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Speech Recognition: Parakeet

Description

RIVA Parakeet-CTC-XL-0.6B-Unified ASR Spanish-English (around 600M parameters) [1] is trained on ASR Set with over 28,000 hours of Spanish (es-US) and English (en-US) speech. The model transcribes speech in Spanish and English, in upper case and lower case alphabets, along with punctuations (period, comma, and question mark), 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] 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-0.6B

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 (in Spanish and English)
\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-XL-unified-0.6b_spe1024_es-en-US_3.0

Training & Evaluation

Training Dataset

** Data Collection Method by dataset

Human

** Labeling Method by dataset

Human

Properties:

This model is trained on over 28,000 hours of Spanish (es-US) and English (en-US) 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] [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-0.6B
\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 (in Spanish and English)
\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-XL-unified-0.6b_spe1024_es-en-US_3.0\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 28,000 hours of Spanish (es-US) and English (en-US) 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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