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Speech Recognition: Parakeet

Description

RIVA Parakeet-CTC-XXL-1.1B ASR Multilingual with Concat Tokenizer (around 1.1B parameters) [1], [2] is trained on ASR Set with over 90,000 hours of speech. Individual tokenizers are trained for each language and then combined into a single tokenizer during model training [3]. The model transcribes 25 languages (English(en-US, en-GB), Spanish (es-US, es-ES), German (de-DE), French (fr-FR, fr-CA), Italian (it-IT), Arabic(ar-AR), Japanese (ja-JP), Korean (ko-KR), Portuguese (pt-BR, pt-PT), Russian (ru-RU), Hindi (hi-IN), Dutch (nl-NL), Danish (da-DK), Norwegian Nynorsk (nn-NO), Norwegian Bokmal (nb-NO), Czech (cs-CZ), Polish (pl-PL), Swedish (sv-SE), Thai (th-TH), Turkish (tr-TR), Hebrew (he-IL)) in upper case and lower case alphabets along with punctuations, spaces, and apostrophes.

This model is ready for commercial use.

License/Terms of Use

NVIDIA AI Foundation Models Community License Agreement

References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
\n[2] Fast-Conformer-CTC Model
\n[3] Multilingual ASR models with Subword Tokenization in NeMo\n[4] Conformer: Convolution-augmented Transformer for Speech Recognition

Model Architecture

Architecture Type: Parakeet-CTC (also known as FastConformer-CTC) [1], [2] which is an optimized version of Conformer model [3] with 8x depthwise-separable convolutional downsampling with CTC loss
\nNetwork Architecture: Parakeet-CTC-XXL-1.1B

Input

Input Type(s): Audio
\nInput Format(s): wav
\nInput Parameters: 1-Dimension
\nOther Properties Related to Input: Maximum Length in seconds specific to GPU Memory, No Pre-Processing Needed, Mono channel is required

Output

Output Type(s): Text
\nOutput Format: String
\nOutput Parameters: 1-Dimension
\nOther Properties Related to Output: No Maximum Character Length, Does not handle special characters

How to Use this Model

The Riva Quick Start Guide is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, see the Riva User Guide.

Software Integration

Runtime Engine(s):

Riva 2.18.0 or higher

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Jetson
NVIDIA Turing
NVIDIA Volta

[Preferred/Supported] Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

Parakeet-CTC-XXL-1.1b_concat_spe12k_1.0

Training & Evaluation

Training Dataset

** Data Collection Method by dataset

Human

** Labeling Method by dataset

Human

Properties:

This model is trained on over 90,000 hours of speech in 25 languages (English(US, GB), Spanish(US, ES), German, French, Italian, Arabic, Japanese, Korean, Portuguese (Brazil), Russian, Hindi, French (Canada), Dutch, Danish, Norwegian Nynorsk, Norwegian Bokmal, Czech, Polish, Swedish, Thai, Turkish, Portuguese(Portugal), Hebrew) speech comprised of a dynamic blend of public and internal proprietary and customer datasets normalized to have upper-cased, lower-cased, punctuated, and spoken forms in text.

Evaluation Dataset

** Data Collection Method by dataset

Human

** Labeling Method by dataset

Human

Properties:

A dynamic blend of public and internal proprietary and customer datasets normalized to have upper-cased, lower-cased, punctuated, and spoken forms in text.

Inference

Engine: Triton
\nTest Hardware:

