# Real-time Transcription

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

Whisper large-v3-turbo is a pruned and fine-tuned version based on Whisper large-v3, with the decoder layers reduced from 32 to 4, significantly improving speed while slightly reducing quality.

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

Lite-Whisper is a lightweight version of OpenAI Whisper compressed using LiteASR technology, maintaining high accuracy while reducing model size.

ONNX-format automatic speech recognition model based on the Moonshine base model, supporting efficient inference

Moonshine Tiny is a lightweight automatic speech recognition (ASR) model suitable for embedded devices and edge computing scenarios.

Moonshine is a series of automatic speech recognition (ASR) models developed by Useful Sensors, specifically designed for English speech transcription, excelling on resource-constrained platforms.

A speech recognition model fine-tuned on the Common Voice 11.0 Chinese dataset based on OpenAI Whisper Tiny model

Whisper is a general-purpose speech recognition model trained by OpenAI. This model is based on large-scale weakly supervised training and supports speech transcription in multiple languages.

Whisper is an automatic speech recognition (ASR) system trained by OpenAI, supporting multilingual speech transcription.

Distilled version of Whisper Large v3 for efficient automatic speech recognition (ASR)

Nue ASR is an end-to-end Japanese speech recognition model that integrates pre-trained speech and language models, offering high accuracy and fast recognition speed.

Distil-Whisper is a distilled version of the Whisper model, 6 times faster than the original, with a 49% reduction in size, while maintaining performance close to the original in English speech recognition tasks.

This model is a fine-tuned version of openai/whisper-small for speech recognition, supporting multiple languages and suitable for automatic speech recognition tasks.

Turkish speech recognition model fine-tuned based on OpenAI Whisper-small, with a word error rate of 26.34%

Whisper Medium is a medium-scale speech recognition model developed by OpenAI, supporting automatic speech recognition (ASR) tasks in multiple languages.

Whisper Small is a small automatic speech recognition (ASR) model developed by OpenAI, capable of converting speech into text.

Whisper is an automatic speech recognition (ASR) system trained by OpenAI, supporting speech-to-text tasks in multiple languages.

Transformer-based automatic speech recognition (ASR) model supporting multilingual transcription

A Japanese speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, supporting hiragana output

A fine-tuned speech recognition model based on Doogie/Waynehills-STT-doogie-server

Distil-wav2vec2 is a distilled version of the wav2vec2 model, with a 45% reduction in size and a two-fold increase in inference speed, suitable for automatic speech recognition tasks.