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/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fstudiovoice.jpg&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fstudiovoice.jpg&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fstudiovoice.jpg&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fstudiovoice.jpg&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fstudiovoice.jpg&w=3840&q=75"/><div class="z-20 flex min-w-0 flex-1 flex-col justify-between gap-ml md:flex-row"><div class="flex min-w-0 flex-1 flex-col"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap align-center mb-sm mt-none flex-wrap gap-sm" data-testid="kui-flex" style="white-space:pre-wrap"><h1 class="text-lg leading-heading"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/nvidia">nvidia</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name">studiovoice</span></span></h1><span class="inline-flex h-5 w-fit items-center gap-1 rounded-1 px-2 align-bottom text-xs font-bold uppercase leading-caps tracking-more border-0 bg-p300 text-p700">RUN ANYWHERE</span></div><p class="text-mm font-normal text-manitoulinLightGray md:text-ms">Enhance speech by correcting common audio degradations to create studio quality speech output.</p><div class="mt-md flex flex-wrap gap-sm"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Digital+Human">digital human</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Nvidia+Maxine">nvidia maxine</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text 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data-testid="kui-tab-trigger" tabindex="-1" data-orientation="horizontal" data-radix-collection-item="">Docker</button></div></div></div><button class="c-TKGAm c-TKGAm-dJzfbb-size-standard c-TKGAm-hGzxBS-tone-tertiary c-TKGAm-ikBYYkb-css c-PJLV" data-testid="kui-tab-control"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 16 16" width="1em" height="1em" display="inline-block" data-icon-name="shapes-chevron-right" class="c-ftWhJU" style="align-self:center"><path fill="currentColor" fill-rule="evenodd" d="M9.293 8 5.646 4.354l.708-.708L10.707 8l-4.353 4.354-.708-.708z" clip-rule="evenodd"></path></svg></button></div><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap border-b-2 border-menuBorderDefault hover:border-menuBorderHover" data-testid="kui-flex"><a class="block px-3 py-2 text-ms text-n000" href="https://docs.nvidia.com/nim/maxine/studio-voice/latest/index.html" rel="noopener noreferrer" target="_blank">API Reference</a></div></div></div><div class="" role="tabpanel"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-iTKOFX-direction-column c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-icmpvrW-css" data-testid="kui-flex"><div class="c-PJLV c-PJLV-ifIbGnO-css t-laoRNc" data-testid="kui-theme"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-icmpvrW-css" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-XefLA-wrap-wrap c-dhzjXW-ifGHEql-css c-cyMReR" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap input-slot w-full flex-1 py-lg lg:pr-lg border-menuBorderDefault lg:border-r" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><div class="c-hakyQ h-[32px] flex-1 border-b-sm border-menuBorderDefault pr-md" data-testid="kui-box"><h3 class="c-cULKyM">Input</h3></div></div><div class=""><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-bZmKkd-justify-flex-end c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-tertiary btn-md btn-pill">View Examples</button></div><div><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap rounded-lg border border-primary p-sm" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap p-xs" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-ktOuEZ-gap-none c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><button data-group-item="" data-group-item-first="" aria-label="Record" class="text-center font-sans font-medium leading-text flex-row btn-primary btn-md btn-rounded btn-inverse hover:z-10 flex items-center justify-center gap-sm rounded-r-none border-input px-[15px] hover:border-transparent hover:bg-tw120 hover:text-n050 