
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});
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return enough;
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}
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return false;
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};
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@ -189,6 +194,8 @@ export const FileUploaderScreen = (props: FileUploaderScreenProps) => {
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} else if (vcType == "LLVC") {
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rows.push(generateFileRow(uploadSetting!, "Model", "llvcModel", ["pth"]));
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rows.push(generateFileRow(uploadSetting!, "Config", "llvcConfig", ["json"]));
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} else if (vcType == "EasyVC") {
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rows.push(generateFileRow(uploadSetting!, "Model", "easyVCModel", ["onnx"]));
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}
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return rows;
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};
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433
client/lib/package-lock.json
generated
433
client/lib/package-lock.json
generated
@ -1,44 +1,44 @@
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{
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"name": "@dannadori/voice-changer-client-js",
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"version": "1.0.180",
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||||
"version": "1.0.182",
|
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"lockfileVersion": 3,
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"requires": true,
|
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"packages": {
|
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"": {
|
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"name": "@dannadori/voice-changer-client-js",
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"version": "1.0.180",
|
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"version": "1.0.182",
|
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"license": "ISC",
|
||||
"dependencies": {
|
||||
"@types/readable-stream": "^4.0.9",
|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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@ -1585,22 +1585,22 @@
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|
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@ -1621,9 +1621,9 @@
|
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@ -3051,9 +3051,9 @@
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@ -3234,9 +3234,9 @@
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@ -3256,9 +3256,9 @@
|
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"dev": true
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@ -3291,9 +3291,9 @@
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@ -3302,27 +3302,27 @@
|
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@ -3721,9 +3721,9 @@
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@ -3736,8 +3736,8 @@
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|
||||
"node": "^18 || ^19 || ^20",
|
||||
"npm": "^8 || ^9 || ^10"
|
||||
}
|
||||
},
|
||||
"node_modules/ansi-escapes": {
|
||||
@ -3835,12 +3835,6 @@
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/array-flatten": {
|
||||
"version": "2.1.2",
|
||||
"resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-2.1.2.tgz",
|
||||
"integrity": "sha512-hNfzcOV8W4NdualtqBFPyVO+54DSJuZGY9qT4pRroB6S9e3iiido2ISIC5h9R2sPJ8H3FHCIiEnsv1lPXO3KtQ==",
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/array-includes": {
|
||||
"version": "3.1.7",
|
||||
"resolved": "https://registry.npmjs.org/array-includes/-/array-includes-3.1.7.tgz",
|
||||
@ -4165,13 +4159,11 @@
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/bonjour-service": {
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/bonjour-service/-/bonjour-service-1.1.1.tgz",
|
||||
"integrity": "sha512-Z/5lQRMOG9k7W+FkeGTNjh7htqn/2LMnfOvBZ8pynNZCM9MwkQkI3zeI4oz09uWdcgmgHugVvBqxGg4VQJ5PCg==",
|
||||
"version": "1.2.1",
|
||||
"resolved": "https://registry.npmjs.org/bonjour-service/-/bonjour-service-1.2.1.tgz",
|
||||
"integrity": "sha512-oSzCS2zV14bh2kji6vNe7vrpJYCHGvcZnlffFQ1MEoX/WOeQ/teD8SYWKR942OI3INjq8OMNJlbPK5LLLUxFDw==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"array-flatten": "^2.1.2",
|
||||
"dns-equal": "^1.0.0",
|
||||
"fast-deep-equal": "^3.1.3",
|
||||
"multicast-dns": "^7.2.5"
|
||||
}
|
||||
@ -4387,16 +4379,10 @@
|
||||
}
|
||||
},
|
||||
"node_modules/chokidar": {
|
||||
"version": "3.5.3",
|
||||
"resolved": "https://registry.npmjs.org/chokidar/-/chokidar-3.5.3.tgz",
|
||||
"integrity": "sha512-Dr3sfKRP6oTcjf2JmUmFJfeVMvXBdegxB0iVQ5eb2V10uFJUCAS8OByZdVAyVb8xXNz3GjjTgj9kLWsZTqE6kw==",
|
||||
"version": "3.6.0",
|
||||
"resolved": "https://registry.npmjs.org/chokidar/-/chokidar-3.6.0.tgz",
|
||||
"integrity": "sha512-7VT13fmjotKpGipCW9JEQAusEPE+Ei8nl6/g4FBAmIm0GOOLMua9NDDo/DWp0ZAxCr3cPq5ZpBqmPAQgDda2Pw==",
|
||||
"dev": true,
|
||||
"funding": [
|
||||
{
|
||||
"type": "individual",
|
||||
"url": "https://paulmillr.com/funding/"
|
||||
}
|
||||
],
|
||||
"dependencies": {
|
||||
"anymatch": "~3.1.2",
|
||||
"braces": "~3.0.2",
|
||||
@ -4409,6 +4395,9 @@
|
||||
"engines": {
|
||||
"node": ">= 8.10.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://paulmillr.com/funding/"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"fsevents": "~2.3.2"
|
||||
}
|
||||
@ -4990,12 +4979,6 @@
|
||||
"node": "^14.15.0 || ^16.10.0 || >=18.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/dns-equal": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/dns-equal/-/dns-equal-1.0.0.tgz",
|
||||
"integrity": "sha512-z+paD6YUQsk+AbGCEM4PrOXSss5gd66QfcVBFTKR/HpFL9jCqikS94HYwKww6fQyO7IxrIIyUu+g0Ka9tUS2Cg==",
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/dns-packet": {
|
||||
"version": "5.6.1",
|
||||
"resolved": "https://registry.npmjs.org/dns-packet/-/dns-packet-5.6.1.tgz",
|
||||
@ -5277,16 +5260,16 @@
|
||||
}
|
||||
},
|
||||
"node_modules/eslint": {
|
||||
"version": "8.55.0",
|
||||
"resolved": "https://registry.npmjs.org/eslint/-/eslint-8.55.0.tgz",
|
||||
"integrity": "sha512-iyUUAM0PCKj5QpwGfmCAG9XXbZCWsqP/eWAWrG/W0umvjuLRBECwSFdt+rCntju0xEH7teIABPwXpahftIaTdA==",
|
||||
"version": "8.57.0",
|
||||
"resolved": "https://registry.npmjs.org/eslint/-/eslint-8.57.0.tgz",
|
||||
"integrity": "sha512-dZ6+mexnaTIbSBZWgou51U6OmzIhYM2VcNdtiTtI7qPNZm35Akpr0f6vtw3w1Kmn5PYo+tZVfh13WrhpS6oLqQ==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"@eslint-community/eslint-utils": "^4.2.0",
|
||||
"@eslint-community/regexpp": "^4.6.1",
|
||||
"@eslint/eslintrc": "^2.1.4",
|
||||
"@eslint/js": "8.55.0",
|
||||
"@humanwhocodes/config-array": "^0.11.13",
|
||||
"@eslint/js": "8.57.0",
|
||||
"@humanwhocodes/config-array": "^0.11.14",
|
||||
"@humanwhocodes/module-importer": "^1.0.1",
|
||||
"@nodelib/fs.walk": "^1.2.8",
|
||||
"@ungap/structured-clone": "^1.2.0",
|
||||
@ -5344,23 +5327,24 @@
|
||||
}
|
||||
},
|
||||
"node_modules/eslint-plugin-prettier": {
|
||||
"version": "5.0.1",
|
||||
"resolved": "https://registry.npmjs.org/eslint-plugin-prettier/-/eslint-plugin-prettier-5.0.1.tgz",
|
||||
"integrity": "sha512-m3u5RnR56asrwV/lDC4GHorlW75DsFfmUcjfCYylTUs85dBRnB7VM6xG8eCMJdeDRnppzmxZVf1GEPJvl1JmNg==",
|
||||
"version": "5.1.3",
|
||||
"resolved": "https://registry.npmjs.org/eslint-plugin-prettier/-/eslint-plugin-prettier-5.1.3.tgz",
|
||||
"integrity": "sha512-C9GCVAs4Eq7ZC/XFQHITLiHJxQngdtraXaM+LoUFoFp/lHNl2Zn8f3WQbe9HvTBBQ9YnKFB0/2Ajdqwo5D1EAw==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"prettier-linter-helpers": "^1.0.0",
|
||||
"synckit": "^0.8.5"
|
||||
"synckit": "^0.8.6"
|
||||
},
|
||||
"engines": {
|
||||
"node": "^14.18.0 || >=16.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/prettier"
|
||||
"url": "https://opencollective.com/eslint-plugin-prettier"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@types/eslint": ">=8.0.0",
|
||||
"eslint": ">=8.0.0",
|
||||
"eslint-config-prettier": "*",
|
||||
"prettier": ">=3.0.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
@ -5997,12 +5981,6 @@
|
||||
"node": ">= 0.6"
|
||||
}
|
||||
},
|
||||
"node_modules/fs-monkey": {
|
||||
"version": "1.0.5",
|
||||
"resolved": "https://registry.npmjs.org/fs-monkey/-/fs-monkey-1.0.5.tgz",
|
||||
"integrity": "sha512-8uMbBjrhzW76TYgEV27Y5E//W2f/lTFmx78P2w19FZSxarhI/798APGQyuGCwmkNxgwGRhrLfvWyLBvNtuOmew==",
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/fs.realpath": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/fs.realpath/-/fs.realpath-1.0.0.tgz",
|
||||
@ -6824,6 +6802,18 @@
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/is-network-error": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/is-network-error/-/is-network-error-1.0.1.tgz",
|
||||
"integrity": "sha512-OwQXkwBJeESyhFw+OumbJVD58BFBJJI5OM5S1+eyrDKlgDZPX2XNT5gXS56GSD3NPbbwUuMlR1Q71SRp5SobuQ==",
|
||||
"dev": true,
|
||||
"engines": {
|
||||
"node": ">=16"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/is-number": {
|
||||
"version": "7.0.0",
|
||||
"resolved": "https://registry.npmjs.org/is-number/-/is-number-7.0.0.tgz",
|
||||
@ -8132,17 +8122,27 @@
|
||||
}
|
||||
},
|
||||
"node_modules/memfs": {
|
||||
"version": "3.5.3",
|
||||
"resolved": "https://registry.npmjs.org/memfs/-/memfs-3.5.3.tgz",
|
||||
"integrity": "sha512-UERzLsxzllchadvbPs5aolHh65ISpKpM+ccLbOJ8/vvpBKmAWf+la7dXFy7Mr0ySHbdHrFv5kGFCUHHe6GFEmw==",
|
||||
"version": "4.7.7",
|
||||
"resolved": "https://registry.npmjs.org/memfs/-/memfs-4.7.7.tgz",
|
||||
"integrity": "sha512-x9qc6k88J/VVwnfTkJV8pRRswJ2156Rc4w5rciRqKceFDZ0y1MqsNL9pkg5sE0GOcDzZYbonreALhaHzg1siFw==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"fs-monkey": "^1.0.4"
|
||||
"tslib": "^2.0.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 4.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/streamich"
|
||||
}
|
||||
},
|
||||
"node_modules/memfs/node_modules/tslib": {
|
||||
"version": "2.6.2",
|
||||
"resolved": "https://registry.npmjs.org/tslib/-/tslib-2.6.2.tgz",
|
||||
"integrity": "sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==",
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/memorystream": {
|
||||
"version": "0.3.1",
|
||||
"resolved": "https://registry.npmjs.org/memorystream/-/memorystream-0.3.1.tgz",
|
||||
@ -8775,16 +8775,20 @@
|
||||
}
|
||||
},
|
||||
"node_modules/p-retry": {
|
||||
"version": "4.6.2",
|
||||
"resolved": "https://registry.npmjs.org/p-retry/-/p-retry-4.6.2.tgz",
|
||||
"integrity": "sha512-312Id396EbJdvRONlngUx0NydfrIQ5lsYu0znKVUzVvArzEIt08V1qhtyESbGVd1FGX7UKtiFp5uwKZdM8wIuQ==",
|
||||
"version": "6.2.0",
|
||||
"resolved": "https://registry.npmjs.org/p-retry/-/p-retry-6.2.0.tgz",
|
||||
"integrity": "sha512-JA6nkq6hKyWLLasXQXUrO4z8BUZGUt/LjlJxx8Gb2+2ntodU/SS63YZ8b0LUTbQ8ZB9iwOfhEPhg4ykKnn2KsA==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"@types/retry": "0.12.0",
|
||||
"@types/retry": "0.12.2",
|
||||
"is-network-error": "^1.0.0",
|
||||
"retry": "^0.13.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=8"
|
||||
"node": ">=16.17"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/p-try": {
|
||||
@ -9038,9 +9042,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/prettier": {
|
||||
"version": "3.1.0",
|
||||
"resolved": "https://registry.npmjs.org/prettier/-/prettier-3.1.0.tgz",
|
||||
"integrity": "sha512-TQLvXjq5IAibjh8EpBIkNKxO749UEWABoiIZehEPiY4GNpVdhaFKqSTu+QrlU6D2dPAfubRmtJTi4K4YkQ5eXw==",
|
||||
"version": "3.2.5",
|
||||
