Speech Recognition: Parakeet

Description

RIVA Parakeet-CTC-XXL-1.1B ASR English Channel Robust (around 1.1B parameters) [1] is trained on ASRSet with over 150,000 hours of English (en-US) speech with telephonic/narrowband augmentation. The model is robust to telephonic speech data. The model transcribes speech in the lower case English alphabet, along with spaces and apostrophes.

This model is ready for commercial use.

License/Terms of Use

GOVERNING TERMS: The use of this model is governed by the NVIDIA Community Model License (found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).

References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
\n[2] Fast-Conformer-CTC Model
\n[3] 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 String in English
\nOutput Format(s): 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 2.19.0 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_spe1024_en-US_8.2

Training & Evaluation

Training Dataset

** Data Collection Method by dataset

Human

** Labeling Method by dataset

Human, Synthetic labels generated by OpenAI's opensource Whisper-v3 model

Properties (Quantity, Dataset Descriptions, Sensor(s)):

In excess of 150,000 hours of English (en-US) speech comprised of a dynamic blend of public and internal proprietary datasets normalized to have lower-cased, unpunctuated, and spoken forms in text.

Evaluation Dataset

** Data Collection Method by dataset

Human

** Labeling Method by dataset

Human

Properties (Quantity, Dataset Descriptions, Sensor(s)):

A dynamic blend of public, internal proprietary, and customer datasets normalized to have lower-cased, unpunctuated, 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] [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 String in English
\n**Output Format(s):** 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 2.19.0 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_spe1024_en-US_8.2\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, Synthetic labels generated by OpenAI's opensource Whisper-v3 model
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**
\n\nIn excess of 150,000 hours of English (en-US) speech comprised of a dynamic blend of public and internal proprietary datasets normalized to have lower-cased, unpunctuated, 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 (Quantity, Dataset Descriptions, Sensor(s)):**\n\nA dynamic blend of public, internal proprietary, and customer datasets normalized to have lower-cased, unpunctuated, and spoken forms in text.\n\n## Inference\n\n**Engine:** Triton
\n**Test Hardware:**
\n* NVIDIA A10 \n* NVIDIA A100 \n* NVIDIA A30 \n* NVIDIA H100 \n* NVIDIA Jetson Orin\n* NVIDIA L4 \n* NVIDIA L40 \n* NVIDIA Turing T4\n* NVIDIA Volta V100\n\n## Ethical Considerations\n\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.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/models/parakeet-ctc-riva-1-1b-en-us-tele/bias).\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","RIVA Parakeet-CTC-XXL-1.1B ASR English - ASR set 8.1",["D",1742375113028],"trainable_v8.2","/api/image/nvidia/riva/models/parakeet-ctc-riva-1-1b-en-us-tele","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",3982174933,"bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nWhat is the language balance of the model validation data? | English: 100%\nWhat is the geographic origin language balance of the model validation data? | United States: 80%, United Kingdom: 10%, Others: 10%\nWhat is the accent balance of the model validation data? | American English: 80%, British English: 10%, Others: 10%\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Age, Gender, Linguistic Background\nMeasures taken to mitigate against unwanted bias: | Used custom dataset to validate model performance across gender, age, and linguistic demographics","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications & Domains: | Speech Transcription\nModel Type: | Speech Transcription\nIntended Users: | This model is intended for developers and data scientists building interactive call centers, virtual assistants, and language learning assistants\nOutput: | Transcribed text with timestamps and confidence scores\nDescribe how the model works: | Model transcribes audio input into text for the input language\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Gender, National Origin\nTechnical Limitations: | Transcripts may not be 100% accurate. Accuracy varies based on the characteristics of input audio (Domain, Use Case, Accent, Noise, Speech Type, Context of speech, etc.)\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Word Error Rate (WER), Silence Robustness (Characters/mins of silent audio), Latency (in milliseconds), Throughput (Total audio processed per unit of time)\nPotential Known Risks: | Not recommended for word-for-word transcription as accuracy varies based on the characteristics of input audio (domain, use case, accent, noise, speech type, and context of speech)\nLicensing: | [https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | None\nPersonal data used to create this model? | None\nHow often is dataset reviewed? | Before Release\nIs there data provenance for all datasets used in training? | 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? | The data is compliant where applicable, but is not applicable for all data.","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Speech Transcription\nDescribe the life-critical impacts (if present). | Not Applicable\nUse Case Restriction(s): | Abide by [https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)\nDescribe access restrictions (if any): | The Principle of Least Privilege (PoLP) is applied limiting access for dataset generation and model development. 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