Faster Whisper Base.en Alternatives

Faster Whisper Small.en is a converted version of the OpenAI Whisper small.en model adapted for the CTranslate2 inference engine. It supports English automatic speech recognition tasks and is hosted on Hugging Face by Systran under the MIT license. The repository contains the model weights transformed from the original openai/whisper-small.en using the ct2-transformers-converter tool with float16 quantization. Model weights are stored in FP16 format, which can be adjusted at load time through the compute_type option. It is explicitly intended for use in CTranslate2 or any project built on CTranslate2, including the faster-whisper library. Usage follows the faster-whisper Python API. Code examples load the model with WhisperModel("small.en"), call transcribe on an audio file such as audio.mp3, and iterate over resulting segments that provide start time, end time, and transcribed text. The conversion process also copies the tokenizer.json file into the output directory. The model addresses efficient local inference for English speech-to-text on compatible hardware. It is delivered as a downloadable repository of model files on the Hugging Face platform rather than a standalone application.

This repository provides a CTranslate2 conversion of the distil-whisper/distil-medium.en model for fast English speech recognition. It is designed for use with faster-whisper and other CTranslate2-based projects, offering significantly faster inference than the original PyTorch model while maintaining accuracy.

Systran/faster-whisper-small is an open-source speech recognition model converted from OpenAI's Whisper, optimized for use with CTranslate2. It enables fast and accurate transcription of audio files into text for developers and researchers.

This is a conversion of OpenAI's Whisper large-v2 model to the CTranslate2 format for faster inference. It supports automatic speech recognition in 99 languages and can be used through the faster-whisper Python package. The model is provided in FP16 precision and is suitable for local transcription tasks.

faster-whisper-base-int8 is an open-source, quantized speech-to-text model designed for fast and efficient inference. It enables developers to transcribe audio to text with reduced resource requirements, making it suitable for real-time and embedded applications.

This repository provides the Whisper-medium model converted to CTranslate2 format for significantly faster inference. It can be used via the faster-whisper Python package. The model supports transcription across many languages and is intended for developers who require efficient, local speech recognition.

Xenova/whisper-base.en is a converted and optimized version of OpenAI's Whisper base model for English speech recognition. It is designed for use with Transformers.js and ONNX Runtime, enabling speech transcription to run locally in web browsers and Node.js environments. The model provides a lightweight alternative for English-only automatic speech recognition tasks.

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.

openai/whisper-base is an open-source automatic speech recognition (ASR) model that transcribes audio files into text. It supports multiple languages and is designed for developers and researchers working on speech-to-text applications. The model is easy to integrate into Python workflows.

faster-whisper-small-int8 is an optimized, INT8-quantized version of the OpenAI Whisper small model for speech-to-text. Created by the Rhasspy team, it is designed for efficient local inference with significantly reduced memory usage and faster performance while maintaining good transcription accuracy. It is available on Hugging Face for use with Transformers or faster-whisper libraries.

This repository provides the Whisper large-v3-turbo model converted to CTranslate2 format for use with faster-whisper and other CTranslate2-based projects. It supports transcription of audio in 100 languages with significantly improved inference speed. The model is intended for developers integrating fast, local speech recognition into applications.

The faster-whisper-large-v3-turbo model is a conversion of OpenAI's Whisper large-v3-turbo to the CTranslate2 format hosted on Hugging Face. It enables speech-to-text transcription through the faster-whisper library and other CTranslate2-based projects. The repository provides the converted model weights along with example code for loading and running transcription. A typical usage imports the WhisperModel class from faster_whisper, instantiates the model with the repository identifier, calls the transcribe method on an audio file such as audio.mp3, and iterates over the resulting segments to access start time, end time, and text. The weights are stored in FP16 precision, which can be adjusted at load time through the compute_type option in CTranslate2. The conversion from the original OpenAI model was performed using the ct2-transformers-converter command-line tool. It is called automatically by the Mobius Labs fork of faster-whisper. The model carries an MIT license and is published by Dropbox Inc. No pricing information is stated because the model is distributed as an open artifact on the Hugging Face platform.

faster-whisper-tiny-int8 is an open-source, quantized speech-to-text model optimized for fast and efficient transcription. It is suitable for developers who need lightweight and accurate speech recognition in their applications, especially on limited hardware.

This is a fine-tuned version of OpenAI's Whisper Base model by Tarteel AI, specialized for recognizing Arabic Quranic recitation. The model improves transcription accuracy on religious texts, tajweed rules, and classical Arabic pronunciation compared to the base Whisper model.

whisper-tiny.en is a highly optimized English-only version of OpenAI's Whisper model converted by Xenova for ONNX Runtime. It enables fast, local speech recognition in web and Node.js environments without requiring large model downloads.