"resolved": "https://registry.npmjs.org/prettier/-/prettier-3.2.5.tgz",
|
||||
"integrity": "sha512-3/GWa9aOC0YeD7LUfvOG2NiDyhOWRvt1k+rcKhOuYnMY24iiCphgneUfJDyFXd6rZCAnuLBv6UeAULtrhT/F4A==",
|
||||
"dev": true,
|
||||
"bin": {
|
||||
"prettier": "bin/prettier.cjs"
|
||||
@ -9127,9 +9131,9 @@
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/protobufjs": {
|
||||
"version": "7.2.5",
|
||||
"resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.2.5.tgz",
|
||||
"integrity": "sha512-gGXRSXvxQ7UiPgfw8gevrfRWcTlSbOFg+p/N+JVJEK5VhueL2miT6qTymqAmjr1Q5WbOCyJbyrk6JfWKwlFn6A==",
|
||||
"version": "7.2.6",
|
||||
"resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.2.6.tgz",
|
||||
"integrity": "sha512-dgJaEDDL6x8ASUZ1YqWciTRrdOuYNzoOf27oHNfdyvKqHr5i0FV7FSLU+aIeFjyFgVxrpTOtQUi0BLLBymZaBw==",
|
||||
"hasInstallScript": true,
|
||||
"dependencies": {
|
||||
"@protobufjs/aspromise": "^1.1.2",
|
||||
@ -9830,9 +9834,9 @@
|
||||
"dev": true
|
||||
},
|
||||
"node_modules/serialize-javascript": {
|
||||
"version": "6.0.1",
|
||||
"resolved": "https://registry.npmjs.org/serialize-javascript/-/serialize-javascript-6.0.1.tgz",
|
||||
"integrity": "sha512-owoXEFjWRllis8/M1Q+Cw5k8ZH40e3zhp/ovX+Xr/vi1qj6QesbyXXViFbpNvWvPNAD62SutwEXavefrLJWj7w==",
|
||||
"version": "6.0.2",
|
||||
"resolved": "https://registry.npmjs.org/serialize-javascript/-/serialize-javascript-6.0.2.tgz",
|
||||
"integrity": "sha512-Saa1xPByTTq2gdeFZYLLo+RFE35NHZkAbqZeWNd3BpzppeVisAqpDjcp8dyf6uIvEqJRd46jemmyA4iFIeVk8g==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"randombytes": "^2.1.0"
|
||||
@ -10044,9 +10048,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/socket.io-client": {
|
||||
"version": "4.7.2",
|
||||
"resolved": "https://registry.npmjs.org/socket.io-client/-/socket.io-client-4.7.2.tgz",
|
||||
"integrity": "sha512-vtA0uD4ibrYD793SOIAwlo8cj6haOeMHrGvwPxJsxH7CeIksqJ+3Zc06RvWTIFgiSqx4A3sOnTXpfAEE2Zyz6w==",
|
||||
"version": "4.7.4",
|
||||
"resolved": "https://registry.npmjs.org/socket.io-client/-/socket.io-client-4.7.4.tgz",
|
||||
"integrity": "sha512-wh+OkeF0rAVCrABWQBaEjLfb7DVPotMbu0cgWgyR0v6eA4EoVnAwcIeIbcdTE3GT/H3kbdLl7OoH2+asoDRIIg==",
|
||||
"dependencies": {
|
||||
"@socket.io/component-emitter": "~3.1.0",
|
||||
"debug": "~4.3.2",
|
||||
@ -10466,9 +10470,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/terser": {
|
||||
"version": "5.24.0",
|
||||
"resolved": "https://registry.npmjs.org/terser/-/terser-5.24.0.tgz",
|
||||
"integrity": "sha512-ZpGR4Hy3+wBEzVEnHvstMvqpD/nABNelQn/z2r0fjVWGQsN3bpOLzQlqDxmb4CDZnXq5lpjnQ+mHQLAOpfM5iw==",
|
||||
"version": "5.28.1",
|
||||
"resolved": "https://registry.npmjs.org/terser/-/terser-5.28.1.tgz",
|
||||
"integrity": "sha512-wM+bZp54v/E9eRRGXb5ZFDvinrJIOaTapx3WUokyVGZu5ucVCK55zEgGd5Dl2fSr3jUo5sDiERErUWLY6QPFyA==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"@jridgewell/source-map": "^0.3.3",
|
||||
@ -10484,16 +10488,16 @@
|
||||
}
|
||||
},
|
||||
"node_modules/terser-webpack-plugin": {
|
||||
"version": "5.3.9",
|
||||
"resolved": "https://registry.npmjs.org/terser-webpack-plugin/-/terser-webpack-plugin-5.3.9.tgz",
|
||||
"integrity": "sha512-ZuXsqE07EcggTWQjXUj+Aot/OMcD0bMKGgF63f7UxYcu5/AJF53aIpK1YoP5xR9l6s/Hy2b+t1AM0bLNPRuhwA==",
|
||||
"version": "5.3.10",
|
||||
"resolved": "https://registry.npmjs.org/terser-webpack-plugin/-/terser-webpack-plugin-5.3.10.tgz",
|
||||
"integrity": "sha512-BKFPWlPDndPs+NGGCr1U59t0XScL5317Y0UReNrHaw9/FwhPENlq6bfgs+4yPfyP51vqC1bQ4rp1EfXW5ZSH9w==",
|
||||
"dev": true,
|
||||
"dependencies": {
|
||||
"@jridgewell/trace-mapping": "^0.3.17",
|
||||
"@jridgewell/trace-mapping": "^0.3.20",
|
||||
"jest-worker": "^27.4.5",
|
||||
"schema-utils": "^3.1.1",
|
||||
"serialize-javascript": "^6.0.1",
|
||||
"terser": "^5.16.8"
|
||||
"terser": "^5.26.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 10.13.0"
|
||||
@ -10827,9 +10831,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/typescript": {
|
||||
"version": "5.3.2",
|
||||
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.2.tgz",
|
||||
"integrity": "sha512-6l+RyNy7oAHDfxC4FzSJcz9vnjTKxrLpDG5M2Vu4SHRVNg6xzqZp6LYSR9zjqQTu8DU/f5xwxUdADOkbrIX2gQ==",
|
||||
"version": "5.3.3",
|
||||
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.3.tgz",
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@ -11208,66 +11217,98 @@
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||||
@ -1,6 +1,6 @@
|
||||
{
|
||||
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|
||||
"version": "1.0.180",
|
||||
"version": "1.0.182",
|
||||
"description": "",
|
||||
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|
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"directories": {
|
||||
@ -26,35 +26,35 @@
|
||||
"author": "wataru.okada@flect.co.jp",
|
||||
"license": "ISC",
|
||||
"devDependencies": {
|
||||
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|
||||
"@types/jest": "^29.5.10",
|
||||
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|
||||
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|
||||
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|
||||
"eslint": "^8.55.0",
|
||||
"@types/audioworklet": "^0.0.54",
|
||||
"@types/jest": "^29.5.12",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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},
|
||||
"dependencies": {
|
||||
"@types/readable-stream": "^4.0.9",
|
||||
"amazon-chime-sdk-js": "^3.18.2",
|
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"@types/readable-stream": "^4.0.10",
|
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"amazon-chime-sdk-js": "^3.20.0",
|
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"buffer": "^6.0.3",
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|
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"socket.io-client": "^4.7.4"
|
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}
|
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}
|
||||
|
||||
@ -13,8 +13,10 @@ export const VoiceChangerType = {
|
||||
Beatrice: "Beatrice",
|
||||
LLVC: "LLVC",
|
||||
WebModel: "WebModel",
|
||||
EasyVC: "EasyVC",
|
||||
} as const;
|
||||
export type VoiceChangerType = (typeof VoiceChangerType)[keyof typeof VoiceChangerType];
|
||||
export type VoiceChangerType =
|
||||
(typeof VoiceChangerType)[keyof typeof VoiceChangerType];
|
||||
|
||||
export const StaticModel = {
|
||||
BeatriceJVS: "Beatrice-JVS",
|
||||
@ -29,14 +31,16 @@ export const InputSampleRate = {
|
||||
"44100": 44100,
|
||||
"24000": 24000,
|
||||
} as const;
|
||||
export type InputSampleRate = (typeof InputSampleRate)[keyof typeof InputSampleRate];
|
||||
export type InputSampleRate =
|
||||
(typeof InputSampleRate)[keyof typeof InputSampleRate];
|
||||
|
||||
export const ModelSamplingRate = {
|
||||
"48000": 48000,
|
||||
"40000": 40000,
|
||||
"32000": 32000,
|
||||
} as const;
|
||||
export type ModelSamplingRate = (typeof InputSampleRate)[keyof typeof InputSampleRate];
|
||||
export type ModelSamplingRate =
|
||||
(typeof InputSampleRate)[keyof typeof InputSampleRate];
|
||||
|
||||
export const CrossFadeOverlapSize = {
|
||||
"128": 128,
|
||||
@ -46,7 +50,8 @@ export const CrossFadeOverlapSize = {
|
||||
"2048": 2048,
|
||||
"4096": 4096,
|
||||
} as const;
|
||||
export type CrossFadeOverlapSize = (typeof CrossFadeOverlapSize)[keyof typeof CrossFadeOverlapSize];
|
||||
export type CrossFadeOverlapSize =
|
||||
(typeof CrossFadeOverlapSize)[keyof typeof CrossFadeOverlapSize];
|
||||
|
||||
export const F0Detector = {
|
||||
dio: "dio",
|
||||
@ -141,7 +146,8 @@ export const ServerSettingKey = {
|
||||
inputSampleRate: "inputSampleRate",
|
||||
enableDirectML: "enableDirectML",
|
||||
} as const;
|
||||
export type ServerSettingKey = (typeof ServerSettingKey)[keyof typeof ServerSettingKey];
|
||||
export type ServerSettingKey =
|
||||
(typeof ServerSettingKey)[keyof typeof ServerSettingKey];
|
||||
|
||||
export type VoiceChangerServerSetting = {
|
||||
passThrough: boolean;
|
||||
@ -317,7 +323,16 @@ export type WebModelSlot = ModelSlot & {
|
||||
samplingRate: number;
|
||||
};
|
||||
|
||||
export type ModelSlotUnion = RVCModelSlot | MMVCv13ModelSlot | MMVCv15ModelSlot | SoVitsSvc40ModelSlot | DDSPSVCModelSlot | DiffusionSVCModelSlot | BeatriceModelSlot | LLVCModelSlot | WebModelSlot;
|
||||
export type ModelSlotUnion =
|
||||
| RVCModelSlot
|
||||
| MMVCv13ModelSlot
|
||||
| MMVCv15ModelSlot
|
||||
| SoVitsSvc40ModelSlot
|
||||
| DDSPSVCModelSlot
|
||||
| DiffusionSVCModelSlot
|
||||
| BeatriceModelSlot
|
||||
| LLVCModelSlot
|
||||
| WebModelSlot;
|
||||
|
||||
type ServerAudioDevice = {
|
||||
kind: "audioinput" | "audiooutput";
|
||||
@ -471,13 +486,15 @@ export const SendingSampleRate = {
|
||||
"44100": 44100,
|
||||
"24000": 24000,
|
||||
} as const;
|
||||
export type SendingSampleRate = (typeof SendingSampleRate)[keyof typeof SendingSampleRate];
|
||||
export type SendingSampleRate =
|
||||
(typeof SendingSampleRate)[keyof typeof SendingSampleRate];
|
||||
|
||||
export const DownSamplingMode = {
|
||||
decimate: "decimate",
|
||||
average: "average",
|
||||
} as const;
|
||||
export type DownSamplingMode = (typeof DownSamplingMode)[keyof typeof DownSamplingMode];
|
||||
export type DownSamplingMode =
|
||||
(typeof DownSamplingMode)[keyof typeof DownSamplingMode];
|
||||
|
||||
export type WorkletNodeSetting = {
|
||||
serverUrl: string;
|
||||
@ -552,9 +569,11 @@ export const VOICE_CHANGER_CLIENT_EXCEPTION = {
|
||||
ERR_SIO_INVALID_RESPONSE: "ERR_SIO_INVALID_RESPONSE",
|
||||
ERR_REST_INVALID_RESPONSE: "ERR_REST_INVALID_RESPONSE",
|
||||
ERR_MIC_STREAM_NOT_INITIALIZED: "ERR_MIC_STREAM_NOT_INITIALIZED",
|
||||
ERR_INTERNAL_AUDIO_PROCESS_CALLBACK_IS_NOT_INITIALIZED: "ERR_INTERNAL_AUDIO_PROCESS_CALLBACK_IS_NOT_INITIALIZED",
|
||||
ERR_INTERNAL_AUDIO_PROCESS_CALLBACK_IS_NOT_INITIALIZED:
|
||||
"ERR_INTERNAL_AUDIO_PROCESS_CALLBACK_IS_NOT_INITIALIZED",
|
||||
} as const;
|
||||
export type VOICE_CHANGER_CLIENT_EXCEPTION = (typeof VOICE_CHANGER_CLIENT_EXCEPTION)[keyof typeof VOICE_CHANGER_CLIENT_EXCEPTION];
|
||||
export type VOICE_CHANGER_CLIENT_EXCEPTION =
|
||||
(typeof VOICE_CHANGER_CLIENT_EXCEPTION)[keyof typeof VOICE_CHANGER_CLIENT_EXCEPTION];
|
||||
|
||||
////////////////////////////////////
|
||||
// indexedDB
|
||||
@ -563,7 +582,8 @@ export const INDEXEDDB_DB_APP_NAME = "INDEXEDDB_KEY_VOICE_CHANGER";
|
||||
export const INDEXEDDB_DB_NAME = "INDEXEDDB_KEY_VOICE_CHANGER_DB";
|
||||
export const INDEXEDDB_KEY_CLIENT = "INDEXEDDB_KEY_VOICE_CHANGER_LIB_CLIENT";
|
||||
export const INDEXEDDB_KEY_SERVER = "INDEXEDDB_KEY_VOICE_CHANGER_LIB_SERVER";
|
||||
export const INDEXEDDB_KEY_MODEL_DATA = "INDEXEDDB_KEY_VOICE_CHANGER_LIB_MODEL_DATA";
|
||||
export const INDEXEDDB_KEY_MODEL_DATA =
|
||||
"INDEXEDDB_KEY_VOICE_CHANGER_LIB_MODEL_DATA";
|
||||
|
||||
// ONNX
|
||||
export type OnnxExporterInfo = {
|
||||
|
||||
@ -1,11 +1,20 @@
|
||||
import { useState, useMemo } from "react";
|
||||
import { VoiceChangerServerSetting, ServerInfo, ServerSettingKey, OnnxExporterInfo, MergeModelRequest, VoiceChangerType, DefaultServerSetting } from "../const";
|
||||
import {
|
||||
VoiceChangerServerSetting,
|
||||
ServerInfo,
|
||||
ServerSettingKey,
|
||||
OnnxExporterInfo,
|
||||
MergeModelRequest,
|
||||
VoiceChangerType,
|
||||
DefaultServerSetting,
|
||||
} from "../const";
|
||||
import { VoiceChangerClient } from "../VoiceChangerClient";
|
||||
|
||||
export const ModelAssetName = {
|
||||
iconFile: "iconFile",
|
||||
} as const;
|
||||
export type ModelAssetName = (typeof ModelAssetName)[keyof typeof ModelAssetName];
|
||||
export type ModelAssetName =
|
||||
(typeof ModelAssetName)[keyof typeof ModelAssetName];
|
||||
|
||||
export const ModelFileKind = {
|
||||
mmvcv13Config: "mmvcv13Config",
|