active:border-transparent active:bg-tw100 disabled:border-input/60 disabled:bg-transparent disabled:text-n600 ml-0 border-x-0 data-[group-item-first]:border-l-sm data-[group-item-last]:border-r-sm"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 16 16" width="1em" height="1em" display="inline-block" data-icon-name="microphone" font-size="24"><path fill="currentColor" d="M8 1a3 3 0 0 1 3 3v4a3 3 0 0 1-6 0V4a3 3 0 0 1 3-3"></path><path fill="currentColor" d="M8 12a4 4 0 0 0 4-4h1a5 5 0 0 1-3.448 4.755L10.151 15H5.848l.602-2.245A5 5 0 0 1 3 8h1a4 4 0 0 0 4 4"></path></svg></button><button disabled="" data-group-item="" data-group-item-last="" aria-label="Play" class="text-center font-sans font-medium leading-text flex-row btn-primary btn-md btn-rounded btn-inverse hover:z-10 flex items-center justify-center gap-sm rounded-l-none border-input px-[15px] hover:border-transparent hover:bg-tw120 hover:text-n050 active:border-transparent active:bg-tw100 disabled:border-input/60 disabled:bg-transparent disabled:text-n600 ml-0 border-x-0 data-[group-item-first]:border-l-sm data-[group-item-last]:border-r-sm"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 16 16" width="1em" height="1em" display="inline-block" data-icon-name="play" font-size="24"><path fill="currentColor" d="M5 12.37V3.63L12.492 8z"></path></svg></button></div><div class="c-hakyQ c-hakyQ-icNkccz-css" data-testid="kui-box"></div><button aria-label="Remove File" class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-tertiary btn-md btn-pill btn-inverse btn-icon-only"></button><span class="c-kAArxJ text-primary" data-testid="kui-text">0:00</span></div><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><label class="sc-aXZVg kGRXWn "><input accept=".wav" type="file"/><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-XefLA-wrap-wrap c-dhzjXW-ikGSFxO-css" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap mr-auto" data-testid="kui-flex"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 16 16" width="1em" height="1em" display="inline-block" data-icon-name="document" color="#5E5E5E" font-size="16"><path fill="currentColor" d="M13 5.293V14H3V2h6.707zM12 13V6H9V3H4v10zm-.707-8L10 3.707V5z"></path></svg><span class="c-kAArxJ c-kAArxJ-jQlRHy-variant-helper" data-testid="kui-text">Upload an audio file</span></div><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><span class="c-kAArxJ c-kAArxJ-jQlRHy-variant-helper peer truncate [&~span]:border-l-inputFgDefault [&~span]:pl-ss" data-testid="kui-text">.wav</span></div></div><div class="c-dhzjXW c-dhzjXW-JrrAq-align-flex-start c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap ml-sm" data-testid="kui-flex"><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-tertiary btn-sm btn-pill">Upload File</button></div></div></label></div></div><label class="c-fpDrvI pt-sm text-manitoulinLightGray" data-testid="kui-label">Your audio input will not be stored by NVIDIA and will only be used to generate the output on this page.</label></div></div></div><div class="c-dhzjXW c-dhzjXW-igtkwp-align-flex-end c-dhzjXW-ejCoEP-direction-row c-dhzjXW-lpoiht-gap-md c-dhzjXW-bZmKkd-justify-flex-end c-dhzjXW-kVNAnR-wrap-nowrap to-n900/0 sticky bottom-0 w-full flex-wrap-reverse bg-gradient-to-t from-manitoulinDarkBlack from-80% to-100% pb-md pt-lg" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-igtkwp-align-flex-end c-dhzjXW-iTKOFX-direction-column c-dhzjXW-dkuYHy-gap-ms c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-stack"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-tertiary btn-lg btn-pill">Reset</button><button data-nvtrack="Model Run button click" data-nvtrack-model="" data-nvtrack-adobe-type="modelEngagementClick" class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-primary btn-lg btn-pill min-w-[75px]">Run</button></div></div></div></div><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap output-slot flex-1 py-lg lg:pl-lg" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-ilkBNdM-css" data-testid="kui-stack"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><div class="c-hakyQ h-[32px] flex-1 border-b-sm border-menuBorderDefault pr-md" data-testid="kui-box"><h3 class="c-cULKyM">Output</h3></div></div><div class="h-full" role="tabpanel"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-lpoiht-gap-md c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-ilkBNdM-css" 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|\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No |2f:T1a78,"])</script><script>self.