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Overview

This is a collection of models to enable OpenVoice support for the NVIDIA In-Game Inferencing (NVIGI) SDK. Please see each overview section below for details on the following models:

BERT base model (uncased)
MelloTTS
OpenVoice Converter

Model Overview : Bert embedding

Description:

Bert-base-uncased is a text embedding model. This is used by OpenVoice Text-to-Speech (TTS) solution to generate embeddings from input text. This model is to be used with the NVIGI SDK OpenVoice TTS plugin.

This model is ready for commercial and non-commercial use.

Model Developer: Google

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see BERT base model (uncased).

License/Terms of Use:

This model is distributed under Apache 2-0 Bert License. Please refer to BERT base model (uncased) Model Card for further details.

Reference(s):

BERT base model (uncased) Model Card

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: BERT

Input:

Input Type(s): Text
\nInput Format(s): Int tokens
\nInput Parameters: 1D

Output:

Output Type(s): Embedding vectors
\nOutput Format: Vector
\nOutput Parameters: Two Dimensional (2D)

Supported Hardware Microarchitecture Compatibility:

NVIDIA Lovelace

Supported Operating System(s):

Linux, Windows

Model Version(s):

BERT base model (uncased) q4_k_s 1.0

Training and Evaluation Datasets:

Training Dataset:
\nLinks: legacy-datasets/wikipedia and bookcorpus/bookcorpus
\nData Collection Method by dataset: Unknown
\nLabeling Method by dataset: Unknown
\nProperties:

wikipedia dataset\n
- ~12GB of data based on wikipedia articles
- More information on Hugging Face model card
\n
Bookcorpus\n
- 7,185 unique books
- More information on Hugging Face model card
\n

Evaluation Dataset:
\nData Collection Method by dataset: Unknown
\nLabeling Method by dataset: Unknown
\nProperties: See Model Card under “Glue Test Results.”

Inference:

Engine: ONNX
\nTest Hardware : RTX 4090

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Model Overview : MelloTTS v2 and v3

Description:

MelloTTS is a Text-to-Speech (TTS) model used in the OpenVoice TTS solution as a base model (before doing voice conversion). The differences between V2 and V3 are only the weights. V3 has better voice (more realistic) quality but fewer speakers.

This model is ready for commercial/non-commercial use.

Model Developer: Myshell-ai

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see:

myshell-ai/MeloTTS-English-v2.
myshell-ai/MeloTTS-English-v3.

License/Terms of Use:

This model is distributed under MIT licenseMelloTTS License. Please refer to MelloTTS Model Card for further details.

Reference(s):

myshell-ai/MeloTTS-English-v2.
myshell-ai/MeloTTS-English-v3.

Model Architecture:

Architecture Type: Transformer/Flow Network
\nNetwork Architecture: ViTs architecture

Input:

Input Type(s): phonemes, tones, embedding, speaker id
\nInput Format(s): Int, int, float, int
\nInput Parameters: 1D for all parameters

Output:

Output Type(s): Audio at a sampling rate of 44100
\nOutput Format: Vector
\nOutput Parameters: 2D

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ada

Supported Operating System(s):

Windows, Linux

Model Version(s):

MELLO TTS float16 v2
MELLO TTS float16 v3

Training, Testing, and Evaluation Datasets:

Data Collection Method by dataset: Unknown
\nLabeling Method by dataset: Unknown
\nProperties: The dataset used to train this model is not known. The full testing dataset is also not known, however, their website features numerous sample outputs.

Inference:

Engine: Onnx
\nTest Hardware: RTX 4090

Ethical Considerations:

Please report security vulnerabilities or NVIDIA AI Concerns here.

Model Overview : OpenVoice Converter Model

Description:

The OpenVoice converter model is used in the OpenVoice TTS solution to clone the voice of a reference speaker and apply it to an output audio (output of the base model).

This model is ready for commercial/non-commercial use.

Model Developer: Myshell-ai

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see:

myshell-ai/OpenVoiceV2.

License/Terms of Use:

This model is distributed under MIT licenseOpenVoice License. Please refer to myshell-ai/OpenVoiceV2 for further details.