||||
@ -32,6 +41,8 @@ export const ModelFileKind = {
|
||||
|
||||
llvcModel: "llvcModel",
|
||||
llvcConfig: "llvcConfig",
|
||||
|
||||
easyVCModel: "easyVCModel",
|
||||
} as const;
|
||||
export type ModelFileKind = (typeof ModelFileKind)[keyof typeof ModelFileKind];
|
||||
|
||||
@ -80,12 +91,23 @@ export type ServerSettingState = {
|
||||
getOnnx: () => Promise<OnnxExporterInfo>;
|
||||
mergeModel: (request: MergeModelRequest) => Promise<ServerInfo>;
|
||||
updateModelDefault: () => Promise<ServerInfo>;
|
||||
updateModelInfo: (slot: number, key: string, val: string) => Promise<ServerInfo>;
|
||||
uploadAssets: (slot: number, name: ModelAssetName, file: File) => Promise<void>;
|
||||
updateModelInfo: (
|
||||
slot: number,
|
||||
key: string,
|
||||
val: string
|
||||
) => Promise<ServerInfo>;
|
||||
uploadAssets: (
|
||||
slot: number,
|
||||
name: ModelAssetName,
|
||||
file: File
|
||||
) => Promise<void>;
|
||||
};
|
||||
|
||||
export const useServerSetting = (props: UseServerSettingProps): ServerSettingState => {
|
||||
const [serverSetting, _setServerSetting] = useState<ServerInfo>(DefaultServerSetting);
|
||||
export const useServerSetting = (
|
||||
props: UseServerSettingProps
|
||||
): ServerSettingState => {
|
||||
const [serverSetting, _setServerSetting] =
|
||||
useState<ServerInfo>(DefaultServerSetting);
|
||||
const setServerSetting = (info: ServerInfo) => {
|
||||
if (!info.modelSlots) {
|
||||
// サーバが情報を空で返したとき。Web版対策
|
||||
@ -101,12 +123,17 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
return async (setting: ServerInfo) => {
|
||||
if (!props.voiceChangerClient) return;
|
||||
for (let i = 0; i < Object.values(ServerSettingKey).length; i++) {
|
||||
const k = Object.values(ServerSettingKey)[i] as keyof VoiceChangerServerSetting;
|
||||
const k = Object.values(ServerSettingKey)[
|
||||
i
|
||||
] as keyof VoiceChangerServerSetting;
|
||||
const cur_v = serverSetting[k];
|
||||
const new_v = setting[k];
|
||||
|
||||
if (cur_v != new_v) {
|
||||
const res = await props.voiceChangerClient.updateServerSettings(k, "" + new_v);
|
||||
const res = await props.voiceChangerClient.updateServerSettings(
|
||||
k,
|
||||
"" + new_v
|
||||
);
|
||||
setServerSetting(res);
|
||||
}
|
||||
}
|
||||
@ -121,10 +148,21 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
|
||||
// (e) モデルアップロード
|
||||
const _uploadFile2 = useMemo(() => {
|
||||
return async (file: File, onprogress: (progress: number, end: boolean) => void, dir: string = "") => {
|
||||
return async (
|
||||
file: File,
|
||||
onprogress: (progress: number, end: boolean) => void,
|
||||
dir: string = ""
|
||||
) => {
|
||||
if (!props.voiceChangerClient) return;
|
||||
const num = await props.voiceChangerClient.uploadFile2(dir, file, onprogress);
|
||||
const res = await props.voiceChangerClient.concatUploadedFile(dir + file.name, num);
|
||||
const num = await props.voiceChangerClient.uploadFile2(
|
||||
dir,
|
||||
file,
|
||||
onprogress
|
||||
);
|
||||
const res = await props.voiceChangerClient.concatUploadedFile(
|
||||
dir + file.name,
|
||||
num
|
||||
);
|
||||
console.log("uploaded", num, res);
|
||||
};
|
||||
}, [props.voiceChangerClient]);
|
||||
@ -159,7 +197,11 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
}),
|
||||
};
|
||||
|
||||
const loadPromise = props.voiceChangerClient.loadModel(0, false, JSON.stringify(params));
|
||||
const loadPromise = props.voiceChangerClient.loadModel(
|
||||
0,
|
||||
false,
|
||||
JSON.stringify(params)
|
||||
);
|
||||
await loadPromise;
|
||||
|
||||
setUploadProgress(0);
|
||||
@ -180,7 +222,9 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
name,
|
||||
file: file.name,
|
||||
};
|
||||
await props.voiceChangerClient.uploadAssets(JSON.stringify(assetUploadSetting));
|
||||
await props.voiceChangerClient.uploadAssets(
|
||||
JSON.stringify(assetUploadSetting)
|
||||
);
|
||||
reloadServerInfo();
|
||||
};
|
||||
}, [props.voiceChangerClient]);
|
||||
@ -209,7 +253,11 @@ export const useServerSetting = (props: UseServerSettingProps): ServerSettingSta
|
||||
return serverInfo;
|
||||
};
|
||||
const updateModelInfo = async (slot: number, key: string, val: string) => {
|
||||
const serverInfo = await props.voiceChangerClient!.updateModelInfo(slot, key, val);
|
||||
const serverInfo = await props.voiceChangerClient!.updateModelInfo(
|
||||
slot,
|
||||
key,
|
||||
val
|
||||
);
|
||||
setServerSetting(serverInfo);
|
||||
return serverInfo;
|
||||
};
|
||||
|
||||
@ -58,6 +58,7 @@ def setupArgParser():
|
||||
parser.add_argument("--hubert_base", type=str, default="pretrain/hubert_base.pt", help="path to hubert_base model(pytorch)")
|
||||
parser.add_argument("--hubert_base_jp", type=str, default="pretrain/rinna_hubert_base_jp.pt", help="path to hubert_base_jp model(pytorch)")
|
||||
parser.add_argument("--hubert_soft", type=str, default="pretrain/hubert/hubert-soft-0d54a1f4.pt", help="path to hubert_soft model(pytorch)")
|
||||
parser.add_argument("--whisper_tiny", type=str, default="pretrain/whisper_tiny.pt", help="path to hubert_soft model(pytorch)")
|
||||
parser.add_argument("--nsf_hifigan", type=str, default="pretrain/nsf_hifigan/model", help="path to nsf_hifigan model(pytorch)")
|
||||
parser.add_argument("--crepe_onnx_full", type=str, default="pretrain/crepe_onnx_full.onnx", help="path to crepe_onnx_full")
|
||||
parser.add_argument("--crepe_onnx_tiny", type=str, default="pretrain/crepe_onnx_tiny.onnx", help="path to crepe_onnx_tiny")
|
||||
@ -106,6 +107,7 @@ voiceChangerParams = VoiceChangerParams(
|
||||
rmvpe=args.rmvpe,
|
||||
rmvpe_onnx=args.rmvpe_onnx,
|
||||
sample_mode=args.sample_mode,
|
||||
whisper_tiny=args.whisper_tiny,
|
||||
)
|
||||
vcparams = VoiceChangerParamsManager.get_instance()
|
||||
vcparams.setParams(voiceChangerParams)
|
||||
|
||||
@ -14,6 +14,7 @@ VoiceChangerType: TypeAlias = Literal[
|
||||
"Diffusion-SVC",
|
||||
"Beatrice",
|
||||
"LLVC",
|
||||
"EasyVC",
|
||||
]
|
||||
|
||||
StaticSlot: TypeAlias = Literal["Beatrice-JVS",]
|
||||
@ -56,7 +57,12 @@ def getFrontendPath():
|
||||
return frontend_path
|
||||
|
||||
|
||||
EmbedderType: TypeAlias = Literal["hubert_base", "contentvec", "hubert-base-japanese"]
|
||||
EmbedderType: TypeAlias = Literal[
|
||||
"hubert_base",
|
||||
"contentvec",
|
||||
"hubert-base-japanese",
|
||||
"whisper",
|
||||
]
|
||||
|
||||
|
||||
class EnumInferenceTypes(Enum):
|
||||
@ -70,6 +76,8 @@ class EnumInferenceTypes(Enum):
|
||||
onnxRVC = "onnxRVC"
|
||||
onnxRVCNono = "onnxRVCNono"
|
||||
|
||||
easyVC = "easyVC"
|
||||
|
||||
|
||||
DiffusionSVCInferenceType: TypeAlias = Literal["combo",]
|
||||
|
||||
|
||||
@ -141,6 +141,14 @@ class LLVCModelSlot(ModelSlot):
|
||||
configFile: str = ""
|
||||
|
||||
|
||||
@dataclass
|
||||
class EasyVCModelSlot(ModelSlot):
|
||||
voiceChangerType: VoiceChangerType = "EasyVC"
|
||||
modelFile: str = ""
|
||||
version: str = ""
|
||||
samplingRate: int = -1
|
||||
|
||||
|
||||
ModelSlots: TypeAlias = Union[
|
||||
ModelSlot,
|
||||
RVCModelSlot,
|
||||
@ -151,6 +159,7 @@ ModelSlots: TypeAlias = Union[
|
||||
DiffusionSVCModelSlot,
|
||||
BeatriceModelSlot,
|
||||
LLVCModelSlot,
|
||||
EasyVCModelSlot,
|
||||
]
|
||||
|
||||
|
||||
@ -188,6 +197,9 @@ def loadSlotInfo(model_dir: str, slotIndex: int | StaticSlot) -> ModelSlots:
|
||||
elif slotInfo.voiceChangerType == "LLVC":
|
||||
slotInfoKey.extend(list(LLVCModelSlot.__annotations__.keys()))
|
||||
return LLVCModelSlot(**{k: v for k, v in jsonDict.items() if k in slotInfoKey})
|
||||
elif slotInfo.voiceChangerType == "EasyVC":
|
||||
slotInfoKey.extend(list(EasyVCModelSlot.__annotations__.keys()))
|
||||
return EasyVCModelSlot(**{k: v for k, v in jsonDict.items() if k in slotInfoKey})
|
||||
else:
|
||||
return ModelSlot()
|
||||
|
||||
|
||||
@ -19,9 +19,19 @@ def downloadWeight(voiceChangerParams: VoiceChangerParams):
|
||||
crepe_onnx_tiny = voiceChangerParams.crepe_onnx_tiny
|
||||
rmvpe = voiceChangerParams.rmvpe
|
||||
rmvpe_onnx = voiceChangerParams.rmvpe_onnx
|
||||
whisper_tiny = voiceChangerParams.whisper_tiny
|
||||
|
||||
weight_files = [content_vec_500_onnx, hubert_base, hubert_base_jp, hubert_soft,
|
||||
nsf_hifigan, crepe_onnx_full, crepe_onnx_tiny, rmvpe]
|
||||
weight_files = [
|
||||
content_vec_500_onnx,
|
||||
hubert_base,
|
||||
hubert_base_jp,
|
||||
hubert_soft,
|
||||
nsf_hifigan,
|
||||
crepe_onnx_full,
|
||||
crepe_onnx_tiny,
|
||||
rmvpe,
|
||||
whisper_tiny,
|
||||
]
|
||||
|
||||
# file exists check (currently only for rvc)
|
||||
downloadParams = []
|
||||
@ -119,6 +129,15 @@ def downloadWeight(voiceChangerParams: VoiceChangerParams):
|
||||
}
|
||||
)
|
||||
|
||||
if os.path.exists(whisper_tiny) is False:
|
||||
downloadParams.append(
|
||||
{
|
||||
"url": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
|
||||
"saveTo": whisper_tiny,
|
||||
"position": 10,
|
||||
}
|
||||
)
|
||||
|
||||
with ThreadPoolExecutor() as pool:
|
||||
pool.map(download, downloadParams)
|
||||
|
||||
|
||||
326
server/voice_changer/EasyVC/EasyVC.py
Normal file
326
server/voice_changer/EasyVC/EasyVC.py
Normal file
@ -0,0 +1,326 @@
|
||||
"""
|
||||
VoiceChangerV2向け
|
||||
"""
|
||||
|
||||
from dataclasses import asdict
|
||||
import numpy as np
|
||||
import torch
|
||||
from data.ModelSlot import RVCModelSlot
|
||||
from mods.log_control import VoiceChangaerLogger
|
||||
from voice_changer.EasyVC.EasyVCSettings import EasyVCSettings
|
||||
from voice_changer.EasyVC.pipeline.Pipeline import Pipeline
|
||||
from voice_changer.EasyVC.pipeline.PipelineGenerator import createPipeline
|
||||
|
||||
from voice_changer.RVC.RVCSettings import RVCSettings
|
||||
from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
|
||||
from voice_changer.utils.Timer import Timer2
|
||||
from voice_changer.utils.VoiceChangerModel import (
|
||||
AudioInOut,
|
||||
PitchfInOut,
|
||||
FeatureInOut,
|
||||
VoiceChangerModel,
|
||||
)
|
||||
from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
|
||||
from voice_changer.RVC.onnxExporter.export2onnx import export2onnx
|
||||
from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
|
||||
from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
|
||||
|
||||
from Exceptions import (
|
||||
DeviceCannotSupportHalfPrecisionException,
|
||||
PipelineCreateException,
|
||||
PipelineNotInitializedException,
|
||||
)
|
||||
import resampy
|
||||
from typing import cast
|
||||
|
||||
logger = VoiceChangaerLogger.get_instance().getLogger()
|
||||
|
||||
|
||||
class EasyVC(VoiceChangerModel):
|
||||
def __init__(self, params: VoiceChangerParams, slotInfo: RVCModelSlot):
|
||||
logger.info("[Voice Changer] [EasyVC] Creating instance ")
|
||||
self.voiceChangerType = "RVC"
|
||||
|
||||
self.deviceManager = DeviceManager.get_instance()
|
||||
EmbedderManager.initialize(params)
|
||||
PitchExtractorManager.initialize(params)
|
||||
self.settings = EasyVCSettings()
|
||||
self.params = params
|
||||
# self.pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector, self.settings.gpu)
|
||||
|
||||
self.pipeline: Pipeline | None = None
|
||||
|
||||
self.audio_buffer: AudioInOut | None = None
|
||||
self.pitchf_buffer: PitchfInOut | None = None
|
||||
self.feature_buffer: FeatureInOut | None = None
|
||||
self.prevVol = 0.0
|
||||
self.slotInfo = slotInfo
|
||||
# self.initialize()
|
||||
|
||||
def initialize(self):
|
||||
logger.info("[Voice Changer][EasyVC] Initializing... ")
|
||||
|
||||
# pipelineの生成
|
||||
try:
|
||||
self.pipeline = createPipeline(self.params, self.slotInfo, self.settings.gpu, self.settings.f0Detector)
|
||||
except PipelineCreateException as e: # NOQA
|
||||
logger.error("[Voice Changer] pipeline create failed. check your model is valid.")
