# Efficient Speech Recognition

This is an automatic speech recognition model that has been converted to a version suitable for MLX and can perform inference quickly.

Distil-Whisper is a distilled version of the Whisper model, optimized for Automatic Speech Recognition (ASR) tasks, offering faster inference speeds.

A CTranslate2 format model converted from Distil-Whisper large-v3.5 for efficient speech recognition

Distil-Whisper is a knowledge-distilled version of OpenAI Whisper-Large-v3, offering superior performance and efficiency.

Distil-Whisper is a distilled version of the Whisper model, achieving efficient speech recognition through large-scale pseudo-labeling technology

Distil-Whisper is a knowledge-distilled version of OpenAI Whisper-Large-v3, achieving efficient speech recognition through large-scale pseudo-label training.

This is an optimized Hebrew and English speech recognition model based on the Whisper model, developed by ivrit.ai.

Distil-Whisper is a knowledge-distilled version of Whisper large-v3, focusing on English automatic speech recognition, offering faster inference speeds while maintaining accuracy close to the original model.

Parakeet TDT 1.1B is an automatic speech recognition (ASR) model jointly developed by NVIDIA NeMo and Suno.ai, capable of transcribing speech into lowercase English letters.

This is a version of the distil-whisper/distil-medium.en model converted to CTranslate2 format for efficient speech recognition tasks.

This is a distilled version of the automatic speech recognition (ASR) model based on the Whisper architecture, designed for efficient inference and suitable for English speech-to-text tasks.

SEW-D-mid is a speech pre-training model developed by ASAPP Research, focusing on automatic speech recognition tasks, achieving a good balance between performance and efficiency.

SEW-D-tiny is an efficient speech recognition pre-trained model developed by ASAPP Research, focusing on the balance between performance and efficiency.

SEW-tiny is a compressed and efficient speech pretraining model developed by ASAPP Research, pretrained on 16kHz sampled speech audio, suitable for various downstream speech tasks.

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.

SEW-D-base+ is an efficient speech recognition model developed by ASAPP Research, pre-trained on 16kHz sampled speech audio, and excels on the LibriSpeech dataset.

SEW-D-mid-k127 is an efficient speech recognition pre-trained model developed by ASAPP Research, demonstrating significant improvements in performance and efficiency compared to wav2vec 2.0.