curl --request POST \
--url https://api.aurigin.ai/v1/voiceid/enroll \
--header 'Content-Type: multipart/form-data' \
--header 'x-api-key: <api-key>' \
--form audio_file='@example-file' \
--form 'user_id=<string>'{
"embedding": [
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0.01862935535609722,
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0.004668207839131355,
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-0.026682903990149498,
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0.026866627857089043,
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-0.0018339564558118582,
-0.02220608852803707,
0.0018473020754754543,
0.03432437777519226,
0.005599610507488251,
-0.020381037145853043,
0.03150622174143791,
0.020770778879523277,
-0.005547627341002226,
0.032829105854034424,
0.020428897812962532,
-0.007288635242730379,
-0.002440565498545766,
-0.018194381147623062,
0.005998202133923769,
-0.018759630620479584,
0.009391428902745247,
0.006601202767342329
],
"processing_time": 13.586525440216064,
"model_version": "titanet-large",
"deepfake_checked": true
}Create a unique, non-reversible voiceprint (embedding) for a user or speaker that can later be used for identity verification. This is the foundation for comparing future recordings to a “known” voice profile.
Requirements:
Note: Voice prints and audio files are not stored in the first implementation. You must store the returned embedding vector yourself.
curl --request POST \
--url https://api.aurigin.ai/v1/voiceid/enroll \
--header 'Content-Type: multipart/form-data' \
--header 'x-api-key: <api-key>' \
--form audio_file='@example-file' \
--form 'user_id=<string>'{
"embedding": [
-0.021047543734312057,
-0.024001557379961014,
-0.011682840064167976,
0.01862935535609722,
0.030680431053042412,
0.012365893460810184,
0.022932840511202812,
-0.02653215453028679,
0.03283798322081566,
0.02938467636704445,
0.016921166330575943,
0.00456651858985424,
0.004668207839131355,
0.019676126539707184,
-0.026682903990149498,
0.007073606364428997,
0.02817155048251152,
-0.01307824905961752,
-0.005019648000597954,
0.014194189570844173,
-0.003651274833828211,
-0.013507496565580368,
0.0035653149243444204,
0.012558531947433949,
-0.008742935955524445,
-0.01317399088293314,
-0.02584591507911682,
-0.04106520116329193,
0.015779513865709305,
-0.012857845053076744,
-0.023655345663428307,
0.008520219475030899,
-0.004576820880174637,
0.01908191852271557,
-0.006375580094754696,
-0.01133985910564661,
-0.024633223190903664,
-0.006668175105005503,
-0.023163946345448494,
-0.027255428954958916,
-0.010428240522742271,
0.04642370715737343,
-0.004443565383553505,
-0.028582191094756126,
0.012907243333756924,
-0.0254207793623209,
-0.009619798511266708,
-0.0018211244605481625,
0.013937097042798996,
0.0079690245911479,
-0.009932221844792366,
0.010009655728936195,
0.014746781438589096,
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-0.015499094501137733,
0.014383708126842976,
0.018852459266781807,
0.04513779282569885,
0.0038257490377873182,
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0.046820636838674545,
-0.0068024941720068455,
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-0.004554769955575466,
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-0.042426858097314835,
-0.020146694034337997,
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-0.04059327021241188,
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-0.0018339564558118582,
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0.03432437777519226,
0.005599610507488251,
-0.020381037145853043,
0.03150622174143791,
0.020770778879523277,
-0.005547627341002226,
0.032829105854034424,
0.020428897812962532,
-0.007288635242730379,
-0.002440565498545766,
-0.018194381147623062,
0.005998202133923769,
-0.018759630620479584,
0.009391428902745247,
0.006601202767342329
],
"processing_time": 13.586525440216064,
"model_version": "titanet-large",
"deepfake_checked": true
}Upload a clean voice sample (>= 10 seconds) to generate a voice embedding.
Voice embedding generated successfully
Voice embedding vector (256-dimensional)
[
-0.021047543734312057,
-0.024001557379961014,
-0.011682840064167976
]Time taken to process the audio and generate the embedding in seconds
13.586525440216064
Model version used to generate the embedding
"titanet-large"
Whether deepfake detection was performed during enrollment
true