|
||||
return
|
||||
|
||||
# その他の設定
|
||||
logger.info("[Voice Changer] [EasyVC] Initializing... done")
|
||||
|
||||
def setSamplingRate(self, inputSampleRate, outputSampleRate):
|
||||
self.inputSampleRate = inputSampleRate
|
||||
self.outputSampleRate = outputSampleRate
|
||||
# self.initialize()
|
||||
|
||||
def update_settings(self, key: str, val: int | float | str):
|
||||
logger.info(f"[Voice Changer][RVC]: update_settings {key}:{val}")
|
||||
if key in self.settings.intData:
|
||||
setattr(self.settings, key, int(val))
|
||||
if key == "gpu":
|
||||
self.deviceManager.setForceTensor(False)
|
||||
self.initialize()
|
||||
elif key in self.settings.floatData:
|
||||
setattr(self.settings, key, float(val))
|
||||
elif key in self.settings.strData:
|
||||
setattr(self.settings, key, str(val))
|
||||
if key == "f0Detector" and self.pipeline is not None:
|
||||
pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector, self.settings.gpu)
|
||||
self.pipeline.setPitchExtractor(pitchExtractor)
|
||||
else:
|
||||
return False
|
||||
return True
|
||||
|
||||
def get_info(self):
|
||||
data = asdict(self.settings)
|
||||
if self.pipeline is not None:
|
||||
pipelineInfo = self.pipeline.getPipelineInfo()
|
||||
data["pipelineInfo"] = pipelineInfo
|
||||
else:
|
||||
data["pipelineInfo"] = "None"
|
||||
return data
|
||||
|
||||
def get_processing_sampling_rate(self):
|
||||
return self.slotInfo.samplingRate
|
||||
|
||||
def generate_input(
|
||||
self,
|
||||
newData: AudioInOut,
|
||||
crossfadeSize: int,
|
||||
solaSearchFrame: int,
|
||||
extra_frame: int,
|
||||
):
|
||||
# 16k で入ってくる。
|
||||
inputSize = newData.shape[0]
|
||||
newData = newData.astype(np.float32) / 32768.0
|
||||
newFeatureLength = inputSize // 160 # hopsize:=160
|
||||
|
||||
if self.audio_buffer is not None:
|
||||
# 過去のデータに連結
|
||||
self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)
|
||||
# if self.slotInfo.f0:
|
||||
# self.pitchf_buffer = np.concatenate([self.pitchf_buffer, np.zeros(newFeatureLength)], 0)
|
||||
self.feature_buffer = np.concatenate(
|
||||
[
|
||||
self.feature_buffer,
|
||||
# np.zeros([newFeatureLength, self.slotInfo.embChannels]),
|
||||
np.zeros([newFeatureLength, 768]),
|
||||
],
|
||||
0,
|
||||
)
|
||||
else:
|
||||
self.audio_buffer = newData
|
||||
# if self.slotInfo.f0:
|
||||
# self.pitchf_buffer = np.zeros(newFeatureLength)
|
||||
self.feature_buffer = np.zeros([newFeatureLength, 768])
|
||||
|
||||
convertSize = inputSize + crossfadeSize + solaSearchFrame + extra_frame
|
||||
|
||||
if convertSize % 160 != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
|
||||
convertSize = convertSize + (160 - (convertSize % 160))
|
||||
outSize = int(((convertSize - extra_frame) / 16000) * self.slotInfo.samplingRate)
|
||||
|
||||
# バッファがたまっていない場合はzeroで補う
|
||||
if self.audio_buffer.shape[0] < convertSize:
|
||||
self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer])
|
||||
# if self.slotInfo.f0:
|
||||
# self.pitchf_buffer = np.concatenate([np.zeros([convertSize // 160]), self.pitchf_buffer])
|
||||
self.feature_buffer = np.concatenate(
|
||||
[
|
||||
np.zeros([convertSize // 160, 768]),
|
||||
self.feature_buffer,
|
||||
]
|
||||
)
|
||||
|
||||
# 不要部分をトリミング
|
||||
convertOffset = -1 * convertSize
|
||||
featureOffset = convertOffset // 160
|
||||
self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出
|
||||
# if self.slotInfo.f0:
|
||||
# self.pitchf_buffer = self.pitchf_buffer[featureOffset:]
|
||||
self.feature_buffer = self.feature_buffer[featureOffset:]
|
||||
|
||||
# 出力部分だけ切り出して音量を確認。(TODO:段階的消音にする)
|
||||
cropOffset = -1 * (inputSize + crossfadeSize)
|
||||
cropEnd = -1 * (crossfadeSize)
|
||||
crop = self.audio_buffer[cropOffset:cropEnd]
|
||||
vol = np.sqrt(np.square(crop).mean())
|
||||
vol = max(vol, self.prevVol * 0.0)
|
||||
self.prevVol = vol
|
||||
|
||||
return (
|
||||
self.audio_buffer,
|
||||
self.pitchf_buffer,
|
||||
self.feature_buffer,
|
||||
convertSize,
|
||||
vol,
|
||||
outSize,
|
||||
)
|
||||
|
||||
def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int):
|
||||
if self.pipeline is None:
|
||||
logger.info("[Voice Changer] Pipeline is not initialized.")
|
||||
raise PipelineNotInitializedException()
|
||||
|
||||
enableTimer = True
|
||||
with Timer2("infer_easyvc", enableTimer) as t:
|
||||
|
||||
# 処理は16Kで実施(Pitch, embed, (infer))
|
||||
receivedData = cast(
|
||||
AudioInOut,
|
||||
resampy.resample(
|
||||
receivedData,
|
||||
self.inputSampleRate,
|
||||
16000,
|
||||
filter="kaiser_fast",
|
||||
),
|
||||
)
|
||||
crossfade_frame = int((crossfade_frame / self.inputSampleRate) * 16000)
|
||||
sola_search_frame = int((sola_search_frame / self.inputSampleRate) * 16000)
|
||||
extra_frame = int((self.settings.extraConvertSize / self.inputSampleRate) * 16000)
|
||||
|
||||
# 入力データ生成
|
||||
data = self.generate_input(receivedData, crossfade_frame, sola_search_frame, extra_frame)
|
||||
t.record("generate_input")
|
||||
|
||||
audio = data[0]
|
||||
pitchf = data[1]
|
||||
feature = data[2]
|
||||
convertSize = data[3]
|
||||
vol = data[4]
|
||||
outSize = data[5]
|
||||
|
||||
if vol < self.settings.silentThreshold:
|
||||
return np.zeros(convertSize).astype(np.int16) * np.sqrt(vol)
|
||||
|
||||
device = self.pipeline.device
|
||||
|
||||
audio = torch.from_numpy(audio).to(device=device, dtype=torch.float32)
|
||||
repeat = 0
|
||||
sid = self.settings.dstId
|
||||
f0_up_key = self.settings.tran
|
||||
index_rate = self.settings.indexRatio
|
||||
protect = self.settings.protect
|
||||
|
||||
# if_f0 = 1 if self.slotInfo.f0 else 0
|
||||
if_f0 = 0
|
||||
# embOutputLayer = self.slotInfo.embOutputLayer
|
||||
# useFinalProj = self.slotInfo.useFinalProj
|
||||
t.record("pre-process")
|
||||
|
||||
try:
|
||||
audio_out, self.pitchf_buffer, self.feature_buffer = self.pipeline.exec(
|
||||
sid,
|
||||
audio,
|
||||
pitchf,
|
||||
feature,
|
||||
f0_up_key,
|
||||
index_rate,
|
||||
if_f0,
|
||||
# 0,
|
||||
self.settings.extraConvertSize / self.inputSampleRate if self.settings.silenceFront else 0.0, # extaraDataSizeの秒数。入力のサンプリングレートで算出
|
||||
repeat,
|
||||
outSize,
|
||||
)
|
||||
t.record("pipeline-exec")
|
||||
# result = audio_out.detach().cpu().numpy() * np.sqrt(vol)
|
||||
result = audio_out[-outSize:].detach().cpu().numpy() * np.sqrt(vol)
|
||||
|
||||
result = cast(
|
||||
AudioInOut,
|
||||
resampy.resample(
|
||||
result,
|
||||
16000,
|
||||
self.outputSampleRate,
|
||||
filter="kaiser_fast",
|
||||
),
|
||||
)
|
||||
t.record("resample")
|
||||
|
||||
return result
|
||||
except DeviceCannotSupportHalfPrecisionException as e: # NOQA
|
||||
logger.warn("[Device Manager] Device cannot support half precision. Fallback to float....")
|
||||
self.deviceManager.setForceTensor(True)
|
||||
self.initialize()
|
||||
# raise e
|
||||
|
||||
return
|
||||
|
||||
def __del__(self):
|
||||
del self.pipeline
|
||||
|
||||
# print("---------- REMOVING ---------------")
|
||||
|
||||
# remove_path = os.path.join("RVC")
|
||||
# sys.path = [x for x in sys.path if x.endswith(remove_path) is False]
|
||||
|
||||
# for key in list(sys.modules):
|
||||
# val = sys.modules.get(key)
|
||||
# try:
|
||||
# file_path = val.__file__
|
||||
# if file_path.find("RVC" + os.path.sep) >= 0:
|
||||
# # print("remove", key, file_path)
|
||||
# sys.modules.pop(key)
|
||||
# except Exception: # type:ignore
|
||||
# # print(e)
|
||||
# pass
|
||||
|
||||
def export2onnx(self):
|
||||
modelSlot = self.slotInfo
|
||||
|
||||
if modelSlot.isONNX:
|
||||
logger.warn("[Voice Changer] export2onnx, No pyTorch filepath.")
