  This paper presents an overview and evaluation of some of the end-to-end ASR
models on long-form audios. We study three categories of Automatic Speech
Recognition(ASR) models based on their core architecture: (1) convolutional,
(2) convolutional with squeeze-and-excitation and (3) convolutional models with
attention. We selected one ASR model from each category and evaluated Word
Error Rate, maximum audio length and real-time factor for each model on a
variety of long audio benchmarks: Earnings-21 and 22, CORAAL, and TED-LIUM3.
The model from the category of self-attention with local attention and global
token has the best accuracy comparing to other architectures. We also compared
models with CTC and RNNT decoders and showed that CTC-based models are more
robust and efficient than RNNT on long form audio.
