Conformer-CTC (around 120M parameters) is trained on ASRSet with over 35000 hours of English(en-US) speech. The model transcribes speech in lower case english alphabet along with spaces and apostrophes.
Conformer-CTC  model is a non-autoregressive variant of Conformer model  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.
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.
Audio sample that is to be transcribed
This model provides transcribed speech as a string for a given audio sample.
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