|
||||
return {"status": "ng", "path": ""}
|
||||
|
||||
if self.pipeline is not None:
|
||||
del self.pipeline
|
||||
self.pipeline = None
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
self.initialize()
|
||||
|
||||
output_file_simple = export2onnx(self.settings.gpu, modelSlot)
|
||||
|
||||
return {
|
||||
"status": "ok",
|
||||
"path": f"/tmp/{output_file_simple}",
|
||||
"filename": output_file_simple,
|
||||
}
|
||||
|
||||
def get_model_current(self):
|
||||
return [
|
||||
{
|
||||
"key": "defaultTune",
|
||||
"val": self.settings.tran,
|
||||
},
|
||||
{
|
||||
"key": "defaultIndexRatio",
|
||||
"val": self.settings.indexRatio,
|
||||
},
|
||||
{
|
||||
"key": "defaultProtect",
|
||||
"val": self.settings.protect,
|
||||
},
|
||||
]
|
||||
17
server/voice_changer/EasyVC/EasyVCModelSlotGenerator.py
Normal file
17
server/voice_changer/EasyVC/EasyVCModelSlotGenerator.py
Normal file
@ -0,0 +1,17 @@
|
||||
import os
|
||||
|
||||
from data.ModelSlot import EasyVCModelSlot
|
||||
from voice_changer.utils.LoadModelParams import LoadModelParams
|
||||
from voice_changer.utils.ModelSlotGenerator import ModelSlotGenerator
|
||||
|
||||
|
||||
class EasyVCModelSlotGenerator(ModelSlotGenerator):
|
||||
@classmethod
|
||||
def loadModel(cls, props: LoadModelParams):
|
||||
slotInfo: EasyVCModelSlot = EasyVCModelSlot()
|
||||
for file in props.files:
|
||||
if file.kind == "easyVCModel":
|
||||
slotInfo.modelFile = file.name
|
||||
slotInfo.name = os.path.splitext(os.path.basename(slotInfo.modelFile))[0]
|
||||
slotInfo.slotIndex = props.slot
|
||||
return slotInfo
|
||||
33
server/voice_changer/EasyVC/EasyVCSettings.py
Normal file
33
server/voice_changer/EasyVC/EasyVCSettings.py
Normal file
@ -0,0 +1,33 @@
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from const import PitchExtractorType
|
||||
|
||||
|
||||
@dataclass
|
||||
class EasyVCSettings:
|
||||
gpu: int = -9999
|
||||
dstId: int = 0
|
||||
|
||||
f0Detector: PitchExtractorType = "rmvpe_onnx" # dio or harvest
|
||||
tran: int = 12
|
||||
silentThreshold: float = 0.00001
|
||||
extraConvertSize: int = 1024 * 4
|
||||
|
||||
indexRatio: float = 0
|
||||
protect: float = 0.5
|
||||
rvcQuality: int = 0
|
||||
silenceFront: int = 1 # 0:off, 1:on
|
||||
modelSamplingRate: int = 48000
|
||||
|
||||
speakers: dict[str, int] = field(default_factory=lambda: {})
|
||||
|
||||
intData = [
|
||||
"gpu",
|
||||
"dstId",
|
||||
"tran",
|
||||
"extraConvertSize",
|
||||
"rvcQuality",
|
||||
"silenceFront",
|
||||
]
|
||||
floatData = ["silentThreshold", "indexRatio", "protect"]
|
||||
strData = ["f0Detector"]
|
||||
237
server/voice_changer/EasyVC/pipeline/Pipeline.py
Normal file
237
server/voice_changer/EasyVC/pipeline/Pipeline.py
Normal file
@ -0,0 +1,237 @@
|
||||
from typing import Any
|
||||
import math
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch.cuda.amp import autocast
|
||||
from Exceptions import (
|
||||
DeviceCannotSupportHalfPrecisionException,
|
||||
DeviceChangingException,
|
||||
HalfPrecisionChangingException,
|
||||
NotEnoughDataExtimateF0,
|
||||
)
|
||||
from mods.log_control import VoiceChangaerLogger
|
||||
|
||||
from voice_changer.RVC.embedder.Embedder import Embedder
|
||||
from voice_changer.RVC.inferencer.Inferencer import Inferencer
|
||||
from voice_changer.RVC.inferencer.OnnxRVCInferencer import OnnxRVCInferencer
|
||||
from voice_changer.RVC.inferencer.OnnxRVCInferencerNono import OnnxRVCInferencerNono
|
||||
|
||||
from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
|
||||
from voice_changer.utils.Timer import Timer2
|
||||
|
||||
logger = VoiceChangaerLogger.get_instance().getLogger()
|
||||
|
||||
|
||||
class Pipeline(object):
|
||||
embedder: Embedder
|
||||
inferencer: Inferencer
|
||||
pitchExtractor: PitchExtractor
|
||||
|
||||
index: Any | None
|
||||
big_npy: Any | None
|
||||
# feature: Any | None
|
||||
|
||||
targetSR: int
|
||||
device: torch.device
|
||||
isHalf: bool
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embedder: Embedder,
|
||||
inferencer: Inferencer,
|
||||
pitchExtractor: PitchExtractor,
|
||||
targetSR,
|
||||
device,
|
||||
isHalf,
|
||||
):
|
||||
self.embedder = embedder
|
||||
self.inferencer = inferencer
|
||||
self.pitchExtractor = pitchExtractor
|
||||
logger.info("GENERATE INFERENCER" + str(self.inferencer))
|
||||
logger.info("GENERATE EMBEDDER" + str(self.embedder))
|
||||
logger.info("GENERATE PITCH EXTRACTOR" + str(self.pitchExtractor))
|
||||
|
||||
self.targetSR = targetSR
|
||||
self.device = device
|
||||
self.isHalf = isHalf
|
||||
|
||||
self.sr = 16000
|
||||
self.window = 160
|
||||
|
||||
def getPipelineInfo(self):
|
||||
inferencerInfo = self.inferencer.getInferencerInfo() if self.inferencer else {}
|
||||
embedderInfo = self.embedder.getEmbedderInfo()
|
||||
pitchExtractorInfo = self.pitchExtractor.getPitchExtractorInfo()
|
||||
return {"inferencer": inferencerInfo, "embedder": embedderInfo, "pitchExtractor": pitchExtractorInfo, "isHalf": self.isHalf}
|
||||
|
||||
def setPitchExtractor(self, pitchExtractor: PitchExtractor):
|
||||
self.pitchExtractor = pitchExtractor
|
||||
|
||||
def extractPitch(self, audio_pad, if_f0, pitchf, f0_up_key, silence_front):
|
||||
try:
|
||||
if if_f0 == 1:
|
||||
pitch, pitchf = self.pitchExtractor.extract(
|
||||
audio_pad,
|
||||
pitchf,
|
||||
f0_up_key,
|
||||
self.sr,
|
||||
self.window,
|
||||
silence_front=silence_front,
|
||||
)
|
||||
# pitch = pitch[:p_len]
|
||||
# pitchf = pitchf[:p_len]
|
||||
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
||||
pitchf = torch.tensor(pitchf, device=self.device, dtype=torch.float).unsqueeze(0)
|
||||
else:
|
||||
pitch = None
|
||||
pitchf = None
|
||||
except IndexError as e: # NOQA
|
||||
print(e)
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
raise NotEnoughDataExtimateF0()
|
||||
return pitch, pitchf
|
||||
|
||||
def extractFeatures(self, feats):
|
||||
with autocast(enabled=self.isHalf):
|
||||
try:
|
||||
feats = self.embedder.extractFeatures(feats)
|
||||
if torch.isnan(feats).all():
|
||||
raise DeviceCannotSupportHalfPrecisionException()
|
||||
return feats
|
||||
except RuntimeError as e:
|
||||
if "HALF" in e.__str__().upper():
|
||||
raise HalfPrecisionChangingException()
|
||||
elif "same device" in e.__str__():
|
||||
raise DeviceChangingException()
|
||||
else:
|
||||
raise e
|
||||
|
||||
def infer(self, feats, p_len, pitch, pitchf, sid, out_size):
|
||||
try:
|
||||
with torch.no_grad():
|
||||
with autocast(enabled=self.isHalf):
|
||||
audio1 = self.inferencer.infer(feats, p_len, pitch, pitchf, sid, out_size)
|
||||
audio1 = (audio1 * 32767.5).data.to(dtype=torch.int16)
|
||||
return audio1
|
||||
except RuntimeError as e:
|
||||
if "HALF" in e.__str__().upper():
|
||||
print("HalfPresicion Error:", e)
|
||||
raise HalfPrecisionChangingException()
|
||||
else:
|
||||
raise e
|
||||
|
||||
def exec(
|
||||
self,
|
||||
sid,
|
||||
audio, # torch.tensor [n]
|
||||
pitchf, # np.array [m]
|
||||
feature, # np.array [m, feat]
|
||||
f0_up_key,
|
||||
index_rate,
|
||||
if_f0,
|
||||
silence_front,
|
||||
repeat,
|
||||
out_size=None,
|
||||
):
|
||||
# print(f"pipeline exec input, audio:{audio.shape}, pitchf:{pitchf.shape}, feature:{feature.shape}")
|
||||
# print(f"pipeline exec input, silence_front:{silence_front}, out_size:{out_size}")
|
||||
|
||||
enablePipelineTimer = True
|
||||
with Timer2("Pipeline-Exec", enablePipelineTimer) as t: # NOQA
|
||||
# 16000のサンプリングレートで入ってきている。以降この世界は16000で処理。
|
||||
# self.t_pad = self.sr * repeat # 1秒
|
||||
# self.t_pad_tgt = self.targetSR * repeat # 1秒 出力時のトリミング(モデルのサンプリングで出力される)
|
||||
audio = audio.unsqueeze(0)
|
||||
|
||||
quality_padding_sec = (repeat * (audio.shape[1] - 1)) / self.sr # padding(reflect)のサイズは元のサイズより小さい必要がある。
|
||||
|
||||
self.t_pad = round(self.sr * quality_padding_sec) # 前後に音声を追加
|
||||
self.t_pad_tgt = round(self.targetSR * quality_padding_sec) # 前後に音声を追加 出力時のトリミング(モデルのサンプリングで出力される)
|
||||
audio_pad = F.pad(audio, (self.t_pad, self.t_pad), mode="reflect").squeeze(0)
|
||||
p_len = audio_pad.shape[0] // self.window
|
||||
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
||||
|
||||
# # RVC QualityがOnのときにはsilence_frontをオフに。
|
||||
# silence_front = silence_front if repeat == 0 else 0
|
||||
# pitchf = pitchf if repeat == 0 else np.zeros(p_len)
|
||||
# out_size = out_size if repeat == 0 else None
|
||||
|
||||
# tensor型調整
|
||||
feats = audio_pad
|
||||
if feats.dim() == 2: # double channels
|
||||
feats = feats.mean(-1)
|
||||
assert feats.dim() == 1, feats.dim()
|
||||
feats = feats.view(1, -1)
|
||||
|
||||
t.record("pre-process")
|
||||
# ピッチ検出
|
||||
pitch, pitchf = self.extractPitch(audio_pad, if_f0, pitchf, f0_up_key, silence_front)
|
||||
t.record("extract-pitch")
|
||||
|
||||
# embedding
|
||||
feats = self.extractFeatures(feats)
|
||||
t.record("extract-feats")
|
||||
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
# if protect < 0.5 and search_index:
|
||||
# feats0 = feats.clone()
|
||||
|
||||
# ピッチサイズ調整
|
||||
p_len = audio_pad.shape[0] // self.window
|
||||
if feats.shape[1] < p_len:
|
||||
p_len = feats.shape[1]
|
||||
if pitch is not None and pitchf is not None:
|
||||
pitch = pitch[:, :p_len]
|
||||
pitchf = pitchf[:, :p_len]
|
||||
|
||||
feats_len = feats.shape[1]
|
||||
if pitch is not None and pitchf is not None:
|
||||
pitch = pitch[:, -feats_len:]
|
||||
pitchf = pitchf[:, -feats_len:]
|
||||
p_len = torch.tensor([feats_len], device=self.device).long()
|
||||
|
||||
# apply silent front for inference
|
||||
if type(self.inferencer) in [OnnxRVCInferencer, OnnxRVCInferencerNono]:
|
||||
npyOffset = math.floor(silence_front * 16000) // 360
|
||||
feats = feats[:, npyOffset * 2 :, :] # NOQA
|
||||
|
||||
feats_len = feats.shape[1]
|
||||
if pitch is not None and pitchf is not None:
|
||||
pitch = pitch[:, -feats_len:]
|
||||
pitchf = pitchf[:, -feats_len:]
|
||||
p_len = torch.tensor([feats_len], device=self.device).long()
|
||||
|
||||
t.record("mid-precess")
|
||||
# 推論実行
|
||||
audio1 = self.infer(feats, p_len, pitch, pitchf, sid, out_size)
|
||||
t.record("infer")
|
||||
|
||||
feats_buffer = feats.squeeze(0).detach().cpu()
|
||||
if pitchf is not None:
|
||||
pitchf_buffer = pitchf.squeeze(0).detach().cpu()
|
||||
else:
|
||||
pitchf_buffer = None
|
||||
|
||||
del p_len, pitch, pitchf, feats
|
||||
# torch.cuda.empty_cache()
|
||||
|
||||
# inferで出力されるサンプリングレートはモデルのサンプリングレートになる。
|
||||
# pipelineに（入力されるときはhubertように16k）
|
||||
if self.t_pad_tgt != 0:
|
||||
offset = self.t_pad_tgt
|
||||
end = -1 * self.t_pad_tgt
|
||||
audio1 = audio1[offset:end]
|
||||
|
||||
del sid
|
||||
t.record("post-process")
|
||||
# torch.cuda.empty_cache()
|
||||
# print("EXEC AVERAGE:", t.avrSecs)
|
||||
return audio1, pitchf_buffer, feats_buffer
|
||||
|
||||
def __del__(self):
|
||||
del self.embedder
|
||||
del self.inferencer
|
||||
del self.pitchExtractor
|
||||
print("Pipeline has been deleted")
|
||||
52
server/voice_changer/EasyVC/pipeline/PipelineGenerator.py
Normal file
52
server/voice_changer/EasyVC/pipeline/PipelineGenerator.py
Normal file
@ -0,0 +1,52 @@
|
||||
import os
|
||||
import traceback
|
||||
from Exceptions import PipelineCreateException
|
||||
from const import EnumInferenceTypes, PitchExtractorType
|
||||
from data.ModelSlot import EasyVCModelSlot
|
||||
from voice_changer.EasyVC.pipeline.Pipeline import Pipeline
|
||||
|
||||
from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
|
||||
from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
|
||||
from voice_changer.RVC.inferencer.InferencerManager import InferencerManager
|
||||
from voice_changer.RVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
|
||||
from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
|
||||
|
||||
|
||||
def createPipeline(params: VoiceChangerParams, modelSlot: EasyVCModelSlot, gpu: int, f0Detector: PitchExtractorType):
|
||||
dev = DeviceManager.get_instance().getDevice(gpu)
|
||||
half = DeviceManager.get_instance().halfPrecisionAvailable(gpu)
|
||||
|
||||
# Inferencer 生成
|
||||
try:
|
||||
modelPath = os.path.join(params.model_dir, str(modelSlot.slotIndex), os.path.basename(modelSlot.modelFile))
|
||||
inferencer = InferencerManager.getInferencer(EnumInferenceTypes.easyVC, modelPath, gpu, modelSlot.version)
|
||||
except Exception as e:
|
||||
print("[Voice Changer] exception! loading inferencer", e)
|
||||
traceback.print_exc()
|
||||
raise PipelineCreateException("[Voice Changer] exception! loading inferencer")
|
||||
|
||||
# Embedder 生成
|
||||
try:
|
||||
embedder = EmbedderManager.getEmbedder(
|
||||
"whisper",
|
||||
half,
|
||||
dev,
|
||||
)
|
||||
except Exception as e:
|
||||
print("[Voice Changer] exception! loading embedder", e, dev)
|
||||
traceback.print_exc()
|
||||
raise PipelineCreateException("[Voice Changer] exception! loading embedder")
|
||||
|
||||
# pitchExtractor
|
||||
pitchExtractor = PitchExtractorManager.getPitchExtractor(f0Detector, gpu)
|
||||
|
||||
pipeline = Pipeline(
|
||||
embedder,
|
||||
inferencer,
|
||||
pitchExtractor,
|
||||
modelSlot.samplingRate,
|
||||
dev,
|
||||
half,
|
||||
)
|
||||
|
||||
return pipeline
|
||||
@ -1,6 +1,6 @@
|
||||
import os
|
||||
|
||||
from data.ModelSlot import BeatriceModelSlot, LLVCModelSlot
|
||||
from data.ModelSlot import LLVCModelSlot
|
||||
from voice_changer.utils.LoadModelParams import LoadModelParams
|
||||
from voice_changer.utils.ModelSlotGenerator import ModelSlotGenerator
|
||||
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
"""
|
||||
VoiceChangerV2向け
|
||||
"""
|
||||
|
||||
from dataclasses import asdict
|
||||
import numpy as np
|
||||
import torch
|
||||
@ -59,13 +60,9 @@ class RVCr2(VoiceChangerModel):
|
||||
|
||||
# pipelineの生成
|
||||
try:
|
||||
self.pipeline = createPipeline(
|
||||
self.params, self.slotInfo, self.settings.gpu, self.settings.f0Detector
|
||||
)
|
||||
self.pipeline = createPipeline(self.params, self.slotInfo, self.settings.gpu, self.settings.f0Detector)
|
||||
except PipelineCreateException as e: # NOQA
|
||||
logger.error(
|
||||
"[Voice Changer] pipeline create failed. check your model is valid."