__next_f.push([1,"# Model Overview\n\n## Description\n\nMaxine Studio Voice enhances the input speech recorded through low quality \nmicrophones in noisy/reverberant environment to studio-recorded quality speech.\n\nStudio Voice is available under NVIDIA Maxine — a developer platform for \ndeploying AI features that enhance audio, video, and creating new experiences \nin real-time audio-video communication. Maxine's state-of-the-art models create \nhigh-quality AI effects using standard microphones and cameras without \nadditional special equipments.\n\nNVIDIA Maxine is exclusively part of NVIDIA AI Enterprise for production \nworkflows — an extensive library of full-stack software, including AI solution \nworkflows, frameworks, pre-trained models, and infrastructure optimization. \n\n\n## Terms of use\n\nThe use of NVIDIA Maxine's Studio Voice is available as a demonstration of the \ninput and output of the Studio Voice generative model. As such the user may \nupload an audio file or select one of the sample inputs and download the \ngenerated audio for evaluation under the terms of the \n[NVIDIA MAXINE EVALUATION LICENSE AGREEMENT](https://developer.download.nvidia.com/maxine/nvidia-maxine-evaluation-license-24oct2023.pdf). \n\n\n## References(s):\n\n* [NVIDIA Maxine](https://developer.nvidia.com/maxine) \n\n\n## Model Architecture\n\n**Architecture Type:** Convolution Neural Networks (CNNs), Transformers,\n Generative Adversarial Networks (GANs) \n**Network Architecture:** Encoder-Decoder \n**Model Version:** 0.2 \n\n\n## Input:\n\n**Input Type(s):** Ordered List (audio samples) \n**Input Format(s):** FP32 (-1.0 to 1.0) \n**Other Properties Related to Input:** Pulse Code Modulation (PCM) audio samples\nwith no encoding or pre-processing; 16kHz or 48kHz sampling rate required. \n\n## Output:\n\n**Output Type(s):** Ordered List (audio samples) \n**Output Format:** FP32 (-1.0 to 1.0) \n**Other Properties Related to Output:** PCM audio samples at input sampling rate\nwith no encoding or post-processing. \n\n## Software Integration\n\n**Supported Hardware Platform(s):** Hopper, Ada, Ampere, Turing, Volta \n**Test Hardware:** A10, L40, T10 \n**Supported Operating System(s):** Linux, Windows \n\n\n# Training \u0026 Evaluation\n\n## Datasets\n\nNVIDIA models are trained on a diverse set of public and proprietary datasets.\nThe Studio Voice model is trained on a dataset that comprises of diverse \nEnglish accents.\n\n**Link:** [DAPS](https://ccrma.stanford.edu/~gautham/Site/daps.html) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nThe DAPS dataset has 15 versions of audio (3 professional versions and\n12 consumer device/real-world environment combinations). Each version consists\nof about 4.5 hours of data (about 14 minutes from each of 20 speakers). \n\n**Link:** [LibriTTS](https://www.openslr.org/60/) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nLibriTTS is a multi-speaker English corpus of approximately 585 hours of read\nEnglish speech, which is resampled at 16kHZ. \n\n**Link:** [VCTK](https://datashare.ed.ac.uk/handle/10283/3443) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nThis CSTR VCTK Corpus includes speech data uttered by 110 English speakers with\nvarious accents. Each speaker reads out about 400 sentences, which were selected\nfrom a newspaper, the rainbow passage and an elicitation paragraph used for the\nspeech accent archive. \n\n**Link:** [HiFi-TTS](https://www.openslr.org/109/) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nA multi-speaker English dataset for training text-to-speech models.