# English ASR

The Moonshine model is an automatic speech recognition (ASR) model developed by Useful Sensors, focusing on efficient English speech transcription on resource-constrained devices.

This is an end-to-end automatic speech recognition system trained on the LibriSpeech dataset, combining the wav2vec 2.0 pre-trained model and CTC technology, excelling in English speech recognition tasks.

A large-scale English automatic speech recognition model based on the Wav2Vec2 architecture, fine-tuned on 4500 hours of multi-source speech data, supporting decoding with a language model

A speech-to-text (S2T) model for automatic speech recognition (ASR), based on a sequence-to-sequence transformer architecture

An automatic speech recognition (ASR) model trained on the ESPnet framework, using the Conformer architecture and the WavLM large pre-trained model, trained on the LibriSpeech dataset.

This is an automatic speech recognition (ASR) pretrained model based on the ESPnet2 framework, trained on the mini-an4 dataset and supports English speech recognition.

This is an automatic speech recognition (ASR) system based on Transformer architecture, combining CTC and Transformer decoder, trained on the LibriSpeech English dataset.

SEW (Squeezed and Efficient Wav2vec) is a speech recognition pre-trained model developed by ASAPP Research, outperforming wav2vec 2.0 in both performance and efficiency.