Citrinet-1024 model which has been trained on the ASR dataset with around 1900 hours of Hindi(hi-IN) speech. It utilizes a Google SentencePiece  tokenizer with vocabulary size 1024, and transcribes text in lower case hindi alphabet along with space.
Citrinet is a deep residual convolutional neural network architecture that is optimized for Automatic Speech Recognition tasks. There are many variants of the Citrinet family of models, which are further discussed in the paper .
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.
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.
Audio sample that is to be transcribed
This model provides transcribed speech as a string for a given audio sample.
 Google Sentencepiece Tokenizer
 Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
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