Model Overview
The model contains large size versions of Conformer-Transducer (around 120M parameters) trained on German NeMo ASRSet with over 2000 hours of speech. 
The model transcribes speech in lower case German alphabet along with spaces but without punctuation.

Architecture
Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding

Dataset
The model trained has been trained on a composite dataset (NeMo ASRSET) comprising of over two thousand hours of cleaned German speech: MCV7.0 567 hours MLS 1524 hours VoxPopuli 214 hours

Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input

Limitations
Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speec