# English speech transcription

Parakeet CTC 1.1B is an automatic speech recognition model jointly developed by NVIDIA NeMo and Suno.ai, based on the FastConformer architecture with approximately 1.1 billion parameters, supporting English speech transcription.

Parakeet RNNT 1.1B is an automatic speech recognition model jointly developed by NVIDIA NeMo and Suno.ai, based on the FastConformer Transducer architecture with approximately 1.1 billion parameters, supporting English speech transcription.

This is a Whisper base.en model converted based on CTranslate2, used for English speech recognition tasks.

This is a large automatic speech recognition (ASR) model based on the FastConformer architecture, specifically designed for transcribing English speech into text.

ONNX weight version of OpenAI Whisper-tiny.en model, designed for Transformers.js, used for English speech transcription.

This is an Automatic Speech Recognition (ASR) model developed by NVIDIA, based on the Conformer-Transducer architecture, with approximately 600 million parameters, specifically designed for English speech transcription.

A speech-to-text (S2T) model for automatic speech recognition (ASR)

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

s2t-small-librispeech-asr is a speech-to-text (S2T) model for automatic speech recognition (ASR), based on a sequence-to-sequence transformer architecture.

An automatic speech recognition (ASR) system based on Facebook's wav2vec2-large-960h-lv60-self model, improved with an enhanced Wikipedia language model

Based on Facebook's Wav2Vec2-Large-960h-lv60-self model, enhanced with an English 4-gram language model to improve speech recognition accuracy

Based on Facebook's Wav2Vec2-Base-960h model, with an added English 4-gram language model to improve automatic speech recognition (ASR) accuracy.

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

An automatic speech recognition model fine-tuned on the LIBRISPEECH_ASR - CLEAN dataset based on microsoft/wavlm-base

A lightweight automatic speech recognition (ASR) model, based on a randomly initialized version of the Wav2Vec2 architecture, designed for robustness testing.

The Wav2Vec2 large model developed by Facebook, pre-trained and fine-tuned on 960 hours of Libri-Light and Librispeech audio data, using self-training objectives, achieving SOTA results on the LibriSpeech test set.

Wav2Vec2 is a self-supervised learning-based speech recognition model developed by Facebook, trained on the LibriSpeech dataset, supporting English speech-to-text tasks.