\nThe HiFi-TTS dataset contains about 291.6 hours of speech from 10 speakers with\nat least 17 hours per speaker sampled at 44.1 kHz. \n\n**Link:** [Device Recorded VCTK (DR-VCTK)](https://github.com/nii-yamagishilab/downloader-DR-VCTK-complete) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nDevice recorded version of VCTK dataset on common consumer devices \n(laptop, tablet and smartphone) in office environment. This dataset contains \n109 English speakers with different accents. There are around 400 sentences \navailable from each speaker. For this recording, 8 different microphones were \nused. This dataset contains around 250 Gb of re-recorded speech. \n\n**Link:** [Dataset of impulse responses from variable acoustics room Arni at Aalto Acoustic Labs](https://zenodo.org/records/6985104#.Y7_vv3ZBy3C) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nA dataset of impulse responses collected in the variable acoustics laboratory \nArni at Acoustics Lab of Aalto University, Espoo, Finland. IRs of 5342 \nconfigurations of sound absorption in Arni are included in the dataset. Each of \nthem were measured using an omnidirectional sound source and 5 sound receivers. \nFor each configuration, 5 impulse reponses (IRs) were captured. The total number\nof measurements in the dataset is 132 037. \n\n**Link:** [Room Impulse Response and Noise Database](https://www.openslr.org/28/) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nA database of simulated and real room impulse responses, isotropic and\npoint-source noises. The audio files in this data are all in 16KHz sampling rate\nand 16-bit precision. \n\n**Link:** [DNS Challenge 5](https://github.com/microsoft/DNS-Challenge/tree/2db96d5f75257df764a6ef66513b4b97bc707f30) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nCollated dataset of clean speech, noise and impulse response provided by \nMicrosoft for the ICASSP 2023 Deep Noise Suppression Challenge. \n\n**Link:** [AudioSet](https://research.google.com/audioset/download.html) \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \nAudioSet consists of an expanding ontology of 632 audio event classes and \na collection of 2,084,320 human-labeled 10-second sound clips drawn from \nYouTube videos. \n\n\n# Inference\n\n**Engine:** [Triton](https://developer.nvidia.com/triton-inference-server) \n**Test Hardware:** A10, L40, T10 \n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established\npolicies and practices to enable development for a wide array of AI applications.\nWhen downloaded or used in accordance with our terms of service, developers\nshould work with their supporting model team to ensure this model meets\nrequirements for the relevant industry and use case and addresses unforeseen\nproduct misuse. For more detailed information on ethical considerations for this\nmodel, please see the Model Card++ [Explainability](explainability), [Bias](bias),\n[Safety \u0026 Security](safety-and-security), and [Privacy](privacy) Subcards. Please\nreport security vulnerabilities or NVIDIA AI Concerns\n[here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"30:T59f,| Field | Response |\n|-------|----------|\n| Intended Application \u0026 Domain: | Voice enhancement |\n| Model Type: | Speech Enhancement |\n| Intended Users: | Content developers and Broadcasters |\n| Output: | Audio |\n| Describe how the model works: | This model enhances the voice in input audio by removing acoustic noise, room reverberation, and bad frequency characteristics of the recording device while maintaining the linguistic and vocal properties. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age (18+), Gender |\n| Verified to have met prescribed quality standards: | Yes |\n| Target Key Performance Indicator(s) (KPI(s)): | Quality Mean Opinion Score (MOS), Non-Intrusive Speech Quality Assessment (NISQA), Latency, Throughput |\n| Technical Limitations: | The model may not work work well on a variety of demographic and regional representations of English or with very noisy or very low quality inputs. Exclaimatory emotions may not be transferred to the output. |\n| Potential Known Risks: | The model may not preserve some speaker characteristics like pitch/fundamental frequency of the speakers voice and might also impact intelligibility of speech. |\n| Licensing: | [NVIDIA Maxine Evaluation License Agreement](https://developer.download.nvidia.com/maxine/nvidia-maxine-evaluation-license-24oct2023.pdf) |31:T1cb7,"])</script><script>self.