Reference(s):

myshell-ai/OpenVoiceV2.
OpenVoice paper

Model Architecture:

Architecture Type: Transformer/Flow Network
\nNetwork Architecture: Encoder-Decoder structure with an invertible normalizing flow.

Input:

Input Type(s): Audio at a sampling rate of 22050, Reference spectrogram
\nInput Format(s): Float, Float
\nInput Parameters: 1D

Output:

Output Type(s): Audio at a sampling rate of 22050
\nOutput Format: Vector
\nOutput Parameters: 2D

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ada

Supported Operating System(s):

Windows, Linux

Model Version(s):

OpenVoice converter TTS float16 v2

Training, Testing, and Evaluation Datasets:

Inference:

Engine: Onnx
\nTest Hardware: RTX 4090

Ethical Considerations:

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n**Network Architecture:** BERT\n\n## Input: \n**Input Type(s):** Text
\n**Input Format(s):** Int tokens
\n**Input Parameters:** 1D\n\n## Output: \n**Output Type(s):** Embedding vectors
\n**Output Format:** Vector
\n**Output Parameters:** Two Dimensional (2D)\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Lovelace\n\n**Supported Operating System(s):**\n* Linux, Windows\n\n## Model Version(s):\n* BERT base model (uncased) q4_k_s 1.0\n\n# Training and Evaluation Datasets:\n\nTraining Dataset:
\n**Links:** [legacy-datasets/wikipedia](https://huggingface.co/datasets/legacy-datasets/wikipedia) and [bookcorpus/bookcorpus](https://huggingface.co/datasets/bookcorpus/bookcorpus)
\n**Data Collection Method by dataset:** Unknown
\n**Labeling Method by dataset:** Unknown
\n**Properties:** \n* wikipedia dataset \n * ~12GB of data based on wikipedia articles\n * More information on Hugging Face model card\n* Bookcorpus\n * 7,185 unique books\n * More information on Hugging Face model card\n\nEvaluation Dataset:
\n**Data Collection Method by dataset:** Unknown
\n**Labeling Method by dataset:** Unknown
\n**Properties:** See Model Card under “Glue Test Results.”\n\n## Inference:\n**Engine:** ONNX
\n**Test Hardware** : RTX 4090\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n

\n\n# Model Overview : MelloTTS v2 and v3\n\n## Description:\nMelloTTS is a Text-to-Speech (TTS) model used in the OpenVoice TTS solution as a base model (before doing voice conversion). The differences between V2 and V3 are only the weights. V3 has better voice (more realistic) quality but fewer speakers.\n\nThis model is ready for commercial/non-commercial use.\n\n### Model Developer: Myshell-ai\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see: \n\n- [myshell-ai/MeloTTS-English-v2](https://huggingface.co/myshell-ai/MeloTTS-English-v2).\n- [myshell-ai/MeloTTS-English-v3](https://huggingface.co/myshell-ai/MeloTTS-English-v3).\n\n### License/Terms of Use:\nThis model is distributed under MIT license[MelloTTS License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md). Please refer to [MelloTTS Model Card](https://huggingface.co/myshell-ai/MeloTTS-English-v2) for further details.\n\n## Reference(s):\n- [myshell-ai/MeloTTS-English-v2](https://huggingface.co/myshell-ai/MeloTTS-English-v2).\n- [myshell-ai/MeloTTS-English-v3](https://huggingface.co/myshell-ai/MeloTTS-English-v3).\n\n## Model Architecture:\n**Architecture Type:** Transformer/Flow Network
\n**Network Architecture:** ViTs architecture\n\n## Input: \n**Input Type(s):** phonemes, tones, embedding, speaker id
\n**Input Format(s):** Int, int, float, int
\n**Input Parameters:** 1D for all parameters\n\n## Output: \n**Output Type(s):** Audio at a sampling rate of 44100
\n**Output Format:** Vector
\n**Output Parameters:** 2D\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Ada\n\n**Supported Operating System(s):**\n* Windows, Linux\n\n## Model Version(s):\n* MELLO TTS float16 v2\n* MELLO TTS float16 v3\n\n# Training, Testing, and Evaluation Datasets:\n**Data Collection Method by dataset:** Unknown
\n**Labeling Method by dataset:** Unknown
\n**Properties:** The dataset used to train this model is not known. The full testing dataset is also not known, however, their website features numerous sample outputs. \n\n## Inference:\n**Engine:** Onnx
\n**Test Hardware:** RTX 4090\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n