|
||||
)
|
||||
logger.error("[Voice Changer] pipeline create failed. check your model is valid.")
|
||||
return
|
||||
|
||||
# その他の設定
|
||||
@ -91,9 +88,7 @@ class RVCr2(VoiceChangerModel):
|
||||
elif key in self.settings.strData:
|
||||
setattr(self.settings, key, str(val))
|
||||
if key == "f0Detector" and self.pipeline is not None:
|
||||
pitchExtractor = PitchExtractorManager.getPitchExtractor(
|
||||
self.settings.f0Detector, self.settings.gpu
|
||||
)
|
||||
pitchExtractor = PitchExtractorManager.getPitchExtractor(self.settings.f0Detector, self.settings.gpu)
|
||||
self.pipeline.setPitchExtractor(pitchExtractor)
|
||||
else:
|
||||
return False
|
||||
@ -127,9 +122,7 @@ class RVCr2(VoiceChangerModel):
|
||||
# 過去のデータに連結
|
||||
self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)
|
||||
if self.slotInfo.f0:
|
||||
self.pitchf_buffer = np.concatenate(
|
||||
[self.pitchf_buffer, np.zeros(newFeatureLength)], 0
|
||||
)
|
||||
self.pitchf_buffer = np.concatenate([self.pitchf_buffer, np.zeros(newFeatureLength)], 0)
|
||||
self.feature_buffer = np.concatenate(
|
||||
[
|
||||
self.feature_buffer,
|
||||
@ -141,27 +134,19 @@ class RVCr2(VoiceChangerModel):
|
||||
self.audio_buffer = newData
|
||||
if self.slotInfo.f0:
|
||||
self.pitchf_buffer = np.zeros(newFeatureLength)
|
||||
self.feature_buffer = np.zeros(
|
||||
[newFeatureLength, self.slotInfo.embChannels]
|
||||
)
|
||||
self.feature_buffer = np.zeros([newFeatureLength, self.slotInfo.embChannels])
|
||||
|
||||
convertSize = inputSize + crossfadeSize + solaSearchFrame + extra_frame
|
||||
|
||||
if convertSize % 160 != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
|
||||
convertSize = convertSize + (160 - (convertSize % 160))
|
||||
outSize = int(
|
||||
((convertSize - extra_frame) / 16000) * self.slotInfo.samplingRate
|
||||
)
|
||||
outSize = int(((convertSize - extra_frame) / 16000) * self.slotInfo.samplingRate)
|
||||
|
||||
# バッファがたまっていない場合はzeroで補う
|
||||
if self.audio_buffer.shape[0] < convertSize:
|
||||
self.audio_buffer = np.concatenate(
|
||||
[np.zeros([convertSize]), self.audio_buffer]
|
||||
)
|
||||
self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer])
|
||||
if self.slotInfo.f0:
|
||||
self.pitchf_buffer = np.concatenate(
|
||||
[np.zeros([convertSize // 160]), self.pitchf_buffer]
|
||||
)
|
||||
self.pitchf_buffer = np.concatenate([np.zeros([convertSize // 160]), self.pitchf_buffer])
|
||||
self.feature_buffer = np.concatenate(
|
||||
[
|
||||
np.zeros([convertSize // 160, self.slotInfo.embChannels]),
|
||||
@ -194,9 +179,7 @@ class RVCr2(VoiceChangerModel):
|
||||
outSize,
|
||||
)
|
||||
|
||||
def inference(
|
||||
self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int
|
||||
):
|
||||
def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int):
|
||||
if self.pipeline is None:
|
||||
logger.info("[Voice Changer] Pipeline is not initialized.")
|
||||
raise PipelineNotInitializedException()
|
||||
@ -208,18 +191,15 @@ class RVCr2(VoiceChangerModel):
|
||||
receivedData,
|
||||
self.inputSampleRate,
|
||||
16000,
|
||||
filter="kaiser_fast",
|
||||
),
|
||||
)
|
||||
crossfade_frame = int((crossfade_frame / self.inputSampleRate) * 16000)
|
||||
sola_search_frame = int((sola_search_frame / self.inputSampleRate) * 16000)
|
||||
extra_frame = int(
|
||||
(self.settings.extraConvertSize / self.inputSampleRate) * 16000
|
||||
)
|
||||
extra_frame = int((self.settings.extraConvertSize / self.inputSampleRate) * 16000)
|
||||
|
||||
# 入力データ生成
|
||||
data = self.generate_input(
|
||||
receivedData, crossfade_frame, sola_search_frame, extra_frame
|
||||
)
|
||||
data = self.generate_input(receivedData, crossfade_frame, sola_search_frame, extra_frame)
|
||||
|
||||
audio = data[0]
|
||||
pitchf = data[1]
|
||||
@ -254,9 +234,7 @@ class RVCr2(VoiceChangerModel):
|
||||
index_rate,
|
||||
if_f0,
|
||||
# 0,
|
||||
self.settings.extraConvertSize / self.inputSampleRate
|
||||
if self.settings.silenceFront
|
||||
else 0.0, # extaraDataSizeの秒数。入力のサンプリングレートで算出
|
||||
self.settings.extraConvertSize / self.inputSampleRate if self.settings.silenceFront else 0.0, # extaraDataSizeの秒数。入力のサンプリングレートで算出
|
||||
embOutputLayer,
|
||||
useFinalProj,
|
||||
repeat,
|
||||
@ -272,14 +250,13 @@ class RVCr2(VoiceChangerModel):
|
||||
result,
|
||||
self.slotInfo.samplingRate,
|
||||
self.outputSampleRate,
|
||||
filter="kaiser_fast",
|
||||
),
|
||||
)
|
||||
|
||||
return result
|
||||
except DeviceCannotSupportHalfPrecisionException as e: # NOQA
|
||||
logger.warn(
|
||||
"[Device Manager] Device cannot support half precision. Fallback to float...."
|
||||
)
|
||||
logger.warn("[Device Manager] Device cannot support half precision. Fallback to float....")
|
||||
self.deviceManager.setForceTensor(True)
|
||||
self.initialize()
|
||||
# raise e
|
||||
|
||||
@ -15,14 +15,6 @@ class Embedder(EmbedderProtocol):
|
||||
|
||||
self.model: Any | None = None
|
||||
|
||||
def loadModel(self, file: str, dev: device, isHalf: bool = True):
|
||||
...
|
||||
|
||||
def extractFeatures(
|
||||
self, feats: torch.Tensor, embOutputLayer=9, useFinalProj=True
|
||||
) -> torch.Tensor:
|
||||
...
|
||||
|
||||
def getEmbedderInfo(self):
|
||||
return {
|
||||
"embedderType": self.embedderType,
|
||||
|
||||
@ -6,6 +6,7 @@ from voice_changer.RVC.embedder.FairseqContentvec import FairseqContentvec
|
||||
from voice_changer.RVC.embedder.FairseqHubert import FairseqHubert
|
||||
from voice_changer.RVC.embedder.FairseqHubertJp import FairseqHubertJp
|
||||
from voice_changer.RVC.embedder.OnnxContentvec import OnnxContentvec
|
||||
from voice_changer.RVC.embedder.Whisper import Whisper
|
||||
from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
|
||||
|
||||
|
||||
@ -18,9 +19,7 @@ class EmbedderManager:
|
||||
cls.params = params
|
||||
|
||||
@classmethod
|
||||
def getEmbedder(
|
||||
cls, embederType: EmbedderType, isHalf: bool, dev: device
|
||||
) -> Embedder:
|
||||
def getEmbedder(cls, embederType: EmbedderType, isHalf: bool, dev: device) -> Embedder:
|
||||
if cls.currentEmbedder is None:
|
||||
print("[Voice Changer] generate new embedder. (no embedder)")
|
||||
cls.currentEmbedder = cls.loadEmbedder(embederType, isHalf, dev)
|
||||
@ -36,9 +35,7 @@ class EmbedderManager:
|
||||
return cls.currentEmbedder
|
||||
|
||||
@classmethod
|
||||
def loadEmbedder(
|
||||
cls, embederType: EmbedderType, isHalf: bool, dev: device
|
||||
) -> Embedder:
|
||||
def loadEmbedder(cls, embederType: EmbedderType, isHalf: bool, dev: device) -> Embedder:
|
||||
if embederType == "hubert_base":
|
||||
try:
|
||||
if cls.params.content_vec_500_onnx_on is False:
|
||||
@ -62,5 +59,8 @@ class EmbedderManager:
|
||||
print(e)
|
||||
file = cls.params.hubert_base
|
||||
return FairseqContentvec().loadModel(file, dev, isHalf)
|
||||
elif embederType == "whisper":
|
||||
file = cls.params.whisper_tiny
|
||||
return Whisper().loadModel(file, dev, isHalf)
|
||||
else:
|
||||
return FairseqHubert().loadModel(file, dev, isHalf)
|
||||
|
||||
53
server/voice_changer/RVC/embedder/Whisper.py
Normal file
53
server/voice_changer/RVC/embedder/Whisper.py
Normal file
@ -0,0 +1,53 @@
|
||||
import torch
|
||||
from torch import device
|
||||
from voice_changer.RVC.embedder.Embedder import Embedder
|
||||
|
||||
from voice_changer.RVC.embedder.whisper.audio import log_mel_spectrogram
|
||||
from .whisper.whisper import load_model
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Whisper(Embedder):
|
||||
def loadModel(self, file: str, dev: device, isHalf: bool = True) -> Embedder:
|
||||
super().setProps("whisper", file, dev, isHalf)
|
||||
|
||||
whisper = load_model(file).to(dev)
|
||||
|
||||
self.model = whisper
|
||||
return self
|
||||
|
||||
def extractFeatures(self, audio: torch.Tensor) -> torch.Tensor:
|
||||
try:
|
||||
if isinstance(audio, np.ndarray):
|
||||
audio = torch.from_numpy(audio.astype(np.float32))
|
||||
audio = audio.to(self.dev)
|
||||
# if self.isHalf and audio.dtype != torch.float16:
|
||||
# audio = audio.half()
|
||||
if self.isHalf is False and audio.dtype != torch.float32:
|
||||
audio = audio.float()
|
||||
|
||||
if audio.dim() != 1:
|
||||
audio = audio.squeeze(0)
|
||||
|
||||
if audio.dim() != 1:
|
||||
raise RuntimeError(f"Exeption in {self.__class__.__name__} audio.dim is not 1 (size :{audio.dim()}, {audio.shape})")
|
||||
|
||||
audln = audio.shape[0]
|
||||
ppgln = audln // 320
|
||||
|
||||
mel = log_mel_spectrogram(audio).to(self.model.device)
|
||||
|
||||
# print(f"[whisper_ppg] audio:{audio.shape}({audio.shape[0]/16000}ms) -> ppg:{ppgln}")
|
||||
# print(f"[whisper_ppg] mel:{mel.shape}({mel.dtype})")
|
||||
with torch.no_grad():
|
||||
ppg = self.model.encoder(mel.unsqueeze(0))
|
||||
padding = (0, 384)
|
||||
ppg_padded = F.pad(ppg, padding, "constant", 0)
|
||||
ppg_padded = ppg_padded.data
|
||||
# print(f"[whisper_ppg] ppg:{ppg.shape}")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
raise RuntimeError(f"Exeption in {self.__class__.__name__}", e)
|
||||
# raise EmbedderProcessException(f"Exeption in {self.__class__.__name__}", e)
|
||||
return ppg_padded
|
||||
BIN
server/voice_changer/RVC/embedder/whisper/assets/mel_filters.npz
Normal file
BIN
server/voice_changer/RVC/embedder/whisper/assets/mel_filters.npz
Normal file
Binary file not shown.