__next_f.push([1,"NVIDIA Maxine Studio Voice NIM uses gRPC APIs for inferencing requests.\n\nA NGC API KEY is required to download the appropriate models and resources when starting the NIM.\n\nIf you are not familiar with how to create the `NGC_API_KEY` environment variable, the simplest way is to export it in your terminal:\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n```\n\nRun one of the following commands to make the key available at startup:\n\n```bash\n# If using bash\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.bashrc\n\n# If using zsh\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.zshrc\n```\n\nOther, more secure options include saving the value in a file, so that you can retrieve with `cat $NGC_API_KEY_FILE`, or using a [password manager](https://www.passwordstore.org/).\n\nThe following command launches the Maxine Studio Voice NIM container with the gRPC service. Find reference to runtime parameters for the container [here](https://docs.nvidia.com/nim/maxine/studio-voice/latest/getting-started.html#runtime-parameters-for-the-container).\n\n```bash\ndocker run -it --rm --name=maxine-studio-voice \\\n --net host \\\n --runtime=nvidia \\\n --gpus all \\\n --shm-size=8GB \\\n -e NGC_API_KEY=$NGC_API_KEY \\\n -e NIM_MODEL_PROFILE=\u003cnim_model_profile\u003e \\\n -e FILE_SIZE_LIMIT=36700160 \\\n nvcr.io/nim/nvidia/maxine-studio-voice:latest\n```\nEnsure you use the appropriate `NIM_MODEL_PROFILE` for your GPU. For more information about `NIM_MODEL_PROFILE`, refer to the the [NIM Model Profile Table](https://docs.nvidia.com/nim/maxine/studio-voice/latest/model-profile-table.html).\n\nPlease note, the flag --gpus all is used to assign all available GPUs to the docker container. This fails on multiple GPU unless all GPUs are same. To assign specific GPU to the docker container (in case of different multiple GPUs available in your machine) use --gpus '\"device=0,1,2...\"'\n\"'`\n\nIf the command runs successfully, you get a response similar to the following.\n\n```bash\n+-------------------------------+---------+--------+\n| Model | Version | Status |\n+-------------------------------+---------+--------+\n| maxine_nvcf_studiovoice | 1 | READY |\n| studio_voice_high_quality-48k | 1 | READY |\n+-------------------------------+---------+--------+\n\nI1126 09:22:21.040917 31 metrics.cc:877] \"Collecting metrics for GPU 0: NVIDIA GeForce RTX 4090\"\nI1126 09:22:21.046137 31 metrics.cc:770] \"Collecting CPU metrics\"\nI1126 09:22:21.046253 31 tritonserver.cc:2598] \n+----------------------------------+------------------------------------------------------------------------------------------------+\n| Option | Value |\n+----------------------------------+------------------------------------------------------------------------------------------------+\n| server_id | triton |\n| server_version | 2.50.0 |\n| server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy m |\n| | odel_configuration system_shared_memory cuda_shared_memory binary_tensor_data parameters stati |\n| | stics trace logging |\n| model_repository_path[0] | /opt/maxine/models |\n| model_control_mode | MODE_EXPLICIT |\n| startup_models_0 | maxine_nvcf_studiovoice |\n| startup_models_1 | studio_voice_high_quality-48k |\n| strict_model_config | 0 |\n| model_config_name | |\n| rate_limit | OFF |\n| pinned_memory_pool_byte_size | 268435456 |\n| cuda_memory_pool_byte_size{0} | 67108864 |\n| min_supported_compute_capability | 6.0 |\n| strict_readiness | 1 |\n| exit_timeout | 30 |\n| cache_enabled | 0 |\n+----------------------------------+------------------------------------------------------------------------------------------------+\n\nI1126 09:22:21.048202 31 grpc_server.cc:2558] \"Started GRPCInferenceService at 127.0.0.1:9001\"\nI1126 09:22:21.048377 31 http_server.cc:4704] \"Started HTTPService at 127.0.0.1:9000\"\nI1126 09:22:21.089295 31 http_server.cc:362] \"Started Metrics Service at 127.0.0.1:9002\"\nMaxine GRPC Service: Listening to 0.0.0.0:8001\n```\n\nBy default Maxine Studio Voice gRPC service is hosted on port `8001`. You will have to use this port for inferencing requests.