Speech Synthesis: Mandarin Chinese Multispeaker - FastPitch Model Overview

Description:

The Mandarin Chinese Multispeaker FastPitch-HifiGAN model transcribes text into audio representations using two model components: FastPitch and HifiGAN. This model is ready for commercial use.

FastPitch is a mel-spectrogram generator used as the first part of a neural text-to-speech system with a neural vocoder. This model uses the International Phonetic Alphabet (IPA) for inference and training, and it can output a male voice for German.

This FastPitch model was trained to be used with a corresponding HifiGAN model available here. HifiGAN is a neural vocoder model for text-to-speech applications. It is the second part of a two-stage speech synthesis pipeline.

References:

FastPitch paper: https://arxiv.org/abs/2006.06873

HifiGAN paper: https://arxiv.org/abs/2010.05646

Model Architecture:

Architecture Type: Transformer + Generative Adversarial Network (GAN)

Network Architecture: FastPitch + HifiGAN

FastPitch is a fully-parallel text-to-speech transformer-based model, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and be engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2 for mel-spectrogram synthesis of a typical utterance.

HifiGAN is a neural vocoder based on a generative adversarial network framework. During training, the model uses a powerful discriminator consisting of small sub-discriminators, each one focusing on specific periodic parts of a raw waveform.

Input:

For FastPitch (1st Stage): Text Strings in Mandarin Chinese

Other Properties Related to Input: 400 Character Text String Limit

Output:

For HifiGAN (2nd Stage): Audio of shape (batch x time) in wav format

Other Parameters Related to Output: Mono, Encoded 16 bit audio; 20 Second Maximum Length; Depending on input, this model can output a female or a male voice for Mandarin Chinese with two (2) emotions for the female voice and six (6) emotions for male voices. The female voice is classified as “neutral” and “calm.” The male voice is classified as “neutral,” “calm,” “happy,” and “fearful”, “sad”, and “angry.”

Software Integration:

Runtime Engine(s): Riva 2.13.0 or greater

Supported Hardware Platform(s):

NVIDIA A100 GPU
NVIDIA A30 GPU
NVIDIA A10 GPU
NVIDIA H100 GPU
NVIDIA Jetson Orin
NVIDIA Jetson AGX Xavier
NVIDIA Jetson NX Xavier
NVIDIA L4 GPU
NVIDIA L40 GPU
NVIDIA Turing T4
NVIDIA Volta V100

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

FastPitch_Zh-CN-Multispeaker-1.1

Training & Evaluation:

Training Dataset:

** Data Collection Method by dataset

Human
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset of audio-text pairs sampled at 44100 Hz, which contains one Female and one Male voice speaking Mandarin Chinese. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.

Evaluation Dataset:

** Data Collection Method by dataset

Human
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): This model is tested on a proprietary dataset sampled at 44100 Hz, which contains one Female and one Male voice speaking Mandarin Chinese. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.

Inference:

Engine: Triton
\nTest Hardware:

NVIDIA A100 GPU
NVIDIA A30 GPU
NVIDIA A10 GPU
NVIDIA H100 GPU
NVIDIA Jetson Orin
NVIDIA Jetson AGX Xavier
NVIDIA Jetson NX Xavier
NVIDIA L4 GPU
NVIDIA L40 GPU
NVIDIA Turing T4
NVIDIA Volta V100

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 supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva license.