120
server/voice_changer/RVC/embedder/whisper/audio.py
Normal file
120
server/voice_changer/RVC/embedder/whisper/audio.py
Normal file
@ -0,0 +1,120 @@
|
||||
import os
|
||||
from functools import lru_cache
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from voice_changer.RVC.embedder.whisper.utils import exact_div
|
||||
|
||||
|
||||
# hard-coded audio hyperparameters
|
||||
SAMPLE_RATE = 16000
|
||||
N_FFT = 400
|
||||
N_MELS = 80
|
||||
HOP_LENGTH = 160
|
||||
CHUNK_LENGTH = 30
|
||||
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
|
||||
N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
|
||||
|
||||
|
||||
# def load_audio(file: str, sr: int = SAMPLE_RATE):
|
||||
# """
|
||||
# Open an audio file and read as mono waveform, resampling as necessary
|
||||
|
||||
# Parameters
|
||||
# ----------
|
||||
# file: str
|
||||
# The audio file to open
|
||||
|
||||
# sr: int
|
||||
# The sample rate to resample the audio if necessary
|
||||
|
||||
# Returns
|
||||
# -------
|
||||
# A NumPy array containing the audio waveform, in float32 dtype.
|
||||
# """
|
||||
# try:
|
||||
# # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
|
||||
# # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
|
||||
# out, _ = ffmpeg.input(file, threads=0).output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr).run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
|
||||
# except ffmpeg.Error as e:
|
||||
# raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
|
||||
|
||||
# return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
|
||||
|
||||
|
||||
def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
|
||||
"""
|
||||
Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
|
||||
"""
|
||||
if torch.is_tensor(array):
|
||||
if array.shape[axis] > length:
|
||||
array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
|
||||
|
||||
if array.shape[axis] < length:
|
||||
pad_widths = [(0, 0)] * array.ndim
|
||||
pad_widths[axis] = (0, length - array.shape[axis])
|
||||
array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
|
||||
else:
|
||||
if array.shape[axis] > length:
|
||||
array = array.take(indices=range(length), axis=axis)
|
||||
|
||||
if array.shape[axis] < length:
|
||||
pad_widths = [(0, 0)] * array.ndim
|
||||
pad_widths[axis] = (0, length - array.shape[axis])
|
||||
array = np.pad(array, pad_widths)
|
||||
|
||||
return array
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
|
||||
"""
|
||||
load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
|
||||
Allows decoupling librosa dependency; saved using:
|
||||
|
||||
np.savez_compressed(
|
||||
"mel_filters.npz",
|
||||
mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
|
||||
)
|
||||
"""
|
||||
assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
|
||||
with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
|
||||
return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
|
||||
|
||||
|
||||
def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
|
||||
"""
|
||||
Compute the log-Mel spectrogram of
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
|
||||
The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
|
||||
|
||||
n_mels: int
|
||||
The number of Mel-frequency filters, only 80 is supported
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor, shape = (80, n_frames)
|
||||
A Tensor that contains the Mel spectrogram
|
||||
"""
|
||||
if not torch.is_tensor(audio):
|
||||
if isinstance(audio, str):
|
||||
audio = load_audio(audio)
|
||||
audio = torch.from_numpy(audio)
|
||||
|
||||
window = torch.hann_window(N_FFT).to(audio.device) # type: ignore
|
||||
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) # type: ignore
|
||||
magnitudes = stft[..., :-1].abs() ** 2
|
||||
|
||||
filters = mel_filters(audio.device, n_mels) # type: ignore
|
||||
mel_spec = filters @ magnitudes
|
||||
|
||||
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
|
||||
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
|
||||
log_spec = (log_spec + 4.0) / 4.0
|
||||
return log_spec
|
||||
222
server/voice_changer/RVC/embedder/whisper/model.py
Normal file
222
server/voice_changer/RVC/embedder/whisper/model.py
Normal file
@ -0,0 +1,222 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict
|
||||
from typing import Iterable, Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor
|
||||
from torch import nn
|
||||
|
||||
|
||||
# from .decoding import detect_language as detect_language_function, decode as decode_function
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelDimensions:
|
||||
n_mels: int
|
||||
n_audio_ctx: int
|
||||
n_audio_state: int
|
||||
n_audio_head: int
|
||||
n_audio_layer: int
|
||||
n_vocab: int
|
||||
n_text_ctx: int
|
||||
n_text_state: int
|
||||
n_text_head: int
|
||||
n_text_layer: int
|
||||
|
||||
|
||||
class LayerNorm(nn.LayerNorm):
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return super().forward(x.float()).type(x.dtype)
|
||||
|
||||
|
||||
class Linear(nn.Linear):
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return F.linear(x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype))
|
||||
|
||||
|
||||
class Conv1d(nn.Conv1d):
|
||||
def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
|
||||
return super()._conv_forward(x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype))
|
||||
|
||||
|
||||
def sinusoids(length, channels, max_timescale=10000):
|
||||
"""Returns sinusoids for positional embedding"""
|
||||
assert channels % 2 == 0
|
||||
log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
|
||||
inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
|
||||
scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
|
||||
return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(self, n_state: int, n_head: int):
|
||||
super().__init__()
|
||||
self.n_head = n_head
|
||||
self.query = Linear(n_state, n_state)
|
||||
self.key = Linear(n_state, n_state, bias=False)
|
||||
self.value = Linear(n_state, n_state)
|
||||
self.out = Linear(n_state, n_state)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
xa: Optional[Tensor] = None,
|
||||
mask: Optional[Tensor] = None,
|
||||
kv_cache: Optional[dict] = None,
|
||||
):
|
||||
q = self.query(x)
|
||||
|
||||
if kv_cache is None or xa is None or self.key not in kv_cache:
|
||||
# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
|
||||
# otherwise, perform key/value projections for self- or cross-attention as usual.
|
||||
k = self.key(x if xa is None else xa)
|
||||
v = self.value(x if xa is None else xa)
|
||||
else:
|
||||
# for cross-attention, calculate keys and values once and reuse in subsequent calls.
|
||||
k = kv_cache[self.key]
|
||||
v = kv_cache[self.value]
|
||||
|
||||
wv, qk = self.qkv_attention(q, k, v, mask)
|
||||
return self.out(wv), qk
|
||||
|
||||
def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
|
||||
n_batch, n_ctx, n_state = q.shape
|
||||
scale = (n_state // self.n_head) ** -0.25
|
||||
q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
|
||||
k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
|
||||
v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
||||
|
||||
qk = q @ k
|
||||
if mask is not None:
|
||||
qk = qk + mask[:n_ctx, :n_ctx]
|
||||
qk = qk.float()
|
||||
|
||||
w = F.softmax(qk, dim=-1).to(q.dtype)
|
||||
return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
|
||||
|
||||
|
||||
class ResidualAttentionBlock(nn.Module):
|
||||
def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
|
||||
super().__init__()
|
||||
|
||||
self.attn = MultiHeadAttention(n_state, n_head)
|
||||
self.attn_ln = LayerNorm(n_state)
|
||||
|
||||
self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
|
||||
self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
|
||||
|
||||
n_mlp = n_state * 4
|
||||
self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
|
||||
self.mlp_ln = LayerNorm(n_state)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
xa: Optional[Tensor] = None,
|
||||
mask: Optional[Tensor] = None,
|
||||
kv_cache: Optional[dict] = None,
|
||||
):
|
||||
x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
|
||||
if self.cross_attn and self.cross_attn_ln:
|
||||
x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
|
||||
x = x + self.mlp(self.mlp_ln(x))
|
||||
return x
|
||||
|
||||
|
||||
class AudioEncoder(nn.Module):
|
||||
def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
|
||||
super().__init__()
|
||||
self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
|
||||
self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
|
||||
self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
|
||||
|
||||
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList([ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)])
|
||||
self.ln_post = LayerNorm(n_state)
|
||||
|
||||
def forward(self, x: Tensor):
|
||||
"""
|
||||
x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
|
||||
the mel spectrogram of the audio
|
||||
"""
|
||||
x = F.gelu(self.conv1(x))
|
||||
x = F.gelu(self.conv2(x))
|
||||
x = x.permute(0, 2, 1)
|
||||
x = (x[:, :, :] + self.positional_embedding[: x.shape[1], :]).to(x.dtype)
|
||||
|
||||
for j, block in enumerate(self.blocks):
|
||||
x = block(x)
|
||||
|
||||
x = self.ln_post(x)
|
||||
return x
|
||||
|
||||
|
||||
class TextDecoder(nn.Module):
|
||||
def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
|
||||
super().__init__()
|
||||
|
||||
self.token_embedding = nn.Embedding(n_vocab, n_state)
|
||||
self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
|
||||
|
||||
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList([ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)])
|
||||
self.ln = LayerNorm(n_state)
|
||||
|
||||
mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
|
||||
self.register_buffer("mask", mask, persistent=False)
|
||||
|
||||
def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
|
||||
"""
|
||||
x : torch.LongTensor, shape = (batch_size, <= n_ctx)
|
||||
the text tokens
|
||||
xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
|
||||
the encoded audio features to be attended on
|
||||
"""
|
||||
offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
|
||||
x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
|
||||
x = x.to(xa.dtype)
|
||||
|
||||
for block in self.blocks:
|
||||
x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
|
||||
|
||||
x = self.ln(x)
|
||||
logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
|
||||
|
||||
return logits
|
||||
|
||||
|
||||
class Whisper(nn.Module):
|
||||
def __init__(self, dims: ModelDimensions):
|
||||
super().__init__()
|
||||
self.dims = dims
|
||||
self.encoder = AudioEncoder(
|
||||
self.dims.n_mels,
|
||||
self.dims.n_audio_ctx,
|
||||
self.dims.n_audio_state,
|
||||
self.dims.n_audio_head,
|
||||
self.dims.n_audio_layer,
|
||||
)
|
||||
self.decoder = TextDecoder(
|
||||
self.dims.n_vocab,
|
||||
self.dims.n_text_ctx,
|
||||
self.dims.n_text_state,
|
||||
self.dims.n_text_head,
|
||||
self.dims.n_text_layer,
|
||||
)
|
||||
|
||||
def embed_audio(self, mel: torch.Tensor):
|
||||
return self.encoder(mel)
|
||||
|
||||
def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
|
||||
return self.decoder(tokens, audio_features)
|
||||
|
||||
def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
|
||||
return self.decoder(tokens, self.encoder(mel))
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
@property
|
||||
def is_multilingual(self):
|
||||
return self.dims.n_vocab == 51865
|
||||
22
server/voice_changer/RVC/embedder/whisper/utils.py
Normal file
22
server/voice_changer/RVC/embedder/whisper/utils.py
Normal file
@ -0,0 +1,22 @@
|
||||
import sys
|
||||
|
||||
|
||||
system_encoding = sys.getdefaultencoding()
|
||||
|
||||
if system_encoding != "utf-8":
|
||||
|
||||
def make_safe(string):
|
||||
# replaces any character not representable using the system default encoding with an '?',
|
||||
# avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729).
|
||||
return string.encode(system_encoding, errors="replace").decode(system_encoding)
|
||||
|
||||
else:
|
||||
|
||||
def make_safe(string):
|
||||
# utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
|
||||
return string
|
||||
|
||||
|
||||
def exact_div(x, y):
|
||||
assert x % y == 0
|
||||
return x // y
|
||||
208
server/voice_changer/RVC/embedder/whisper/whisper.py
Normal file
208
server/voice_changer/RVC/embedder/whisper/whisper.py
Normal file
@ -0,0 +1,208 @@
|
||||
# from whisper_ppg.model import Whisper, ModelDimensions
|
||||
|
||||
# from whisper_ppg_custom._LightWhisper import LightWhisper
|
||||
# from whisper_ppg_custom.Timer import Timer2
|
||||
# from whisper_ppg_custom.whisper_ppg.audio import load_audio, pad_or_trim, log_mel_spectrogram
|
||||
# from whisper_ppg_custom.whisper_ppg.model import Whisper, ModelDimensions
|
||||
import torch
|
||||
|
||||
# import numpy as np
|
||||
# from easy_vc_dev.utils.whisper.audio import load_audio, pad_or_trim
|
||||
from .model import ModelDimensions, Whisper
|
||||
|
||||
# import onnx
|
||||
|
||||
# from onnxsim import simplify
|