\n\nWe have provided a sample client script file in our GitHub repo.\nThe script could be used to invoke the Docker container using the following instructions.\n\nDownload the Maxine Studio Voice Python client code by cloning the [NVIDIA Maxine NIM Clients Repository](https://github.com/NVIDIA-Maxine/nim-clients):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/studio-voice\n```\n\nInstall the dependencies for the NVIDIA Maxine Studio Voice Python client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\nGo to scripts directory\n```bash\ncd scripts\n```\n\nRun the command to send gRPC request\n```bash\npython studio_voice.py --target \u003ctarget_ip:port\u003e --input \u003cinput_file_path\u003e --output \u003coutput_file_path\u003e\n```\n\nTo view details of command line arguments run this command\n```bash\npython studio_voice.py -h\n```\nYou will get a response similar to the following.\n```bash\nusage: studio_voice.py [-h] [--target TARGET] [--input INPUT] [--output OUTPUT]\n\nProcess wav audio files using gRPC and apply Studio Voice.\n\noptions:\n -h, --help show this help message and exit\n --target TARGET The target gRPC server address.\n --input INPUT The path to the input audio file.\n --output OUTPUT The path for the output audio file.\n```\n\nFor more details on getting started with this NIM including configuring using parameters, visit the [NVIDIA Maxine Studio Voice NIM Docs](https://docs.nvidia.com/nim/maxine/studio-voice/latest/index.html).\n"])</script><script>self.__next_f.push([1,"32:T7f3,## Getting Started\n\nNVIDIA Maxine Studio Voice NIM uses gRPC APIs for inferencing requests. Following instructions demonstrate the usage of Maxine Studio Voice NIM model using Python client.\n\n### Prerequisites\n\nYou will need a system with `git` and `Python 3.10+` installed.\n\n### Setup NVIDIA Maxine Studio Voice Python client\n\nDownload the Maxine Studio Voice Python client code by cloning the [NVIDIA Maxine NIM Clients Repository](https://github.com/NVIDIA-Maxine/nim-clients):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/studio-voice\n```\n\nInstall the dependencies for the NVIDIA Maxine Studio Voice Python client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\n\n### Run Python Client\n\nNavigate to the scripts directory.\n\n```bash\ncd scripts\n```\n\nSend the gRPC requests\n\n```bash\npython studio_voice.py --use-ssl \\\n --target grpc.nvcf.nvidia.com:443 \\\n --function-id 7cf12edb-2181-4947-8b19-2b1c18270588 \\\n --api-key \u003c%- apiKey %\u003e \\\n --input \u003cinput_file_path\u003e \\\n --output \u003coutput_file_path\u003e\n```\n\nNote the requirements for input file:\n\n- The supported format is 16-bit mono channel wav file.\n- The size limit for input file is 32 MB.\n- The duration limit for input file is 6 min.\n\nCommand line arguments:\n\n- `--use-ssl` - Flag to control SSL/TLS encryption enablement.\n- `--target \u003cip:port\u003e` - URI of NIM's gRPC service. Use grpc.nvcf.nvidia.com:443 when hosted on NVCF. (Default: `127.0.0.1:8001`)\n- `--api-key \u003c%- apiKey %\u003e` - NGC API key required for authentication. Utilized when using `TRY API` ignored otherwise.\n- `--function-id \u003cfunction_id\u003e` - Function ID for the feature.\n- `--input \u003cinput_file_path\u003e` - The path to the input audio file. (Default: `../assets/studio_voice_48k_input.wav`)\n- `--output \u003coutput_file_path\u003e` - The path to the output audio file. (Default: `./studio_voice_48k_output.wav`)\n\nRefer the [Maxine Studio Voice NIM documentation](https://docs.nvidia.com/nim/maxine/studio-voice/latest/index.html) for more information.\n33:{\""])</script><script>self.