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\n\n## Output:\nFor HifiGAN (2nd Stage): Audio of shape (batch x time) in wav format\n\n**Other Parameters Related to Output:** Mono, Encoded 16 bit audio; 20 Second Maximum Length; Depending on input, this model can output a female or a male voice for Mandarin Chinese with two (2) emotions for the female voice and six (6) emotions for male voices. The female voice is classified as “neutral” and “calm.” The male voice is classified as “neutral,” “calm,” “happy,” and “fearful”, “sad”, and “angry.” \n\n## Software Integration:\n**Runtime Engine(s):** Riva 2.13.0 or greater
\n\n**Supported Hardware Platform(s):**
\n* NVIDIA A100 GPU\n* NVIDIA A30 GPU\n* NVIDIA A10 GPU\n* NVIDIA H100 GPU\n* NVIDIA Jetson Orin\n* NVIDIA Jetson AGX Xavier\n* NVIDIA Jetson NX Xavier\n* NVIDIA L4 GPU\n* NVIDIA L40 GPU\n* NVIDIA Turing T4\n* NVIDIA Volta V100\n\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \nFastPitch_Zh-CN-Multispeaker-1.1
\n\n# Training & Evaluation: \n\n## Training Dataset:\n** Data Collection Method by dataset
\n* Human
\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** This model is trained on a proprietary dataset of audio-text pairs sampled at 44100 Hz, which contains one Female and one Male voice speaking Mandarin Chinese. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.
\n\n## Evaluation Dataset:\n** Data Collection Method by dataset
\n* Human
\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** This model is tested on a proprietary dataset sampled at 44100 Hz, which contains one Female and one Male voice speaking Mandarin Chinese. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.
\n\n## Inference:\n**Engine:** Triton
\n**Test Hardware:**
\n* NVIDIA A100 GPU\n* NVIDIA A30 GPU\n* NVIDIA A10 GPU\n* NVIDIA H100 GPU\n* NVIDIA Jetson Orin\n* NVIDIA Jetson AGX Xavier\n* NVIDIA Jetson NX Xavier\n* NVIDIA L4 GPU\n* NVIDIA L40 GPU\n* NVIDIA Turing T4\n* NVIDIA Volta V100\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 supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## License\nBy downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva [license](https://docs.nvidia.com/ai-foundation-models-community-license.pdf).","RIVA Mandarin CN Fastpitch with Emotions",["D",1719559150984],"2.15.0","/api/image/nvidia/riva/models/speechsynthesis_zh_cn_fastpitch_ipa","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",90025181,"bias","Field | Response\n:------|:----------\nWhat is the language balance of the model validation data? | Mandarin Chinese: 100%\nWhat is the geographic origin language balance of the model validation data? | China: 100%\nWhat is the accent balance of the model validation data? | Mandarin Chinese: 100%\nMeasures taken to mitigate against unwanted bias: | None\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None","explainability","Field | Response\n:------|:----------\nIntended Application & Domain: | Speech Synthesis\nModel Task | Speech Synthesis and Generative Adversarial Network\nIntended Users | This model is intended for developers building interactive call centers, virtual assistants, language learning assistants to improve pronunciation, automatically generate voice-overs, narrate or comment on videos, and provide audio alternatives for visually impaired users or people with light sensitivity.\nModel Output | Mel spectrograms (passed onto pair HifiGAN model to create audio files (.wav))\nHow the model works | Model transcribes input text characters into audio representation.\nTechnical Limitations | Model only has the capacity to produce a voice in the language, dialect and gender(s) in which it is trained. This model makes no effort to moderate or modify input text. \nPerformance Metrics | % preference when compared with available alternatives
Pitch (mean)
Pitch_standard deviation (std) (mean)
Pitch_kurtosis (mean)
Pitch_skew (mean)
Fundamental Frequency Ratio (f0_ratio) (mean)
f0_ratio_std (mean)
f0_ratio_kurtosis (mean)
f0_ratio_skew (mean)
Pitch (median)
Pitch_std (median)
Pitch_kurtosis (median)
Pitch_skew (median)
f0_ratio (median)
f0_ratio_std (median)
f0_ratio_kurtosis (median)
f0_ratio_skew (median)\"\nPotential Known Risks | May unnaturally synthesize vocabulary not included in the pronunciation dictionary or omit phonetic symbols not used in training.\nLicensing: | [https://docs.nvidia.com/ai-foundation-models-community-license.pdf](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)","privacy","Field | Response\n:------|:---------\nGeneratable or reverse engineerable personal information? | None\nHow often is dataset reviewed? | Before release (during training)\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data collected for the development of the model, was it collected directly by NVIDIA? | Yes\nIf personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes\nIf personal data collected for the development of this AI model, was it minimized to only what was required? | Yes\nIs there provenance for all datasets used in training? | Yes\nIs data in dataset traceable? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.","safetyAndSecurity","Field | Response\n:------|:---------\nModel Application(s): | Speech synthesis\nDescribe the life critical impact (if present). | Not Applicable\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.\nUse case restrictions for the model. | Abide by [https://developer.nvidia.com/riva/ga/license](https://developer.nvidia.com/riva/ga/license)",[],[],[],[],[],{"_171":1229,"_173":1230},"ngcApps",[1182],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1163":1164,"_1165":1166,"_1167":1168,"_1169":1170,"_1171":263,"_203":287,"_1172":1173,"_1174":51,"_1175":214,"_1176":214,"_1177":1178,"_1179":214,"_1180":1181},"amplitude",{"_1160":1161,"_802":-7,"_1162":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1154":1155,"_1156":1157,"_1158":1159},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1042":369,"_1043":1044,"_1045":1046,"_1047":1048},"announcements",[],"consentState",{"_325":1032,"_1033":214,"_1034":26,"_1035":26,"_1036":1037},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1785711494916,[],[],[301,302,303,304,305,306],{"_40":54,"_42":65,"_44":166,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":64,"_44":166,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":166,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":166,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":166,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"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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