||||
# import json
|
||||
|
||||
# import onnxruntime
|
||||
|
||||
|
||||
def load_model(path) -> Whisper:
|
||||
device = "cpu"
|
||||
checkpoint = torch.load(path, map_location=device)
|
||||
dims = ModelDimensions(**checkpoint["dims"])
|
||||
model = Whisper(dims)
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
model = model.to(device)
|
||||
return model
|
||||
|
||||
|
||||
# def pred_ppg(whisper: Whisper, wavPath: str, ppgPath: str):
|
||||
# print("pred")
|
||||
# # whisper = load_model("base.pt") # "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt"
|
||||
# audio = load_audio(wavPath)
|
||||
# audln = audio.shape[0]
|
||||
# ppgln = audln // 320
|
||||
# print("audio.shape1", audio.shape, audio.shape[0] / 16000)
|
||||
# audio = pad_or_trim(audio)
|
||||
# audio = audio[:400000]
|
||||
# print("audio.shape2", audio.shape)
|
||||
# print(f"whisper.device {whisper.device}")
|
||||
# for i in range(5):
|
||||
# with Timer2("mainPorcess timer", True) as t:
|
||||
# mel = log_mel_spectrogram(audio).to(whisper.device)
|
||||
# with torch.no_grad():
|
||||
# ppg = whisper.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy()
|
||||
# print("ppg.shape", ppg.shape)
|
||||
# ppg = ppg[:ppgln,]
|
||||
# print(ppg.shape)
|
||||
# np.save(ppgPath, ppg, allow_pickle=False)
|
||||
# t.record("fin")
|
||||
# print("res", ppg)
|
||||
|
||||
|
||||
# def pred_ppg_onnx(wavPath, ppgPath):
|
||||
# print("pred")
|
||||
# # whisper = load_model("base.pt") # "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt"
|
||||
# whisper = load_model("tiny.pt")
|
||||
# audio = load_audio(wavPath)
|
||||
# # audln = audio.shape[0]
|
||||
# # ppgln = audln // 320
|
||||
# print("audio.shape1", audio.shape, audio.shape[0] / 16000)
|
||||
# audio = pad_or_trim(audio)
|
||||
# audio = audio[:1000]
|
||||
# print("audio.shape2", audio.shape)
|
||||
# print(f"whisper.device {whisper.device}")
|
||||
# onnx_session = onnxruntime.InferenceSession(
|
||||
# "wencoder_sim.onnx",
|
||||
# providers=["CPUExecutionProvider"],
|
||||
# provider_options=[
|
||||
# {
|
||||
# "intra_op_num_threads": 8,
|
||||
# "execution_mode": onnxruntime.ExecutionMode.ORT_PARALLEL,
|
||||
# "inter_op_num_threads": 8,
|
||||
# }
|
||||
# ],
|
||||
# )
|
||||
|
||||
# for i in range(5):
|
||||
# with Timer2("mainPorcess timer", True) as t:
|
||||
# mel = log_mel_spectrogram(audio).to(whisper.device).unsqueeze(0)
|
||||
# onnx_res = onnx_session.run(
|
||||
# ["ppg"],
|
||||
# {
|
||||
# "mel": mel.cpu().numpy(),
|
||||
# },
|
||||
# )
|
||||
# t.record("fin")
|
||||
# print("onnx_res", onnx_res)
|
||||
|
||||
|
||||
# def export_encoder(wavPath, ppgPath):
|
||||
# print("pred")
|
||||
# # whisper = load_model("base.pt") # "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt"
|
||||
# whisper = load_model("tiny.pt")
|
||||
# audio = load_audio(wavPath)
|
||||
# # audln = audio.shape[0]
|
||||
# # ppgln = audln // 320
|
||||
# print("audio.shape1", audio.shape, audio.shape[0] / 16000)
|
||||
# audio = pad_or_trim(audio)
|
||||
# print("audio.shape2", audio.shape)
|
||||
# print(f"whisper.device {whisper.device}")
|
||||
|
||||
# mel = log_mel_spectrogram(audio).to(whisper.device).unsqueeze(0)
|
||||
# input_names = ["mel"]
|
||||
# output_names = ["ppg"]
|
||||
|
||||
# torch.onnx.export(
|
||||
# whisper.encoder,
|
||||
# (mel,),
|
||||
# "wencoder.onnx",
|
||||
# dynamic_axes={
|
||||
# "mel": [2],
|
||||
# },
|
||||
# do_constant_folding=False,
|
||||
# opset_version=17,
|
||||
# verbose=False,
|
||||
# input_names=input_names,
|
||||
# output_names=output_names,
|
||||
# )
|
||||
|
||||
# metadata = {
|
||||
# "application": "VC_CLIENT",
|
||||
# "version": "2.1",
|
||||
# }
|
||||
# model_onnx2 = onnx.load("wencoder.onnx")
|
||||
# model_simp, check = simplify(model_onnx2)
|
||||
# meta = model_simp.metadata_props.add()
|
||||
# meta.key = "metadata"
|
||||
# meta.value = json.dumps(metadata)
|
||||
# onnx.save(model_simp, "wencoder_sim.onnx")
|
||||
|
||||
|
||||
# def pred_ppg_onnx_w(wavPath, ppgPath):
|
||||
# print("pred")
|
||||
# audio = load_audio(wavPath)
|
||||
# print("audio.shape1", audio.shape, audio.shape[0] / 16000)
|
||||
# audio = pad_or_trim(audio)
|
||||
# print("audio.shape2", audio.shape)
|
||||
# onnx_session = onnxruntime.InferenceSession(
|
||||
# "wencoder_sim.onnx",
|
||||
# providers=["CPUExecutionProvider"],
|
||||
# provider_options=[
|
||||
# {
|
||||
# "intra_op_num_threads": 8,
|
||||
# "execution_mode": onnxruntime.ExecutionMode.ORT_PARALLEL,
|
||||
# "inter_op_num_threads": 8,
|
||||
# }
|
||||
# ],
|
||||
# )
|
||||
|
||||
# for i in range(5):
|
||||
# with Timer2("mainPorcess timer", True) as t:
|
||||
# mel = log_mel_spectrogram(audio).to("cpu").unsqueeze(0)
|
||||
# # mel = mel[:, :, 1500:]
|
||||
# mel = mel[:, :, 2500:]
|
||||
# # mel[0, 79, 1499] = 0.1
|
||||
|
||||
# print("x.shape", mel.shape)
|
||||
# onnx_res = onnx_session.run(
|
||||
# ["ppg"],
|
||||
# {
|
||||
# "mel": mel.cpu().numpy(),
|
||||
# },
|
||||
# )
|
||||
# t.record("fin")
|
||||
# print("onnx_res", onnx_res)
|
||||
|
||||
|
||||
# def export_wrapped_encoder(wavPath, ppgPath):
|
||||
# print("pred")
|
||||
# whisper = LightWhisper("tiny.pt")
|
||||
# audio = load_audio(wavPath)
|
||||
# # audln = audio.shape[0]
|
||||
# # ppgln = audln // 320
|
||||
# print("audio.shape1", audio.shape, audio.shape[0] / 16000)
|
||||
# audio = pad_or_trim(audio)
|
||||
# print("audio.shape2", audio.shape)
|
||||
|
||||
# mel = log_mel_spectrogram(audio).to("cpu").unsqueeze(0)
|
||||
# mel = mel[:, :, 1500:]
|
||||
# input_names = ["mel"]
|
||||
# output_names = ["ppg"]
|
||||
|
||||
# torch.onnx.export(
|
||||
# whisper,
|
||||
# (mel,),
|
||||
# "wencoder.onnx",
|
||||
# dynamic_axes={
|
||||
# "mel": [2],
|
||||
# },
|
||||
# do_constant_folding=True,
|
||||
# opset_version=17,
|
||||
# verbose=False,
|
||||
# input_names=input_names,
|
||||
# output_names=output_names,
|
||||
# )
|
||||
|
||||
# metadata = {
|
||||
# "application": "VC_CLIENT",
|
||||
# "version": "2.1",
|
||||
# }
|
||||
# model_onnx2 = onnx.load("wencoder.onnx")
|
||||
# model_simp, check = simplify(model_onnx2)
|
||||
# meta = model_simp.metadata_props.add()
|
||||
# meta.key = "metadata"
|
||||
# meta.value = json.dumps(metadata)
|
||||
# onnx.save(model_simp, "wencoder_sim.onnx")
|
||||
46
server/voice_changer/RVC/inferencer/EasyVCInferencerONNX.py
Normal file
46
server/voice_changer/RVC/inferencer/EasyVCInferencerONNX.py
Normal file
@ -0,0 +1,46 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from const import EnumInferenceTypes
|
||||
|
||||
from voice_changer.RVC.inferencer.OnnxRVCInferencer import OnnxRVCInferencer
|
||||
|
||||
|
||||
class EasyVCInferencerONNX(OnnxRVCInferencer):
|
||||
def loadModel(self, file: str, gpu: int, inferencerTypeVersion: str | None = None):
|
||||
super().loadModel(file, gpu, inferencerTypeVersion)
|
||||
self.setProps(EnumInferenceTypes.easyVC, file, self.isHalf, gpu)
|
||||
return self
|
||||
|
||||
def infer(
|
||||
self,
|
||||
feats: torch.Tensor,
|
||||
pitch_length: torch.Tensor,
|
||||
pitch: torch.Tensor | None,
|
||||
pitchf: torch.Tensor | None,
|
||||
sid: torch.Tensor,
|
||||
convert_length: int | None,
|
||||
) -> torch.Tensor:
|
||||
if self.isHalf:
|
||||
audio1 = self.model.run(
|
||||
["audio"],
|
||||
{
|
||||
"feats": feats.cpu().numpy().astype(np.float16),
|
||||
"p_len": pitch_length.cpu().numpy().astype(np.int64),
|
||||
},
|
||||
)
|
||||
else:
|
||||
audio1 = self.model.run(
|
||||
["audio"],
|
||||
{
|
||||
"feats": feats.cpu().numpy().astype(np.float32),
|
||||
"p_len": pitch_length.cpu().numpy().astype(np.int64),
|
||||
},
|
||||
)
|
||||
res = audio1[0][0][0]
|
||||
|
||||
# if self.inferencerTypeVersion == "v2.1" or self.inferencerTypeVersion == "v1.1":
|
||||
# res = audio1[0]
|
||||
# else:
|
||||
# res = np.array(audio1)[0][0, 0]
|
||||
# res = np.clip(res, -1.0, 1.0)
|
||||
return torch.tensor(res)
|
||||
@ -1,4 +1,5 @@
|
||||
from const import EnumInferenceTypes
|
||||
from voice_changer.RVC.inferencer.EasyVCInferencerONNX import EasyVCInferencerONNX
|
||||
from voice_changer.RVC.inferencer.Inferencer import Inferencer
|
||||
from voice_changer.RVC.inferencer.OnnxRVCInferencer import OnnxRVCInferencer
|
||||
from voice_changer.RVC.inferencer.OnnxRVCInferencerNono import OnnxRVCInferencerNono
|
||||
@ -42,6 +43,7 @@ class InferencerManager:
|
||||
elif inferencerType == EnumInferenceTypes.pyTorchVoRASbeta or inferencerType == EnumInferenceTypes.pyTorchVoRASbeta.value:
|
||||
if sys.platform.startswith("darwin") is False:
|
||||
from voice_changer.RVC.inferencer.VorasInferencebeta import VoRASInferencer
|
||||
|
||||
return VoRASInferencer().loadModel(file, gpu)
|
||||
else:
|
||||
raise RuntimeError("[Voice Changer] VoRAS is not supported on macOS")
|
||||
@ -55,5 +57,8 @@ class InferencerManager:
|
||||
return OnnxRVCInferencer().loadModel(file, gpu, inferencerTypeVersion)
|
||||
elif inferencerType == EnumInferenceTypes.onnxRVCNono or inferencerType == EnumInferenceTypes.onnxRVCNono.value:
|
||||
return OnnxRVCInferencerNono().loadModel(file, gpu, inferencerTypeVersion)
|
||||
elif inferencerType == EnumInferenceTypes.easyVC or inferencerType == EnumInferenceTypes.easyVC.value:
|
||||
return EasyVCInferencerONNX().loadModel(file, gpu)
|
||||
|
||||
else:
|
||||
raise RuntimeError("[Voice Changer] Inferencer not found", inferencerType)
|
||||
|
||||
@ -206,6 +206,12 @@ class VoiceChangerManager(ServerDeviceCallbacks):
|
||||
slotInfo = LLVCModelSlotGenerator.loadModel(params)
|
||||
self.modelSlotManager.save_model_slot(params.slot, slotInfo)
|
||||
|
||||
elif params.voiceChangerType == "EasyVC":
|
||||
from voice_changer.EasyVC.EasyVCModelSlotGenerator import EasyVCModelSlotGenerator
|
||||
|
||||
slotInfo = EasyVCModelSlotGenerator.loadModel(params)
|
||||
self.modelSlotManager.save_model_slot(params.slot, slotInfo)
|
||||
|
||||
logger.info(f"params, {params}")
|
||||
|
||||
def get_info(self):
|
||||
@ -307,6 +313,15 @@ class VoiceChangerManager(ServerDeviceCallbacks):
|
||||
self.voiceChanger.setModel(self.voiceChangerModel)
|
||||
pass
|
||||
|
||||
elif slotInfo.voiceChangerType == "EasyVC":
|
||||
logger.info("................EasyVC")
|
||||
from voice_changer.EasyVC.EasyVC import EasyVC
|
||||
|
||||
self.voiceChangerModel = EasyVC(self.params, slotInfo)
|
||||
self.voiceChanger = VoiceChangerV2(self.params)
|
||||
self.voiceChanger.setModel(self.voiceChangerModel)
|
||||
pass
|
||||
|
||||
else:
|
||||
logger.info(f"[Voice Changer] unknown voice changer model: {slotInfo.voiceChangerType}")
|
||||
if hasattr(self, "voiceChangerModel"):
|
||||
|
||||
@ -216,8 +216,8 @@ class VoiceChangerV2(VoiceChangerIF):
|
||||
try:
|
||||
if self.voiceChanger is None:
|
||||
raise VoiceChangerIsNotSelectedException("Voice Changer is not selected.")
|
||||
|
||||
with Timer2("main-process", False) as t:
|
||||
enableMainprocessTimer = False
|
||||
with Timer2("main-process", enableMainprocessTimer) as t:
|
||||
processing_sampling_rate = self.voiceChanger.get_processing_sampling_rate()
|
||||
|
||||
if self.noCrossFade: # Beatrice, LLVC
|
||||
@ -234,12 +234,14 @@ class VoiceChangerV2(VoiceChangerIF):
|
||||
block_frame = receivedData.shape[0]
|
||||
crossfade_frame = min(self.settings.crossFadeOverlapSize, block_frame)
|
||||
self._generate_strength(crossfade_frame)
|
||||
t.record("generate_strength")
|
||||
|
||||
audio = self.voiceChanger.inference(
|
||||
receivedData,
|
||||
crossfade_frame=crossfade_frame,
|
||||
sola_search_frame=sola_search_frame,
|
||||
)
|
||||
t.record("inference")
|
||||
|
||||
if hasattr(self, "sola_buffer") is True:
|
||||
np.set_printoptions(threshold=10000)
|
||||
@ -271,6 +273,8 @@ class VoiceChangerV2(VoiceChangerIF):
|
||||
logger.info("[Voice Changer] warming up... generating sola buffer.")
|
||||
result = np.zeros(4096).astype(np.int16)
|
||||
|
||||
t.record("sora")
|
||||
|
||||
if hasattr(self, "sola_buffer") is True and sola_offset < sola_search_frame:
|
||||
offset = -1 * (sola_search_frame + crossfade_frame - sola_offset)
|
||||
end = -1 * (sola_search_frame - sola_offset)
|
||||
@ -280,6 +284,8 @@ class VoiceChangerV2(VoiceChangerIF):
|
||||
self.sola_buffer = audio[-crossfade_frame:] * self.np_prev_strength
|
||||
# self.sola_buffer = audio[- crossfade_frame:]
|
||||
|
||||
t.record("post")
|
||||
|
||||
mainprocess_time = t.secs
|
||||
|
||||
# 後処理
|
||||
|
||||
@ -22,6 +22,7 @@ LoadModelParamFileKind: TypeAlias = Literal[
|
||||
"beatriceModel",
|
||||
"llvcModel",
|
||||
"llvcConfig",
|
||||
"easyVCModel",
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -57,7 +57,7 @@ class Timer2(object):
|
||||
if self.enable is False:
|
||||
return
|
||||
|
||||
self.maxStores = 10
|
||||
self.maxStores = 1
|
||||
|
||||
current_frame = inspect.currentframe()
|
||||
caller_frame = inspect.getouterframes(current_frame, 2)
|
||||
|
||||
@ -16,3 +16,4 @@ class VoiceChangerParams:
|
||||
crepe_onnx_tiny: str
|
||||
rmvpe: str
|
||||
rmvpe_onnx: str
|
||||
whisper_tiny: str
|
||||
|
||||
Loading…
Reference in New Issue
Block a user