__next_f.push([1,"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/maxine-studio-voice\"}\n37:T1cb7,"])</script><script>self.__next_f.push([1,"NVIDIA Maxine Studio Voice NIM uses gRPC APIs for inferencing requests.\n\nA NGC API KEY is required to download the appropriate models and resources when starting the NIM.\n\nIf you are not familiar with how to create the `NGC_API_KEY` environment variable, the simplest way is to export it in your terminal:\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n```\n\nRun one of the following commands to make the key available at startup:\n\n```bash\n# If using bash\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.bashrc\n\n# If using zsh\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.zshrc\n```\n\nOther, more secure options include saving the value in a file, so that you can retrieve with `cat $NGC_API_KEY_FILE`, or using a [password manager](https://www.passwordstore.org/).\n\nThe following command launches the Maxine Studio Voice NIM container with the gRPC service. Find reference to runtime parameters for the container [here](https://docs.nvidia.com/nim/maxine/studio-voice/latest/getting-started.html#runtime-parameters-for-the-container).\n\n```bash\ndocker run -it --rm --name=maxine-studio-voice \\\n --net host \\\n --runtime=nvidia \\\n --gpus all \\\n --shm-size=8GB \\\n -e NGC_API_KEY=$NGC_API_KEY \\\n -e NIM_MODEL_PROFILE=\u003cnim_model_profile\u003e \\\n -e FILE_SIZE_LIMIT=36700160 \\\n nvcr.io/nim/nvidia/maxine-studio-voice:latest\n```\nEnsure you use the appropriate `NIM_MODEL_PROFILE` for your GPU. For more information about `NIM_MODEL_PROFILE`, refer to the the [NIM Model Profile Table](https://docs.nvidia.com/nim/maxine/studio-voice/latest/model-profile-table.html).\n\nPlease note, the flag --gpus all is used to assign all available GPUs to the docker container. This fails on multiple GPU unless all GPUs are same. To assign specific GPU to the docker container (in case of different multiple GPUs available in your machine) use --gpus '\"device=0,1,2...\"'\n\"'`\n\nIf the command runs successfully, you get a response similar to the following.\n\n```bash\n+-------------------------------+---------+--------+\n| Model | Version | Status |\n+-------------------------------+---------+--------+\n| maxine_nvcf_studiovoice | 1 | READY |\n| studio_voice_high_quality-48k | 1 | READY |\n+-------------------------------+---------+--------+\n\nI1126 09:22:21.040917 31 metrics.cc:877] \"Collecting metrics for GPU 0: NVIDIA GeForce RTX 4090\"\nI1126 09:22:21.046137 31 metrics.cc:770] \"Collecting CPU metrics\"\nI1126 09:22:21.046253 31 tritonserver.cc:2598] \n+----------------------------------+------------------------------------------------------------------------------------------------+\n| Option | Value |\n+----------------------------------+------------------------------------------------------------------------------------------------+\n| server_id | triton |\n| server_version | 2.50.0 |\n| server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy m |\n| | odel_configuration system_shared_memory cuda_shared_memory binary_tensor_data parameters stati |\n| | stics trace logging |\n| model_repository_path[0] | /opt/maxine/models |\n| model_control_mode | MODE_EXPLICIT |\n| startup_models_0 | maxine_nvcf_studiovoice |\n| startup_models_1 | studio_voice_high_quality-48k |\n| strict_model_config | 0 |\n| model_config_name | |\n| rate_limit | OFF |\n| pinned_memory_pool_byte_size | 268435456 |\n| cuda_memory_pool_byte_size{0} | 67108864 |\n| min_supported_compute_capability | 6.0 |\n| strict_readiness | 1 |\n| exit_timeout | 30 |\n| cache_enabled | 0 |\n+----------------------------------+------------------------------------------------------------------------------------------------+\n\nI1126 09:22:21.048202 31 grpc_server.cc:2558] \"Started GRPCInferenceService at 127.0.0.1:9001\"\nI1126 09:22:21.048377 31 http_server.cc:4704] \"Started HTTPService at 127.0.0.1:9000\"\nI1126 09:22:21.089295 31 http_server.cc:362] \"Started Metrics Service at 127.0.0.1:9002\"\nMaxine GRPC Service: Listening to 0.0.0.0:8001\n```\n\nBy default Maxine Studio Voice gRPC service is hosted on port `8001`. You will have to use this port for inferencing requests.