NVIDIA A10
NVIDIA A100
NVIDIA A30
NVIDIA H100
NVIDIA Jetson Orin
NVIDIA L4
NVIDIA L40
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 here.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n[2] [Fast-Conformer-CTC Model](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html)
\n[3] [Multilingual ASR models with Subword Tokenization in NeMo](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Multilang_ASR.ipynb)\n[4] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
\n\n## Model Architecture\n\n**Architecture Type:** Parakeet-CTC (also known as FastConformer-CTC) [1], [2] which is an optimized version of Conformer model [3] with 8x depthwise-separable convolutional downsampling with CTC loss
\n**Network Architecture:** Parakeet-CTC-XXL-1.1B
\n\n## Input \n\n**Input Type(s):** Audio
\n**Input Format(s):** wav
\n**Input Parameters:** 1-Dimension
\n**Other Properties Related to Input:** Maximum Length in seconds specific to GPU Memory, No Pre-Processing Needed, Mono channel is required
\n\n## Output \n\n**Output Type(s):** Text
\n**Output Format:** String
\n**Output Parameters:** 1-Dimension
\n**Other Properties Related to Output:** No Maximum Character Length, Does not handle special characters
\n\n## How to Use this Model\n\nThe [Riva Quick Start Guide](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart) is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, see the [Riva User Guide](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html).\n\n## Suggested Reading\n\nRefer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n## Software Integration\n\n**Runtime Engine(s):** \n* Riva 2.18.0 or higher
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA Jetson
\n* NVIDIA Turing
\n* NVIDIA Volta
\n\n**[Preferred/Supported] Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \n\nParakeet-CTC-XXL-1.1b_concat_spe12k_1.0\n\n# Training & Evaluation \n\n## Training Dataset\n\n** Data Collection Method by dataset
\n\n* Human
\n\n** Labeling Method by dataset
\n\n* Human
\n\n**Properties:**
\n\nThis model is trained on over 90,000 hours of speech in 25 languages (English(US, GB), Spanish(US, ES), German, French, Italian, Arabic, Japanese, Korean, Portuguese (Brazil), Russian, Hindi, French (Canada), Dutch, Danish, Norwegian Nynorsk, Norwegian Bokmal, Czech, Polish, Swedish, Thai, Turkish, Portuguese(Portugal), Hebrew) speech comprised of a dynamic blend of public and internal proprietary and customer datasets normalized to have upper-cased, lower-cased, punctuated, and spoken forms in text.\n\n## Evaluation Dataset\n\n** Data Collection Method by dataset
\n\n* Human
\n\n** Labeling Method by dataset
\n\n* Human
\n\n**Properties:**\n\nA dynamic blend of public and internal proprietary and customer datasets normalized to have upper-cased, lower-cased, punctuated, and spoken forms in text.\n\n## Inference\n\n**Engine:** Triton
\n**Test Hardware:**
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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498],[445,446,447,448,449],{"_284":359,"_121":359,"_119":14,"_257":469},{"_284":333,"_121":333,"_119":14,"_257":464},{"_284":459,"_121":459,"_119":14,"_257":460},{"_284":454,"_121":454,"_119":14,"_257":455},{"_284":450,"_121":450,"_119":14,"_257":451},"nvidia.partners",[452],{"_251":453,"_290":450,"_291":20,"_119":327},"edge","app.hushly.com",[456],{"_251":457,"_290":454,"_291":20,"_119":458},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[461],{"_251":462,"_290":459,"_291":20,"_119":463},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585],[537,538,539,540],{"_284":552,"_121":552,"_119":14,"_257":553},{"_284":547,"_121":547,"_119":14,"_257":548},{"_284":346,"_121":346,"_119":14,"_257":544},{"_284":459,"_121":459,"_119":14,"_257":541},[542],{"_251":543,"_290":459,"_291":85,"_119":463},"_gh_sess",[545],{"_251":546,"_290":346,"_291":85,"_119":353},"YSC","bizible.com",[549],{"_251":550,"_290":547,"_291":20,"_119":551},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[554],{"_251":555,"_290":552,"_291":20,"_119":556},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_251":627,"_290":405,"_291":20,"_119":628},{"_251":612,"_290":626,"_291":85,"_119":613},{"_251":595,"_290":408,"_291":20,"_119":520},{"_251":595,"_290":508,"_291":20,"_119":520},{"_251":624,"_290":405,"_291":20,"_119":625},{"_251":612,"_290":411,"_291":85,"_119":613},{"_251":622,"_290":405,"_291":20,"_119":623},{"_251":612,"_290":408,"_291":85,"_119":613},{"_251":620,"_290":507,"_291":20,"_119":621},{"_251":612,"_290":529,"_291":85,"_119":613},{"_251":592,"_290":408,"_291":20,"_119":594},{"_251":618,"_290":405,"_291":85,"_119":619},{"_251":616,"_290":405,"_291":20,"_119":617},{"_251":614,"_290":405,"_291":85,"_119":615},{"_251":612,"_290":503,"_291":85,"_119":613},{"_251":610,"_290":405,"_291":20,"_119":611},{"_251":608,"_290":405,"_291":20,"_119":609},{"_251":606,"_290":405,"_291":85,"_119":607},{"_251":604,"_290":512,"_291":20,"_119":605},{"_251":592,"_290":503,"_291":20,"_119":594},{"_251":602,"_290":405,"_291":20,"_119":603},{"_251":600,"_290":405,"_291":85,"_119":601},{"_251":598,"_290":405,"_291":20,"_119":599},{"_251":596,"_290":405,"_291":20,"_119":597},{"_251":595,"_290":405,"_291":20,"_119":520},{"_251":592,"_290":593,"_291":20,"_119":594},{"_251":590,"_290":405,"_291":20,"_119":591},{"_251":588,"_290":405,"_291":85,"_119":589},{"_251":586,"_290":405,"_291":20,"_119":587},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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