\n\n# Model Overview : OpenVoice Converter Model\n\n## Description:\nThe OpenVoice converter model is used in the OpenVoice TTS solution to clone the voice of a reference speaker and apply it to an output audio (output of the base model).\n\nThis model is ready for commercial/non-commercial use.\n\n### Model Developer: Myshell-ai\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see: \n\n- [myshell-ai/OpenVoiceV2](https://huggingface.co/myshell-ai/OpenVoiceV2).\n\n### License/Terms of Use:\nThis model is distributed under MIT license[OpenVoice License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md). Please refer to [myshell-ai/OpenVoiceV2](https://huggingface.co/myshell-ai/OpenVoiceV2) for further details.\n\n## Reference(s):\n- [myshell-ai/OpenVoiceV2](https://huggingface.co/myshell-ai/OpenVoiceV2).\n- [OpenVoice paper](https://arxiv.org/pdf/2312.01479)\n\n## Model Architecture:\n**Architecture Type:** Transformer/Flow Network
\n**Network Architecture:** Encoder-Decoder structure with an invertible normalizing flow.\n\n## Input: \n**Input Type(s):** Audio at a sampling rate of 22050, Reference spectrogram
\n**Input Format(s):** Float, Float
\n**Input Parameters:** 1D \n\n## Output: \n**Output Type(s):** Audio at a sampling rate of 22050
\n**Output Format:** Vector
\n**Output Parameters:** 2D\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Ada\n\n**Supported Operating System(s):**\n* Windows, Linux\n\n## Model Version(s):\nOpenVoice converter TTS float16 v2\n\n# Training, Testing, and Evaluation Datasets:\n**Data Collection Method by dataset:** Unknown
\n**Labeling Method by dataset:** Unknown
\n**Properties:** The dataset used to train this model is not known. The full testing dataset is also not known, however, their website features numerous sample outputs. \n\n## Inference:\n**Engine:** Onnx
\n**Test Hardware:** RTX 4090\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","OpenVoice",["D",1738194294354],"publisher","","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nvigisdk/models/openvoice","rawImageUrl","https://developer.download.nvidia.com/images/products/aim-sdk-1920x1080.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[259],"displayLabels",[238,239,240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":255,"_243":256,"_245":257,"_247":248},{"_206":253,"_243":253,"_245":254,"_247":248},{"_206":251,"_243":251,"_245":252,"_247":248},{"_206":242,"_243":244,"_245":246,"_247":248},"Text to Speech","value","uscs_text_to_speech","filter",{"_249":250,"_243":244},"category","label","field","application","Nv IGI SDK",{"_249":248,"_243":251},"NSPECT-SDAR-0UZD",{"_249":248,"_243":253},"Gaming","indus_gaming",{"_249":258,"_243":256},"industry",{"_206":260,"_243":261,"_245":262,"_247":263},"Runs on RTX","pltfm_runs_on_rtx",{"_249":264,"_243":261},"feature","platform",{"_105":1441,"_107":1442},"ngcApps",[1394],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1375":1376,"_1377":1378,"_1379":1380,"_1381":1382,"_1383":276,"_137":300,"_1384":1385,"_1386":55,"_1387":84,"_1388":84,"_1389":1390,"_1391":84,"_1392":1393},"amplitude",{"_1372":1373,"_973":-7,"_1374":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1366":1367,"_1368":1369,"_1370":1371},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1248":210,"_1249":1250,"_1251":1252,"_1253":1254},"announcements",[],"consentState",{"_339":1238,"_1239":84,"_1240":30,"_1241":30,"_1242":1243},"consentConfig",{"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340},"showContactAdmin",1784258083247,[],[],[314,315,316,317,318,319],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":60,"_50":61,"_52":66,"_54":55},{"_44":58,"_46":65,"_48":60,"_50":61,"_52":66,"_54":55},{"_44":58,"_46":59,"_48":60,"_50":61,"_52":62,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":320,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},3,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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