\n\nWe have provided a sample client script file in our GitHub repo.\nThe script could be used to invoke the Docker container using the following instructions.\n\nDownload the Maxine Studio Voice Python client code by cloning the [NVIDIA Maxine NIM Clients Repository](https://github.com/NVIDIA-Maxine/nim-clients):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/studio-voice\n```\n\nInstall the dependencies for the NVIDIA Maxine Studio Voice Python client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\nGo to scripts directory\n```bash\ncd scripts\n```\n\nRun the command to send gRPC request\n```bash\npython studio_voice.py --target \u003ctarget_ip:port\u003e --input \u003cinput_file_path\u003e --output \u003coutput_file_path\u003e\n```\n\nTo view details of command line arguments run this command\n```bash\npython studio_voice.py -h\n```\nYou will get a response similar to the following.\n```bash\nusage: studio_voice.py [-h] [--target TARGET] [--input INPUT] [--output OUTPUT]\n\nProcess wav audio files using gRPC and apply Studio Voice.\n\noptions:\n -h, --help show this help message and exit\n --target TARGET The target gRPC server address.\n --input INPUT The path to the input audio file.\n --output OUTPUT The path for the output audio file.\n```\n\nFor more details on getting started with this NIM including configuring using parameters, visit the [NVIDIA Maxine Studio Voice NIM Docs](https://docs.nvidia.com/nim/maxine/studio-voice/latest/index.html).\n"])</script><script>self.__next_f.push([1,"38:T7f3,## Getting Started\n\nNVIDIA Maxine Studio Voice NIM uses gRPC APIs for inferencing requests. Following instructions demonstrate the usage of Maxine Studio Voice NIM model using Python client.\n\n### Prerequisites\n\nYou will need a system with `git` and `Python 3.10+` installed.\n\n### Setup NVIDIA Maxine Studio Voice Python client\n\nDownload the Maxine Studio Voice Python client code by cloning the [NVIDIA Maxine NIM Clients Repository](https://github.com/NVIDIA-Maxine/nim-clients):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/studio-voice\n```\n\nInstall the dependencies for the NVIDIA Maxine Studio Voice Python client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\n\n### Run Python Client\n\nNavigate to the scripts directory.\n\n```bash\ncd scripts\n```\n\nSend the gRPC requests\n\n```bash\npython studio_voice.py --use-ssl \\\n --target grpc.nvcf.nvidia.com:443 \\\n --function-id 7cf12edb-2181-4947-8b19-2b1c18270588 \\\n --api-key \u003c%- apiKey %\u003e \\\n --input \u003cinput_file_path\u003e \\\n --output \u003coutput_file_path\u003e\n```\n\nNote the requirements for input file:\n\n- The supported format is 16-bit mono channel wav file.\n- The size limit for input file is 32 MB.\n- The duration limit for input file is 6 min.\n\nCommand line arguments:\n\n- `--use-ssl` - Flag to control SSL/TLS encryption enablement.\n- `--target \u003cip:port\u003e` - URI of NIM's gRPC service. Use grpc.nvcf.nvidia.com:443 when hosted on NVCF. (Default: `127.0.0.1:8001`)\n- `--api-key \u003c%- apiKey %\u003e` - NGC API key required for authentication. Utilized when using `TRY API` ignored otherwise.\n- `--function-id \u003cfunction_id\u003e` - Function ID for the feature.\n- `--input \u003cinput_file_path\u003e` - The path to the input audio file. (Default: `../assets/studio_voice_48k_input.wav`)\n- `--output \u003coutput_file_path\u003e` - The path to the output audio file. (Default: `./studio_voice_48k_output.wav`)\n\nRefer the [Maxine Studio Voice NIM documentation](https://docs.nvidia.com/nim/maxine/studio-voice/latest/index.html) for more information.\n36:{\""])</script><script>self.__next_f.push([1,"dockerRun\":\"$37\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"usage\":\"$38\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.nvidia.com/nim/maxine/studio-voice/latest/index.html\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; 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