Speech Recognition: Conformer

Model Overview

Conformer-CTC (around 120M parameters) is trained on ASRSet with over 2800 hours of Spanish(es-US) speech. The model transcribes speech in lower case Spanish alphabet along with spaces and apostrophes. This model is ready for commercial use.

References

[1] Conformer: Convolution-augmented Transformer for Speech Recognition

Model Architecture

Conformer-CTC [1] model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: Conformer-CTC Model.

Training

The model was trained on various proprietary and open-source datasets. These datasets include variety of accents, domain specific data for various domains, spontaneous speech and dialogue, all of which contribute to the model’s accuracy. This model delivers WER that is better than or comparable to popular alternate Speech to Text solutions for a range of domains and use cases.

How to Use this Model

To use this model , we can use Riva Skills Quick start guide , it is a starting point to try out Riva models . Information regarding Quick start guide can be found : here. To use Riva Speech ASR service using this model , document has all the necessary information.

Input

Audio sample that is to be transcribed

Output

This model provides transcribed speech as a string for a given audio sample.

License

By downloading and using the models and resources packaged with Riva Conversational AI, you would be accepting the terms of the Riva license

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.