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Speech Recognition: EMEA N-Gram Language Models

Description

Riva ASR EMEA LM estimates the likelihood of word sequence in six languages including Arabic (ar-AR), German (de-DE), British English (en-GB), European Spanish (es-ES), French (fr-FR), Italian (it-IT). The model is trained on normalized text, where numbers are converted to their spoken forms, and punctuation and capitalization are included. When deployed, the ASR engine can optionally condition the transcript output on n-gram language models.

This model is ready for commercial use.

License/Terms of Use

NVIDIA AI Foundation Models Community License Agreement

References

KenLM

Model Architecture

Architecture Type: n-gram
\nNetwork Architecture: 4-gram trained with Kneser-Ney smoothing

Input

Input Type(s): Text in Language
\nInput Format(s): String
\nInput Parameters: 1-Dimension\nOther Properties Related to Input: Sentences of any length, lower-cased and unpunctuated text can be provided.

Output

Output Type(s): Likelihood of word sequence
\nOutput Format: Float
\nOutput Parameters: 1-Dimension
\nOther Properties Related to Output: The output represents the log-probability of any given sentence under the loaded language model.

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.

There are a variety of formats contained within this model archive:

ARPA-formatted Language Models:

ml_cs_em-ea_default_pnc_1.0.arpa

KenLM-formatted Binary Language Models

ml_cs_em-ea_default_pnc_1.0.bin

Vocabulary Files

ml_cs_em-ea_default_pnc_1.0_dict_vocab.txt

ARPA and KenLM binary formatted files can be used directly by the CTC CPU Decoder.

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)

ml_cs_em-ea_default_pnc_1.0

Training & Evaluation

Training 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 punctuated, capitalized and spoken forms in the 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 punctuated, capitalized and spoken forms in the 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\n## License/Terms of Use \n\n[NVIDIA AI Foundation Models Community License Agreement](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)\n\n## References\n\n[KenLM](https://github.com/kpu/kenlm) \n\n## Model Architecture\n \n**Architecture Type:** n-gram
\n**Network Architecture:** 4-gram trained with Kneser-Ney smoothing
\n\n## Input\n\n**Input Type(s):** Text in Language
\n**Input Format(s):** String
\n**Input Parameters:** 1-Dimension\n**Other Properties Related to Input:** Sentences of any length, lower-cased and unpunctuated text can be provided.
\n\n## Output\n\n**Output Type(s):** Likelihood of word sequence
\n**Output Format:** Float
\n**Output Parameters:** 1-Dimension
\n**Other Properties Related to Output:** The output represents the log-probability of any given sentence under the loaded language model.
\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\nThere are a variety of formats contained within this model archive:\n\n**ARPA-formatted Language Models:**\n- `ml_cs_em-ea_default_pnc_1.0.arpa`\n\n**KenLM-formatted Binary Language Models**\n- `ml_cs_em-ea_default_pnc_1.0.bin`\n\n**Vocabulary Files**\n- `ml_cs_em-ea_default_pnc_1.0_dict_vocab.txt`\n\nARPA and KenLM binary formatted files can be used directly by the CTC CPU Decoder.\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\nml_cs_em-ea_default_pnc_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\nA dynamic blend of public and internal proprietary and customer datasets normalized to have punctuated, capitalized and spoken forms in the 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 punctuated, capitalized and spoken forms in the text.\n\n## Inference\n\n**Engine:** Triton
\n**Test Hardware:**
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These cookies may store a unique ID so that our system will remember you when you return. 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