whisper-medium-gguf is an open-source automatic speech recognition model based on OpenAI's Whisper, supporting transcription and translation in 99 languages. Below are 14 voice, tts & speech apps with similar functionality to Whisper Medium, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
whisper-large-v3-turbo is an open-source automatic speech recognition (ASR) model designed for transcribing audio into text. It supports multiple languages and offers fast, accurate transcription for developers and researchers working on speech processing applications. The model is distributed under an open license for local deployment.
whisper-large-v3-turbo is an open-source automatic speech recognition model for transcribing audio to text. It is available for local inference via CLI and is intended for AI researchers and developers.
whisper-large-v3-turbo-mlx-8bit is an open-source speech recognition model based on Whisper, quantized for efficient on-device inference using MLX. It enables developers to perform speech-to-text transcription locally, making it suitable for privacy-focused or offline applications.
whisper-large-v3-onnx is an open-source ONNX-converted version of the Whisper large v3 model for automatic speech recognition. It enables developers to run speech-to-text inference locally or integrate with JavaScript environments using transformers.js. Suitable for building custom ASR solutions.
whisper.cpp is an open-source implementation of OpenAI's Whisper speech recognition models, converted for efficient local and server-side inference. It allows developers to transcribe audio to text without cloud dependencies, supporting multiple model sizes and formats.
Whisper Large V2 is a web application that transcribes audio files into text using a large-scale speech recognition model. Users can upload audio and receive accurate transcriptions, making it useful for transcription tasks and accessibility.
Whisper is a web application that enables users to transcribe or translate spoken audio into written text. Users can upload audio files, record directly, or provide YouTube links, and receive accurate transcriptions or translations. It is ideal for content creators, journalists, and researchers.
Whisper Large V3 is a web application that converts spoken audio from recordings, uploads, or YouTube links into written text. It supports both transcription and translation, making it useful for content creators, researchers, and anyone needing accurate speech-to-text conversion.
faster-whisper-large-v3 is an open-source automatic speech recognition model converted for use with CTranslate2. It supports 100 languages and is suitable for developers and researchers building local or embedded speech-to-text solutions.
Whisper Turbo is a web app that transcribes or translates spoken content from audio files, microphone recordings, or YouTube links into text. It is designed for content creators, researchers, and anyone needing fast and accurate speech-to-text conversion.
Whisper Webui is a web-based application that enables users to transcribe audio files into text using AI models. It supports multiple languages and provides downloadable transcriptions, ideal for journalists and researchers.
Whisper For Large Audio is a browser-based tool that allows users to upload and partition large audio files, then transcribe them using OpenAI Whisper. It is designed for content creators and researchers who need accurate, scalable audio transcription.
moonshine-streaming-medium-gguf is an open-source automatic speech recognition (ASR) model distributed in GGUF format for use with transcribe.cpp and similar tools. It enables developers to transcribe English audio to text efficiently on local devices. The model is suitable for building speech-to-text applications and supports quantized weights for performance.
Qwen3-ASR-0.6B-gguf is an open source automatic speech recognition model supporting over 30 languages. It is designed for developers and researchers needing accurate speech-to-text transcription in multilingual contexts.
